1

Chikio Hayashi: The Man Who Liked Data
Copyright © 2021 Kumiko Maruyama
The moral right of the author has been asserted.

Published by
Public Interest Incorporated Foundation
Statistical Information Institute for Consulting and Analysis
5F Nogaku-Shorin Bldg., 3-6 Kanda-Jinbocho, Chiyoda-ku,
Tokyo 101-0051 JAPAN
Tel: +81-3-3234-7471 Fax: +81-3-3234-7472
Email: webmaster@sinfonica.or.jp
Book design, editing, and cover artwork by Takako Sinzi.

Dedicated to Haruo Yanai

The front cover image “Chikio Hayashi writes on Data Science”
is reproduced by kind permission ofEmeritus Professor Dr. Yadolah Dodge(University of Neuchâtel, Switzerland).

All rights reserved.
No part of this book may be reproduced
in any form without written permission from the copyright owner.
Printed in Japan by Shigematsu Co., Ltd.

ISBN: 978-4-925079-76-1
Publication date: 28 March 2021

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O reward after a thought
What a long look at the calm of the gods!
--- Paul Valéry --From “ The Graveyard by the Sea”

Ô récompense après une pensée
Qu’un long regard sur le calme des dieux !
---- Paul Valéry --Extrait de “ Le Cimetière marin “

45Contents
Preface

Chapter Four

9Marketing Research and theMultidimensional Analysis Research Group

Chapter One
Boyhood and Young Adulthood

1. Marketing research efforts 68
2. Program rating projections 69
3. The frontline perspective 73
4. The Multidimensional Analysis Research Group15

1. The birth of a young visionary 16
2. Days of immersion in music, sports, and reading 21
3. The statistician Richard von Mises 22
4. First Lieutenant (Aeronautics), Imperial Japanese Army
5. The life-and-death importance of data 29
6. Decision to become a mathematician 29

25

Founding the Behaviormetric Society89

1. Measuring human behavior 90
2. Kinji Mizuno and the foundingof the Behaviormetric Society 92
3. Growth and evolution of the Behaviormetric Society
4. Hopes for behaviormetrics 102

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36
1. The National Literacy Survey
2. The first Japanese National Character Survey

76

Chapter Five

Chapter Two
The Thrill of Sampling Surveys

67

41

97

Chapter Six

Chapter Three

Exploration of Data

Journey to the Formationof Quantification Theory 45
1. Parole research 46
2. Glimmers of quantification theory 48
3. Encounter with Louis Guttman 52
4. Developing the theory 54
5. The computer era 58
6. Raising concerns about statistical mathematics in medicine

6105

1. From the Japanese National Character Survey to
the Cross-national Comparative Attitude Survey
2. A leading French classification expert 112
3. Benzécri’s view of statistics 114
4. Japanese-French exchange in statistics 116
5. The importance of data mining methods 118
62

7

106

Preface
Chapter Seven"The Fire in My Soul
Will Never Be Extinguished..."123

1. Final life work 124
2. Promoting international research exchange
3. Last message 128
4. The idea and future of data science 133
5. We will surely meet again in the Pure Land

Epilogue

139

Acknowledgments
Appendix

145

Chronology

154

References

164

Index

168

About the Author
Notes

143

175

176

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125

135

Chikio Hayashi (1918–2002) lived during an era when mostyoung Japanese men were drafted to fight in wars. When thenation was defeated in World War II, most of the population
had no time to feel lost, but had to earn their living by the sweatof their brow, propelling Japan into becoming an economic
superpower. Today, most have forgotten the travails of this era.
But for many who lived through it, the circumstances of thiscountry called Japan were important, and these people were
proud of the fundamental strength they cultivated in themselvesand how the sum total of the strength they demonstrated saved
the nation from the crisis it faced.
To a greater or lesser extent, postwar researchers shunned
derivative thinking and accepted ways of making things. They
were ashamed to be imitators—always valuing original thinking
and ideas—and whether others understood them or not, they put
their ideas forward boldly, refusing to compromise when facedwith counterarguments, believing in their ideas and defending
them stubbornly to the last. Chikio Hayashi was one of those
who absolutely despised following others in his research.
Sometimes he even refused to read other researchers’ papers.
Once an idea or a concept had occurred to him, he carefully
nurtured it, and until his throat was dry and he could no longer
hold a pencil, he strove to make his ideas known. He authored a9Preface

series of papers and books with a wide range of viewpoints, andhis untiring efforts and abilities demand our admiration.
The more people stepped up to argue against his ideas, themore spirited he became. Chikio Hayashi had many opponents.
He argued passionately with those who challenged him anddevoted all his strength to unearthing the truths hidden in thereams of puzzling data (measurements and observations).
But with a moment’s inattention, the black capes of thedemons (anomalies or unexpected values) hiding in myriads
of data can block the light and make it impossible to see thetruths hidden there. Sometimes one must walk in impenetrable
darkness, feeling one’s way. Those who know this must work
hard every day, eyes peeled, to discern the truths hiding in
the data. This is the attitude and thesis that Hayashi held mostimportant when approaching data.
Chikio Hayashi’s theory of quantification gained widespreadattention thanks to his 1959 paper, “Fundamental Conceptof the Theory of Quantification and Prediction” which for atime was very influential in the field of marketing. Hayashi
developed a series of methods (designated as methods I-IV)
for quantification of qualitative data in multidimensional data
analysis. People studied thoroughly Hayashi’s meticulous social
survey methods and sampling theory. Society subsequently
began moving gradually away from these sorts of laborious
statistical methods toward simple dependence on large
computers, but this meant that one could not battle the demonshiding in the data. This is one of the pitfalls awaiting many

researchers who are involved with data science today. It is
hardly an overstatement to say that Chikio Hayashi’s insistent
warnings pointing this out to researchers working with data
are one of his principal traits. He succeeded in drawing on theessence of data science by immersing himself in it, seeking
the obscured truth and unraveling the complexities of humanbehavior, and never forgetting his own warnings to others.
In today’s data science, with dramatic development ofcomputers and information engineering, statistical mathematicalmethodology and data analysis are considered to be merely
tools. Generally, there is no concern for the processes taking
place in the computer; only the ultimate results are deemed to
be important, and there is a strong tendency to be satisfied withthem. Modern society is awash in data; people have become
entranced and manipulated by it. As time goes on, more andmore individuals aiming to become data scientists may nothave chance to think deeply about what data is, and as society
becomes more data-based, how many will be capable ofunderstanding Chikio Hayashi’s earnest approach to data science,
searching for the underlying meaning in the given data?
In the current era, young statisticians can write best sellers
predicting that society will be ruled by statistics. Indeed, some
may envisage that data obtained from the internet will infringeon individual privacy. These developments suggest that now
more than ever there is a need to engage sincerely with the basic
fundamentals of data science. By doing so, it is certain we will
understand naturally the stance that Chikio Hayashi took with10

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Preface

respect to statistical mathematics.
If false data is propagated freely in a society that depends ondata as a source of information human beings will inevitably
be forced in the direction of their own destruction. Computers
intensify human intellectual curiosity to the highest degree, but
a pitfall awaits: if there are demons in the data, they can topple adata-driven society into the depths. And because this is just suchan era, it is imperative that we understand and engage in the true
statistical analysis that was Hayashi’s goal.
Why study Hayashi’s quantification theory (or simply“quantification”) today? To answer this question, it helps to havean overview of his life and an understanding of his ideas, even
if only in outline. I invite the reader to learn more about the coreideas of data science by walking in the footsteps of statistician
Chikio Hayashi, a towering intellect who covered a vast rangeof research subjects and laid the foundations of modern data
science, worked out its fundamental concepts, ventured out togather data from the front lines, and followed where it led him.

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1213
1415

Chapter One

In June 1918, amid the balmy breezes of a fresh summer
morning, a son was born to the Hayashi family in Komagome,
Hongo-ku, Tokyo City (today’s Komagome, Bunkyo City,
Tokyo Metropolis). He was preceded by brothers and a sister,
but there was a gap in their ages, and the little boy was raised
as though he were an only child. His name was Chikio,
nicknamed Chiki-chan, and everyone around him treated him
with affection. In his early years, he wanted for nothing. Whenhe had completed the local kindergarten and elementary school,
he entered Tokyo Kaisei Middle School in Nishi-Nippori. The
school was known for developing outstanding boys instilledwith a spirit of austerity and fortitude. At the same time, it had amarkedly free atmosphere. Young Chikio had a passionate loveof books, and he spent many hours immersed in reading.
Hayashi vividly depicts his feelings from this time in “The
Pleasures of Reading,” an essay he wrote for the school bulletin
when he was a third-year student. In it, he repeatedly makes
the point that just as one cannot go without food, reading is apleasure that as a person, he could not forego. Compared withessays written by his classmates, Hayashi’s essay is extremely
down-to-earth, using a plain style that expresses his thoughts atthe time in a forthright manner.
We can discern the early signs of his lifelong devotion toreading. As he grew up, books were a form of sustenance justas important to Hayashi as breakfast, lunch, and dinner—firstof all as a way of gaining knowledge, and then for the spiritualpleasure they afforded. Every time he added yet another bookto the pile of those he had read, he writes that he was somehow
filled with the pleasure of great accomplishment, as though
he had become a great scholar who had made an importantdiscovery. During his years in middle school, his room was
submerged in mountains of books, and the joy he felt when
he took a new one in hand was a precious moment he would
not exchange for anything. Among the authors who left theirimpressions on him, the most important were Paul Valéry, theFrench philosopher, intellectual, and poet, and Torahiko Terada,
the essayist and professor of physics at the Tokyo Imperial
University. Valéry in particular became a lodestar in Hayashi’slife.
It must be said that for young Chikio, as a third-year middle
school student, to sympathize with and become an ardentadmirer of Valéry’s recondite world view is highly unusual. But
when he encountered the thinking of this esoteric individual,
when he touched the depths of the poet’s soul and his innermost
thoughts, Hayashi, struck with awe, was seized with an impulse
to run in circles, crying out as if in an intoxication resembling
madness. What he learned from Valéry was not simply to
express one’s sensibilities in poetry, but to cast his gaze into thesky toward impossibly distant aspirations. Valéry had gained areputation as a poet when at 20, he felt revulsion toward literatureand poetry, abruptly transforming himself into a philosopher andcritic. Just as Valéry created the fictional character of Monsieur

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17

1. The birth of a young visionaryChapter One
 

   

18

Teste, the embodiment of intellect, the lesson that Chikio likely
learned from Valéry was the courage to rebel against beautifully
tended mathematical theorems as well as poetry and literature,and the need to maintain the penetrating gaze that seeks ultimate
truth.
Furthermore, as the young man was drawn to physicist
Torahiko Terada’s sensibility and sharp eye for observation, hereached the age where he was capable of understanding what he
was destined to do. At some point his ears detected the machinelike rhythms hidden in abstract algebra. Might there not bea means of mastering these rhythms? The emotions roilinginside him would drive his subsequent motivation to study
mathematics in university. The sensibilities of Valéry and Terada
are strongly reflected in the mentality that led him, while feelingthe pull of literature, to move as though toward its opposite pole,mathematics. The philosophy of ideas in the natural sciences
had been accumulated by traversing a long road, examining
reality consistently and unwaveringly. The lessons he absorbed
from Valéry and Torahiko Terada penetrated his heart deeply,
remaining there without his conscious awareness, exerting astrong influence on the spirit of the man he would become. At
age 60, Hayashi recalled his youth this way.
“There are many different ways to arrange one’s books onshelves. I keep all my especially precious books in one favorite
place. If I was asked to name my favorites among them, I would
choose two that suit my temperament perfectly, and reinforced
and developed it. They are not properly classic works, but so-

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Chapter One

called miscellanea: Torahiko Terada’s collected essays fromhis early and middle periods, and Paul Valéry’s anthology,
Variété. Torahiko Terada’s essays consider how the phenomenaof ‘things’ should be understood, and I was very impressed byhis original analytic approach. That was as far as it went at thetime, but as I began to do my own research, before I knew it
I found that his approach was greatly influencing me. When I
read research papers, I immediately wonder what things mean,or whether the findings are genuine, before I decide whether or
not the paper interests me; I always proceed without artifice,trying to keep before me what is most important. Variété greatly
influenced my point of view regarding phenomena of the spirit,
my attitude toward civilization, and my views concerning
human action. From ‘La Crise de l'Esprit’ and ‘Note etdigression’ to ‘Introduction à la méthode de Léonard de Vinci’,
I found the power of these essays breathtaking, and myself
in complete agreement with them (I refer to the Hakusuisha
edition, translated by Kenzo Nakajima and Masaaki Sato). Later
Valéry compilations are neatly categorized into literary research,
philosophical research, quasi-political essays, poetics, esthetics
and so on—superficial and not at all compelling. No matter how
impressively presented, the finished product is already useless;what is required is the courage and readiness to rebel and startanew. I realized that intellectual and physical reluctance andindolence are the greatest enemies. Don’t follow a path laidby others, but blaze your trail yourself. This will strengthen
you and build your body, reinforcing your autonomy. This is

the only approach to living in a satisfying way. This is how theargument goes, and it is the impact that Variété had on me.” (TheNihon Keizai Shimbun, April 16, 1978, evening edition.)20

21

2. Days of immersion in music, sports, and reading
In addition to reading, music—particularly playing theflute—was another boyhood hobby. Not that Hayashi had
someone to teach him; he had been entranced when he heard
his older brother playing it. He was completely self-taught.
He enjoyed playing French impressionist composer Debussy’s“Prélude à l'aprés-midi d'un faune,” which was inspired by
one of the poems of Mallarmé. To the end of his life, Hayashi
was a lover of classical music, and was particularly devoted
to Debussy. The latter’s opera, “Pelléas et Mélisande,” was afavorite.
But young Chikio was no introvert, amusing himself withbooks and music only. He was also active in sports at Tokyo
Kaisei Middle School, including track and field, and later
swimming, rugby, and sumo. He helmed sailboats to experience
the vastness of the sea, and later took part in the National Sports
Festival sailing competition. This honed his reflexes and gave
him a flexible body. As a youth, he was proficient at all outdoor
sports and loved physical activity. As an adult, he climbed
mountains and skied, and even in old age he enjoyed scuba
diving. His spirit of adventure never waned.
Later he would participate in a survey to count the number ofChapter One

wild hares living deep in snow-clad mountains, but this was no
exuberant winter ski trip. It was the sort of mountain skiing thattoday would be called cross-country skiing—not the enjoymentof gliding along, but mountain climbing on skis. It was
extremely physically demanding, but he aimed for the summits
without hesitation and climbed with ease. On his return, he
sped down the slopes defiantly, shouting for joy. On a visit to
Switzerland, Hayashi often skied in the famous Matterhorn andEiger areas, logging several kilometers of high-speed skiing
every day; and he was past 70 when he enjoyed sailplaning in
Hawaii.
Another episode dates to his participation in a 1970 NHK
public opinion survey, which required him to travel frequently
to Mount Atago. There he discovered the Atago Concerts,
performances by amateur musicians that were held twice
yearly. He greatly approved of these concerts, and participatedfor 24 years without missing a single one. At 66, to stretch his
capabilities, he switched from flute, which he had played since
youth, to the horn, and was so dedicated to practice that hemade rapid progress. Hayashi approached this new instrumentwith the same passion that marked everything he did, and thesame commitment to enjoyment.
3. The statistician Richard von Mises
Hayashi graduated from Tokyo Kaisei Middle School in
1936, and enrolled in Seijo High School’s Department of

22

Science under the prewar educational system. The clouds of
war were gathering over Japan, and in 1939, when young men
increasingly took pride in joining the army or navy, he entered
the Department of Mathematics at Tokyo Imperial University’s
Faculty of Science. As his major, he chose probability theory—a field widely disliked by others because it was not puremathematics. His academic advisor, Soichi Kakeya, was an
authority in the field of analytics, but he allowed Hayashi topursue probability theory. That a leading light in analytics
allowed him to do so had a decisive influence on the rest of
Hayashi’s life. Kakeya was no imitator, and was not easily
influenced by things that interested others; his fundamental
stance was to value originality. He was a deep thinker who
did not rely on others' academic papers, famous for his superb
conceptions, and known for the fact that if he reached the sameconclusion as a foreign academic, it would be through original
thinking and not due to having read foreign publications.
Consequently, Kakeya practically forbade his students fromreading others’ academic papers. He told Hayashi to value his
own thinking and write papers that took a different stance fromthe rest. But it was around this time that Hayashi encountered
Probability, Statistics and Truth, written in 1939 by the highly
original mathematician Richard von Mises. Hayashi later
recalled “almost weeping with excitement” as he read this work,
and without telling Kakeya, he began to seek out other works by
von Mises.

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Chapter One

4. First Lieutenant (Aeronautics), Imperial Japanese Army   

24

In October 1942, Hayashi graduated ahead of schedulefrom Tokyo Imperial University. In order to join the military,
he became a technical cadet at the Imperial Japanese Army’s
Mito Flying School. Upon graduation, he would become afirst lieutenant (aeronautics), and be assigned to the army’s
air service headquarters. But rather than serve as a technician,he was intent on fighting on the front line as a private secondclass. Recalling his fervor, he wrote, “I was interested in testing
myself, and in strengthening myself. At the root of my attitude
was Paul Valéry’s idea, which was said to be intellectualism,of ordeal and autoplasty. As a private second class, I wanted
to test myself by fighting equipped with nothing but myself,with all my heart” (The complete works of Chikio Hayashi,
Bensei 2004, Vol. 10, page 245). This romantic view earned
Hayashi a rebuke from military veterans. “Use your knowledge
to serve the nation,” he was told curtly, and he devoted himself
obediently to serving the nation as a mathematician.
During his time at the flying school, Hayashi was orderedby an army surgeon to calculate the appearance of air-to-air
ballistic trajectories. Exempt from military drills and training,
Hayashi was to produce detailed calculations of how accuracy
rates could be enhanced when firing from an aircraft at anotheraircraft. Each day, Hayashi would visit the surgeon’s room,
listen to what he had to say, and spend the rest of the day
calculating accuracy rates. When Hayashi was finished, the25Chapter One

surgeon, who was a lieutenant commander, took them to a joint
Army-Navy aviation research group at the Naval Air Facility in
Atsugi, introducing them as “Hayashi’s theory.”
Hayashi graduated from flying school in February 1943.
Though he repeated his request to be sent to the front line, he was
assigned to the Research Section of the Imperial Army Air Service
Headquarters, General Affairs and Administrative Department.
There he encountered the Remington Rand punch card systemused for data tabulation. At the time, Hayashi knew only abstract
mathematics and probability theory; he had no knowledge ofdata. This is where his trials began. It was his first step toward
recognition as Chikio Hayashi, standard bearer for data science.
The Research Section followed a German model. A Japanesemilitary officer returning from Germany had recommended thatJapan establish a similar organization, and the Research Section
was assembled with aviation staff officers and fixed-term
technical commissioned officers with backgrounds in scienceand engineering. Hayashi was ordered to gather and organize
information relating to the international situation as well as
war tactics and strategy, but he was almost completely unable
to understand his assignment, and was at a loss. He began by
categorizing the flood of information and telegrams coming into
the department by type. Once this task was complete, he would
have to write a report. A colleague who later became a sciencejournalist taught him how to express his thoughts in writing,concisely and quickly. He created a “Weekly Report” pamphlet
with interesting articles about the international situation forcirculation to the entire Army Air Service. This experiencetaught him the art of organizing information, how to identify
key points, and how to build a story around them.
His next challenge was forecasting. His first problem was toforecast Boeing B-29 production and the number of bombers
likely to be used in attacks. He projected production rates by
calculations based on large volumes of production technologyrelated information from aviation data and information in
dispatches, and curve fitting using historical data. This forecast
was later incorporated into plans for defending the Japanesemain islands from air attacks. Hayashi had earned a place in thehistory of the war.
This experience taught Hayashi that data gathered correctly
and processed carefully could be of great help in clarifying
contentious issues. The experience was extremely eye-opening.
Practicing techniques to identify the truth hidden in data could
even defend the nation in a crisis. While such a realization
seems obvious today, Japanese army officers of the time did
not necessarily receive intellectual training; they depended on
intuition acquired through military experience to guide theirconduct. Hayashi’s emphasis on the importance of data dates
to this time, and was born from the huge volume of graphic
situation reports sent from the front lines of the war. Moreover,
he recorded the opinions he derived from the data relating toJapan’s air defense strategy and submitted them to the General
Staff Office. For these he received much praise, but due toopposition from senior staff officers, his recommendations were26

27

Chapter One

not put in print; the officers did not like the idea of conferring
such an honor upon the recommendations of an amateur inmilitary operations. Afterward, Hayashi even thought that
if his recommendations had been considered and put into
practice, Japan would not have suffered such a terrible defeat.
Perhaps it might even have been victorious; his confidence inthe correctness of his opinions was that strong. That is how
forcefully the raw reality of the data impacted his mind, and this
impact remained with him for a long time.
The suicide attacks by Special Attack Unit aviators, whichbegan in late 1944, were so shocking to the Allied forces thatthey naturally concluded that these aircraft were not piloted
by men of flesh and blood, but by robots. For a human being
to pilot a fighter plane without the intention of returning alive
was suicide. This characteristically Japanese approach to war
fighting became known outside Japan as “kamikaze attacks,”
and it caused much apprehension among Japan’s foes.
Hayashi was ordered to carry out more calculations, thistime regarding the success rate of the kamikaze strategy. As the
young cadet developed the calculation techniques, data analysis,
and methods to enhance the accuracy of the attacks, he musthave felt a sense of sublime heroism.
And so it was that as a young man, Hayashi grappled withlarge data sets at the height of war, later creating his uniquequantification theory. But without considerable spiritualstrength, it would hardly have been surprising if Hayashi had
fallen into war neurosis as a consequence of the way the war

consumed his youth, buried him in mountains of data, andforced him to take responsibility for life or death decisions.
Instead, Hayashi used his experience as a springboard to honean almost obdurate spiritual strength.

28

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5. The life-and-death importance of data
People today would be very unlikely to suspect that thefoundations of data science were formulated at the height ofwartime. They would surely also be shocked to hear, as Hayashi
was wont to say, that “from data comes gushers of blood.” They
might regard this as merely a figure of speech. But each piece ofdata he was handling was stained deeply with fresh blood andsweat. His paper commemorating his receiving the Medal of
Honor with Purple Ribbon in 1981 was titled “Blood is Spillingfrom Data.” The highly sensitive youth who immersed himself
in music and reading and excelled at outdoor sports, especiallysumo, surely never dreamed that he would soon find himself
grappling with data from the front lines of the war, laboringnight and day to uncover the truths held within.
6. Decision to become a mathematician
In 1945, with the end of the war, Hayashi returned to the
Mathematics Department at the University of Tokyo. Onceagain, under Kakeya’s tutelage, he embarked on probability
theory research. Though Kakeya discouraged him from readingChapter One

the works of others, Hayashi immersed himself discretely in vonMises’s probability theory and von Neumann’s game theory,
and was excited to see how von Mises’s theory related to gametheory and was utilized in economics. In the wake of Japan’s
defeat, the books Hayashi wanted to read had mostly been lost.
This meant a series of frustrations for someone as naturally fondof books as he was, and when new books arrived, he hurried
to the bookstore as fast as he could. He read all the magazinesand books in the Mathematics Department, calculating that if
he read 30 pages a day, he could read a thousand pages in a
year; in fact, he outpaced his original plan, reading everything
in the department in two years. Conditions in Japan were very
unsettled at the time, and there were few books he wanted toread in the university’s half-destroyed library. Again and again,
he read the works of von Mises and von Neumann that had
impressed him so much.
At the time of Japan’s defeat, the Mathematics Department
was holding colloquiums attended by a diverse groupof professors who spoke on the latest developments inmathematics. At one of these gatherings, Kunihiko Kodaira gave
a talk on John von Neumann and Andrei Kolmogorov’s gametheory. When Hayashi asked where Kodaira had found such an
endlessly fascinating mathematical theory, he was referred to
the American Cultural Center near Hibiya Park. Hayashi visitedthe center immediately, and eagerly read the books he foundthere in abundance. These books in the center were apparently
intended for study by Japanese readers to promote America’s

30
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Chapter One

democratization of Japan. Whatever the intention might be,
Hayashi just devoured the latest mathematical theories available
with bloodshot eyes. Today, with bookshops overflowing
with books, Hayashi’s situation is difficult for us to imagine.
More than anything else, he craved knowledge. But Kakeya
reproached him for that kind of moratorium, telling him sternly
to go back to working on what was important.
The Mathematics Department colloquia, with numerous
professors introducing a succession of new mathematicalmethodologies, gave Hayashi intellectual satisfaction unlike any
he had experienced before. He found himself elevated above therealities of everyday life—a society in chaos from the war, withpeople desperately searching for food to purchase—to a “higher
spiritual realm.” He believed that his mission was to dare to give
himself over to a non-ordinary world; this was how he could
best contribute to helping a defeated Japan make a comeback.
When Shizuo Kakutani, who had studied at Yale University,
began to speak on topological analysis and demonstrated
his grasp of the sense and essence of mathematical progress,
Hayashi felt as though the scales had dropped from his eyes,
and he felt the strength welling up from within to create somemathematical theory of his own. It was then that he decided tomake mathematics his profession.
In October 1946, Hayashi joined the staff of the Institute of
Statistical Mathematics (ISM) at the recommendation of Soichi
Kakeya, who was then serving as the institute’s director-general.

Hayashi would spend more than half his life at ISM. Even after
he took over the helm there, he maintained a personal researchroom where he spent his days surrounded by data. He only used
the tidy director-general’s office to receive visitors, and to talkto research staff who visited him burdened with various humanrelations problems.
And so it was that through new data-related theories andtechniques, Hayashi set forth on the tossing waves of a postwar
society in a state of flux. He would move forward vigorously,
fighting new battles for truth day by day while experiencing a
number of important human encounters.

32

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

34

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Chapter Two

In 1948, in line with the US occupation policy, the order
was given to conduct a nationwide survey of literacy in Japan.
The American military authorities had a one-sided convictionthat the Japanese language was difficult, in particular that theJapanese writing system, including Chinese characters, wasa peculiar system that could only be understood by a limited
segment of the population. The Americans estimated that onlyaround half of the population was capable of writing and reading
Chinese characters. Kikuo Nomoto of the National Institute forJapanese Language and Linguistics (NINJAL), later director ofthat institute, had just recently graduated from the department ofLinguistics, Faculty of Literature of Tokyo Imperial University.
According to Nomoto, the Americans believed that Japan’s
use of Chinese characters promoted cultural inequality, which
in turn was a factor in the formation of a totalitarian state, andwas considering the promotion of a more accessible writing
system based on romanization. To test the fixed idea that script
reform based on romanization would facilitate democratization,a national survey of literacy would be carried out, with theeventual intention of converting the writing system to Latin
letters. Risaku Mutai (Tokyo University of Education) waschosen to head the survey committee, with Osamu Ishiguro(National Institute for Educational Research, NIER), Takeshi
Shibata (NINJAL), Kazuo Shimazu (NIER), Kikuo Nomoto(NINJAL), and Chikio Hayashi (ISM) as members.

Around this time, the occupation’s Civil Information and
Educational Section (CIE) was based in the NHK Building in
Uchisaiwaicho, Tokyo. American scholars in sociology andanthropology, schooled in American methods, directed theimplementation of public opinion surveys. Everything was
carried out according to American methodology. Those involvedin public opinion surveys, including ISM researchers, visited
CIE daily to study the fundamentals of social surveys.
CIE director John C. Pelzel was charged with overseeing
the National Literacy Survey field work. The guidelines heformulated were known as “Pelzel’s Constitution” and weretreated as scripture. The survey was conducted in accordancewith the guidelines, which reflected orthodox social survey
methods. Hayashi was responsible for national sampling. Withreference to ideas from the field of geography, Hayashi usedstratified sampling with three strata. This survey, carried out in
1948, was the first postwar nationwide survey. The American
researchers were impressed by the capability and dedicationof the Japanese survey team members, which exceeded their
expectations to a surprising degree. Many of the best of Japan’s
younger generation had perished in the war, making the
responsibilities of those who survived that much heavier. The
Japanese survey members worked fervently and gave their all,carrying out their tasks for the sake of the country.
The survey results were eye-opening. Illiteracy among the
population was a very low 1.6%. This meant that the Japanese
were far more proficient at reading and writing than the

36

37

1. The National Literacy Survey

Chapter Two

Americans believed and as a result, the push for romanization
was abandoned. The survey can truly be said to have been thesalvation of the nation, averting as it did the destruction of theJapanese language. Hayashi, who had never been involvedin such a survey, was bewitched by the allure of sampling.
The sampling and social survey methods he used would bea precursor to those employed in public opinion and social
surveys in Japan, and would become a model in the field. The
National Literacy Survey remains a standard for social surveys
today. Hayashi gained confidence in handling surveys; he wasentranced by the allure of the data gathered, and by the way thatsurveys could extract meaning from raw data. Freed from themoratoria that were a feature of his student days, he had finallyfound a “man’s life work” he could devote himself to.
It was at this time that Hayashi began to entertain doubts
regarding Gaussian distribution (normal distribution) and the
Neyman-Pearson statistical testing theory. The Neyman-Pearson
standpoint was predicated on the Gaussian distribution, taking it
as a basis for judgment. Where data fails to constitute a Gaussian
distribution, large amounts of data are collected to moreclosely approach such a distribution. Hayashi considered this torepresent a reversion to the ideas of A. Quetelet’s era, because
the more data he collected, the more he saw that the Gaussian
distribution only applied to a limited portion of the data. Since
most testing used the central limit theorem, a representative
theorem of the Gaussian distribution, as a foundation, statistical
hypothesis testing meant applying the ideas of the Gaussian

distribution. Though a wide variety of distributions had since
been discovered, the weakness of statistics in that period was
the way it truly harked back to a time when Quetelet’s statistics
was regarded as almighty.
After the National Literacy Survey, Hayashi next worked
on a linguistic survey of Hachijo Island. This was a dialectsurvey, but what made it different from surveys until then was
the way it considered the relationships between dialect, social
background, and social circumstances. Though it reached the
expected conclusions, the survey resulted in the developmentof invaluable methodologies that would help establish thefoundations of modern social survey methodologies, including
sampling, sampling survey strategies, and error evaluation.
From around this time, a large number of documents frominternational sources were becoming available in Japan. Amongthese was Methods of Operations Research (Morse & Kimball,1946), a book that astonished Hayashi. The approaches it
described closely resembled those Hayashi had employedduring wartime; in particular, its analysis of data relating tosuicide planes was virtually identical. Hayashi was becoming
increasingly aware of the importance of data and data analysis.
He sought to propagate widely the concepts of statisticalmathematics, as opposed to mathematical statistics, and withhis senior colleague Hiroshi Mizuno, he raised the banner ofdata centrism, a form of statistical mathematical supremacy.
He criticized researchers who persisted in using mathematicalstatistics, and quarreled with them so heatedly that it seemed38

39

Chapter Two

as if they might come to blows. Statistical mathematics ormathematical statistics—it was one or the other, and Hayashi
and his opponents were so determined to defend their ideas thatthey allowed no room whatsoever for compromise. In the wakeof defeat, Japan would soon be fired with passion, its people
working like bees in a feverish effort to rebuild the nation, andHayashi was feverish as well, burning with ardour for what hetermed “survey science.” Together with Mizuno, he laid out aprocess for design, collection, organization and analysis of data
and results, beginning with sample surveys and progressing
to statistical mathematics, quantification, multidimensional
data analysis, and finally revealing the hidden structure andunderstanding actual phenomena (natural, human, and social).
Today, the general term for these techniques is known as data
science. This is how Chikio Hayashi became a data science
pioneer.
In Hayashi’s view, there was nothing without data. His
approach was to devise what was required for data acquisition
design and analysis in accordance with concrete considerations:seek to discern the underlying philosophy and devise methods
based on that philosophy. Rather than carry out so-called
hypothesis testing, he was committed to seeking out the truthin the data and verifying it through investigative data analysis
with a quality of hypothesis discovery. He adhered increasingly
to a methodology that discovered hypotheses from dataand explained phenomena inductively, rather than using theconventional scientific methodology of deducing hypotheses.

40

2. The first Japanese National Character Survey
Hayashi became known as a “conference crasher.” Because
he was extremely interested in the work of young researchers,
he was usually to be found in the front row of any conference
held by an association of which he was a member, arms folded
and listening with great attention to the research presentations.
And thanks to his fervent criticism of conventional statistics,
he was shunned by almost everyone. It is a fact that Hayashi’s
tough academic criticism made him adversaries. “That Chikio”
was regarded as a troublesome fellow, always finding fault, but
he was convinced that stating his beliefs in no uncertain terms
to a broad audience was the best way to advance the cause ofscience.
Hayashi opposed the idea that if obvious, common-sense
conclusions were derived from data, that in itself indicatedthat the survey was carried out correctly. He was hostile to theAmerican adherence to operationalism and behaviorism, inwhich hypotheses were formulated and then tested. His belief
that hypotheses should be structured based on survey results
never wavered, and he directly attacked the supremacy ofstatistical hypothesis testing. It was an era when public interestin surveys was increasing; the notion was pervasive that if
sampling surveys could be carried out correctly and without
error, everything relating to society could be grasped, andsupport for the idea that natural science was supreme gradually
weakened. The 1950s can almost be said to have been an era

41

Chapter Two

when social surveys ruled supreme, and much social survey
technology was developed, but Hayashi was a step ahead,promoting survey science and opening the way for a new
development that might be called survey philosophy.
In 1953, Hayashi decided that a large-scale survey of theJapanese national character was needed. Conflicting theories
concerning Japanese national and cultural identity before,
during, and after the war, loss of identity in the wake of defeat,a view of the Japanese as villains—these subjects were in playamong Japan’s intellectuals, and Hayashi sought to shed lighton them. He recalled the “Imperial Rescript on Promotion of
National Spirit,” promulgated in 1923 at a time of widespreadnational demoralization, and he called his proposal “A Survey
on National Spiritual Trends.” Once the survey’s budget wasapproved, he changed its name to “Japanese National Character
Survey” to avoid misunderstanding. Since then, this survey has
been conducted every five years, developing into a time-series
survey and a major ISM project. Hayashi planned the sampling
survey, devised the system for carrying it out, evaluated nonsampling errors, worked on probability methods for thequestionnaire approach, and carried out a large-scale randomsampling pretest. The form he established for the questions, thelists, and other elements of the survey are in use today.
This survey was a comprehensive exercise in sample survey
and social research methods, and along with the National
Literacy Survey, became a reference for analyzing the attitudesof the Japanese people. It made it possible for the first time toachieve a good understanding of who the Japanese people were.
Hayashi eventually used these survey methods to carry out
detailed research into international attitude comparisons.

42

43
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 

44

45

Chapter Three

We have seen how wartime ballistics (research to enhance thepredicted accuracy of reaching a target) as well as postwar social
survey methodology and analytics underlay most of Chikio
Hayashi's work. Another episode of importance began in 1947,
when Katsuhiko Nishimura, a Ministry of Justice technicalofficer at the Training Institute for Correctional Personnel (latera professor at Aoyama Gakuin University), visited Hayashi torequest that he analyze numerous factors relating to criminal
behavior using statistical methodology.
At the time, criminal jurisprudence was divided into two
factions regarding punishment methods: disciplinarians
and educators. The former advocated reform through strict
punishment, while the latter argued for an educational
approach to behavior modification. Nishimura was a memberof the educator faction. He had reviewed the data used by thedisciplinarian faction and found it incomplete. Their analysis
used simple calculations only, and was superficial and lacked
analytical depth; the approach was rather unfair, even for
criminals. It resembled the kind of coercive approach used
to educate children in the dark ages, and was education inname only. For the disciplinarians, the crime determined thepunishment; individual differences between offenders wereirrelevant.
To reform the criminal, the education faction advocatedgranting parole once the offender had served a third of his

sentence. If he did not re-offend, he would remain free; if here-offended, he would have to serve out the remainder of his
sentence, with time added for the new offense. However, toprotect society from harm, it would be necessary to forecast thelikelihood of that criminals would re-offend after being paroled.
This research would entail heavy responsibility and have to beapproached very carefully. Hayashi’s role would be to quantify,
as much as possible, offenders’ individual traits (including
developmental and residential histories, family environment,social and psychological attitudes and values, and living, social,
and work environments), attitudes toward the crime and thejustice system, attitudes and behavior during incarceration, andso on. Based on the results, he would attempt to forecast theprobability of a repeat offense as accurately as possible.
Yokohama Prison was chosen as the survey site, andHayashi’s days of commuting to the prison began. As he began
to examine the inmates closely, he took notice of a man who
was submissive and cheerful. Hayashi was puzzled; why wasa person like this in prison, he asked. He discovered that theman was an inveterate repeat offender—dubbed “Prison Joe.”
In effect, the man had become so accustomed to prison life thatas soon as he was released, he would commit another crime.
Hayashi thus realized that there were many types of prisoners,
and a single standard of measurement would not be applicable toall of them; the problem could therefore not be dealt with in theusual way. In a similar survey carried out in the United States,
researchers actually lived in the prison to immerse themselves in

46

47

1. Parole researchChapter Threethe specific environment, and Hayashi felt he would need to do
the same thing—live in the prison to grasp the actual conditions
from the same standpoint as the prisoners. But when he madethis request to the head of ISM, he was told curtly that the idea
was absurd. Hayashi was sure that if their positions had been
reversed, he would have granted his enthusiastic researcher’srequest. Reluctantly, he continued his visits to the prison with
Nishimura, and by conducting survey interviews from numerous
angles, creating survey forms, and carrying out pretests, hemanaged to make progress, working diligently to provide usefulforecasts of post-parole behavior.

Forecasts notwithstanding, there were only two possible
outcomes: either the parolee would re-offend, or he would
not. Using qualitative data—those who re-offend havethese characteristics, while those who do not have thosecharacteristics—Hayashi could calculate forecasting
accuracy rates, but to do so, he would need to apply a scale
to his qualitative data. The Likert scale is used in the field of
psychology. In a simplified approach, reaction categories are
scaled from –3 to +3, with 0 as the middle value; prisoners
falling on the right (positive) side of the scale were categorized
as likely to re-offend, while those on the left (negative) sideof the scale were unlikely to do so. The result was a Gaussian
distribution centered nearly at 0. Individual categories weredivided by frequency of occurrence, and the average value wasused to judge the likelihood of re-offending. The approach was
simple, with equal spacing between scale units.
But for Hayashi, who was allergic to Gaussian distributions,this sort of scaling was completely unacceptable. He was
fed up with the way that the ghost of Quetelet had possessed
statistical analysis in psychology. Such scales were useless; they
made it impossible to measure deep truths. What was needed
was to return to square one and consider the survey objective
seriously, especially given the importance of accurate re-offense
forecasting. The importance of raising forecasting accuracy inthis case was no less than during wartime, when Hayashi had
strived to increase the number of enemy aircraft shot down byeven one. From that standpoint, becoming discouraged was not
an option, and Hayashi rose to the occasion. He would assign
numerical values to his qualitative data so that he could makestatistical decisions by adding these values.
First, to carry out an analysis, he had to quantify his
qualitative data. He employed the expression δi(j, k); when
individual i reacted to category k in survey item j, the expression
was assigned a value of 1. If there was no reaction, it was
assigned a value of 0. For item category (content) k, he assigned
sum Xjk (k=1,…,Kj; j=1,…,R). This is ɑi (i=1, …N). Using thisprocedure, for each of the groups with and without post-parole
re-offense, it should be possible to calculate a distributionfor ɑi. If ɑi is expressible as a one-dimensional quantity, one
can assume that post-parole behavior can be explained one-48

49

2. Glimmers of quantification theory

Chapter Three

dimensionally. If the number of items R is large, it approaches
a Gaussian distribution from the standpoint of the central
limit theorem. Hayashi assumed that if the two distributions
were separate, the forecasts would likely be correct, so
he defined a single segmentation point and proposed that
forecast accuracy P be maximized if those with an ɑi value
above the segmentation point were predicted to not re-offend,
while those with a value below the segmentation point werepredicted to re-offend. Thus, P becomes a metric (yardstick)for measuring forecasting success.
Around this time, John von Neumann’s minimax decision
theorem was published. Hayashi was surprised to see how
closely this rule fit his own thinking; excited, he became
confident about his own approach. Von Neumann’s minimax
theorem has been incorporated into game theory, and waslater developed by Abraham Wald into a statistical decision
theory.
Hayashi was now free of the Likert scale approach, andhe pushed forward with his survey. But even when he hadgathered his data and was ready to begin his calculations, he
did not have the benefit of modern computers. There were
only desktop hand-operated calculators, and calculating a 9x 9 inverse matrix took two weeks to complete. Yet, though
the process was time-consuming without the benefit ofcomputers, which return answers instantly, manual calculation
enabled Hayashi to understand the procedure in great detail,and strengthened his numerical calculation abilities. Looking

back, some would later say that ISM’s capacity for numericalcalculation may have been the best in the world. Working
through each step personally gives rise to many ideas.
Hayashi’s concept of maximizing the square of the correlation
ratio η, a foundation of quantification theory, was the resultof a repeated, meticulous trial-and-error process of the samekind.
Once the work was complete, Hayashi saw many resultsthat would never have been obtained with the Likert scale.
He found that prisoners with both exemplary and very
unsatisfactory behavior during incarceration could have highre-offense rates, while those who fell between these twogroups had low re-offense rates. Those with whose responses
to arrest and trial were neither exemplary nor negative andwithout special characteristics were the least likely to reoffend. These results had not been verified by previous
mathematical calculations. Expected forecast accuracy P
increased to 0.95. When Hayashi announced these results at a
meeting of psychologists, one of the audience was so moved
that he approached Hayashi and shook his hand. This was Juji
Misumi, who would later become a noted authority in groupdynamics. This young man, who would be one of the earlysupporters of quantification theory, was then an assistant at
Kyushu University.

50

51

Chapter Three

3. Encounter with Louis Guttman
Around the time Hayashi had completed his parole research—
research relating to quantification theory—scale theory was
becoming popular, and after telling Hajime Ikeuchi, who would
later become director of the Institute of Journalism at the
University of Tokyo, about his parole research, Hayashi and
Ikeuchi became close friends. Ikeuchi recommended the newly
proposed Guttman scale as being superior to the Likert scale.
The Guttman scale was the work of the mathematician
and social scientist Louis Guttman. It is a measuring scale
based on one-dimensionality and the fact that the items in thequestionnaire are hierarchically related. If a positive answer by
a respondent to a measurement attribute with high difficultymeans positive responses by the respondent to all simpler
attributes, one can create a response diagram called a scalogram,which can be used to perform scaling and derive coefficients ofreproducibility.
From Ikeuchi, Hayashi also learned about Guttman’s methodfor quantifying paired comparisons. Guttman developed thistechnique during research aimed at enhancing the satisfaction ofAmerican military personnel with the process of demobilization.
Total years of service, years of overseas service, number offrontline postings, family status, number of children, andnumerous other attributes were compared in pairs. Based on the
results, the requirements for accelerated demobilization wereidentified. The method of assigning values to each attribute52   53   Chapter Three

category was aimed at maximizing the distinction between
soldiers to be demobilized rapidly and those to be demobilized
slowly. The study’s computational approach resembled
Hayashi’s quantification theory and the maximization of thecorrelation ratio in his parole research.
Hayashi sensed that Guttman was no ordinary person, andwas a kindred spirit, regardless of the name attached to theapproach, whether Guttman scale or facet theory. The name ofquantification theory itself was inspired by Guttman, with input
from Hayashi’s mentors Mizuno and Ikeuchi. This was the birthof quantification theory.

Hayashi had led the rebellion against mathematical statistics,
and with Mizuno, he had refined the philosophy of statisticalmathematics. Now it was time to go on the offensive. This wasa conviction he had held since joining ISM, and with help fromhis powerful ally Mizuno, he proselytized for quantification
theory and charted a proper course for data science. The successof quantification theory gave him confidence, and Mizuno’s
strong personality spurred him to action.
As Hayashi’s confidence increased due to such factors
as sampling theory, quantification theory that was optimalfor qualitative analysis, and the theories’ high rates of event
forecasting accuracy, he maintained that the centerpiece ofstatistical mathematics was the analysis of human behaviors,

taking a position that compared to hard mathematical methods,
soft methods were more suited to and useful in the analysis ofphenomena. His discussions with Ikeuchi as well as Mizuno
were bearing fruit.
Hayashi’s fundamental notion was that quantity was notinherent to things themselves; instead, it was a tool given to
us to do science. In this, his philosophical ideas resembled
those of David Hume, the British philosopher who perfected
Empiricism; and when Hajime Ikeuchi pointed this out to him,
Hayashi read Hume’s A Treatise of Human Nature and foundhimself so much in sympathy with it that henceforth he kept thebook close at hand for frequent reference. Equipped with thisfundamental concept, the way forward, which until now had
been obscured, became plain as day.
Hayashi’s philosophical essence was British-style statisticalmathematics with Empiricism as its core, and it traced
its lineage in the history of statistics less to the German
Staatenkunde than to the British Political Arithmetic school.
Like the tabula rasa of the British philosopher John Locke, thefather of Empiricism, our minds are blank slates, devoid of
innate ideas; the source of our ideas is purely experience. We
can only perceive things and process that experience. Hayashi’s
thinking was similar to this philosophical concept. In his case,experience meant data, and social phenomena and the humanmind could be understood from data.
Hayashi soon became involved in the field of psychology. In
1955, the Research Society for Quantification in Psychology

54

55

4. Developing the theory

Chapter Three

was launched under the guidance of Prof. Sadaji Takagi, thehead of the University of Tokyo’s Department of Psychology.
In addition to standard psychological research in such areas
as perception (awareness), learning, and memory, the society
conducted research in social psychology and other areas ofapplied psychology. Takashi Ogawa (psychology of learning,
Keio University) served as chairman, and members included
Hajime Ikeuchi (social psychology, Institute of Journalism,
University of Tokyo), Taisuke Mizuhara (social psychology,
Ochanomizu University), Katsuo Sano (cognitive psychology,
Keio University), Shunsuke Oshio (cognitive psychology, Tokyo
Metropolitan University), Taro Indo (mathematical psychology,
Keio University), Yoshitaka Umeoka (mathematical psychology,
University of Tokyo), and Chikio Hayashi (statistics, ISM).
In addition, a number of University of Tokyo assistantprofessors and teaching assistants associated with Sadaji Takagi
took part, and the society investigated attitude measurement,
scaling and other quantification theory methods. It was
through the society that Hayashi encountered Sociometry
theory (developed by Jacob L. Moreno and Helen Hall
Jennings), which would later become the point of departurefor development of the e ij-type quantification method for the
National Railway lineman survey. eij represents the relationshipbetween i and j. This method later developed into the method
generally known as quantification method Type IV; even later,
the e ijk-type quantification method would be proposed from achanged standpoint.

The society’s findings were published in 1955 as Research on
Quantification Theory in Psychology (edited by Sadaji Takagi).
The preface was extremely interesting; it was penned jointly
by Umeoka, Indo, Ikeuchi, and Hayashi, who stated that during
their meeting to discuss the text, they found it difficult to agree
on an approach. This was because social psychology tends to
avoid the hypothetico-deductive methods employed in mostperception experiments; properly, phenomena to be investigatedare derived inductively from the data. It was only after Umeoka
moved to Hokkaido University that the others were finallyable to arrive at a decision that psychology research should notemphasize hypothetico-deductive methods but experimentum
crucis, that is, repeating experiments and surveys, and whilegathering data, analyzing it inductively while discerning theessence of phenomena.
The society conducted a survey of dormitory residents at
Japan Women’s University. The survey divided the students
into two groups—those who preferred French culture andthose preferring American culture—and sought to discovercharacteristics that distinguished them. Today, this approach
would be referred to as Quantification Method Type II; it has
become routine for categories with external benchmarks. At
the time, there was a fixed view that the external benchmark
was present/absent. The terminology of Quantification Method
Types I to IV dates to 1964 and originated with Hiroshi Akuto(University of Tokyo); it has since become established.

56

57

Chapter Three

5. The computer era
In 1955, Tatsujiro Sasaki was ISM director-general. Before
and during the war, he had been associated with the Aeronautical
Research Institute at Tokyo Imperial University, where he had
been an aeronautical instruments specialist and a leader inengineering mathematics. Sasaki was also deeply interested
in calculators, and before the war he had been building analog
devices for such tasks as solving differential equations. He
subsequently continued to stress the importance of calculatorsand had a strong interest in their development.
In an era without automated calculation, computations
involved a tremendous amount of effort. Hayashi asked his
subordinate, Masatsugu Ishida, to identify a suitable equipment
manufacturer, and finally Fujitsu agreed to build a computer.
The resulting machine was able to perform computations only
slightly faster than they could be done by hand.
Sasaki saw data analysis as critical, and agreed with theviews of Mizuno and Hayashi regarding statistical theory. He
also shared Hayashi’s doubts about Gaussian distributions,and resembled both Mizuno and Hayashi in his tendency to
speak bluntly. He valued experiment over theory, and pointed
to the dangers of relying on hypothetico-deductive methods.
Moreover, this was a time when ISM continued to be divided
by the debate—which amounted to an ideological conflict—within statistics (“statistical mathematics” vs. “mathematicalstatistics”). Seeking to resolve this conflict, Sasaki appointed

58

himself referee, and pitted his people against each other in a
debate that lasted for three days and nights. The losers had toleave ISM.
Through this process, the institute’s direction was unified
around the importance of data, and the union of computers andstatistical mathematics was promoted. The first relay computer
was named FACOM 100 and designed to calculate correlation
coefficients. This computer was completed in 1954. Fujitsu’s
next computer, delivered to ISM in 1956, was the FACOM
128A (also known as the TSK general-purpose relay computer).
These and many other computers were designed by Fujitsu’s
Toshio Ikeda, a pioneer in Japanese computer development.
With the delivery of the FACOM 128A, quantification theory
calculations became significantly easier, facilitating detailed
research into a wide range of phenomena, including election
forecasting. With each election, Hayashi became absorbed inprojecting the election chances of each candidate based on
historical data. The work was thrilling, and the opinion poll
section at The Asahi Shimbun was a hotbed of excitement. The
editor-in-chief was also passionately involved. No one went as
far as setting up an election betting pool, but everyone was ontenterhooks to see what the results of the forecast would be. In
the 1960s, there was a strong impression that public-opinion
polling methods produced accurate forecasts. Before an election,
everyone at ISM was in a festive mood, and getting work done
was difficult.

59Chapter Three

Hayashi had a friend named Ichiro Saito, who worked in the
research department of the Bank of Tokyo. Saito enjoyed data
analysis and doubled as the director of the Economic Dynamics
Research Committee. Saito introduced Hayashi to a rather
unusual friend named Keinosuke Sato, who taught at Musashino
Art University.
Sato possessed an extremely insightful sensibility andawareness. He and Hayashi must have gotten on quite wellfrom the outset, because he often stayed over at Hayashi’s
house in Mitakadai, Tokyo, though Hayashi had only recently
married. There Sato would sketch, listen to records, and spend
time relaxing. Around this time, Hayashi was writing poems inthe style of Valéry, and he would discuss poetics and literature
with Sato. One day Sato said to him, “I’m designing a productlabel, but it’s proving to be a challenge. What sort of Japanesedesign do you think would work overseas?” Foreign images ofJapan tended toward Mt. Fuji and geisha, which Sato wanted
to avoid, but the client insisted that both to be included in thelabel for export purposes. Sato wanted to know if there was any
way he could demonstrate that the client’s preference was anindication of poor taste. Hayashi’s reply was that data relating topreferences for a wide range of product labels did in fact exist.
Hayashi consulted the data, and after pondering the problem,
it occurred to him that label preferences could be plottedalong a diagonal line. Sato was impressed with this idea andimmediately set about working on the calculations. This was
the beginning of Quantification Method Type III (1956). This60

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61

Chapter Threemethod quantified simultaneous classification based on personal
choice patterns. This approach produced good results when
fed into the relay computer, and Sato was elated. (The logo
for Behaviormetrika, the Behaviormetric Society’s Englishlanguage journal, is also the work of Keinosuke Sato.)

Around this time, the development of computers was driving
their increased use in medical diagnostics. Medical practitioners,
whose original purpose was to diagnose and cure disease,were treating the suffering individuals before them as objectsof observation, and diagnosis was carried out using scientificmethods. Motosaburo Masuyama’s theories on inferential
statistics Shosurei no matomekata to jikken keikaku no tatekata(How to organize limited-case examples and plan experiments)
dominated the field of medical statistics, and much data analysis
on the medical frontline was carried out with reliance on his
work. This infuriated Hayashi, who asserted that limited-casemethods for analyzing patient data were outrageous (Hayashi
1973, 1977), and furthermore that treating patients as objects
could lead to unforeseen errors. In a sense, the approach involved
major hidden dangers, and considerable risk of departing fromthe true path of medicine. Today, patient-centered medicine ispracticed, but at the time, individual patient differences werehardly taken into consideration in medical treatment.

The majority of medical statistical models did not fully treat
reaction and question response statistical errors. For example,
a certain drug is developed and is said to be effective against
a given disease. However, while the drug may be effective in
specific patients with specific characteristics, it may not beeffective with patients having different characteristics. Why?
Because the new drug was aimed at patients with specificcharacteristics, and the differences between those patients andother patients was treated as a statistical error. In response,
Hayashi developed a response error model to enhance theeffectiveness of new drugs. The model aimed at identifying
patients who had been treated as statistical errors. In a sense,
implementing the model required considerable time. The same
patient had to be tested repeatedly. This yielded a probability
that the drug would be effective on the same patient in the next
trial. That probability became the key to forecasting the patient’s
reaction on the next trial.
The same approach applies to public opinion surveys. One
asks the same person the same survey question a second timeand measures the distance between responses. The idea isto weight the survey with the resulting Bayesian probability(subjective probability) to obtain a closer approach to the true
value. If a patient is administered a new drug every other week
and fails to respond, the difference between that patient andthose who do respond to the drug will become apparent. This
will make the patient’s personal variation clear, and may also
reveal disease etiology in patients who had been treated as

6263

6. Raising concerns about statistical mathematics inmedicineChapter Threestatistical errors. The problem of individual variation between
patients later became an important challenge for Hayashi.
Furthermore, having extended quantification theory to market
research, Hayashi applied it to the field of medicine, first in
joint research with Eiichi Kimura (Cardiology Department,
Electrocardiography (ECG) Research) of Nippon Medical
School. The impetus was research Hayashi carried out with
Satoki Ninomija (later Professor Emeritus, Aoyama Gakuin
University), a member of ISM Research Dept. II, regarding
automated ECG measurement. Ninomija asked Hayashi tohelp him with an inquiry from Kimura regarding what could
be discerned statistically from ECG wave shapes, and this led
Hayashi to become acquainted with Kimura. After that, Hayashi
delegated most of his physical exams to Kimura and his
students.
Today, Quality of Life (QOL) is routinely taken into
consideration in medical treatment. When Hayashi was doing
his research, QOL was not considered at all; today, most large
urban hospitals emphasize QOL and patient-centered care. Fromthe perspective of modern medicine, which takes into accountthe whole person and individual factors when making treatment
decisions, medicine at that time was very different. In Hayashi’sview, it was extremely dangerous to attempt to understandcomplex phenomena using theory; complex phenomena must be
grasped through data. In 1965, when Hayashi underwent surgery
for glaucoma, he experienced that danger personally.
The journey to quantification theory bears the continuous

traces of Hayashi’s unshakable conviction and intellectual
philosophy, which was always in sharp focus. His stancetoward the correct gathering of survey data was stringent
and uncompromising. But today, survey data collection has
undergone a sea change from Hayashi’s era; the excessive
regard for personal privacy means surveys cannot be carriedout easily. Nevertheless, society is drowning in data. How canmethodologies be developed to make Big Data more intelligent?
How can the identification of essential aspects of data be
optimized to analyze phenomena from data? And how candiverse forms of wisdom be harnessed to extract real data fromraw data? Many challenges remain. For Hayashi, grappling withsuch challenges was a lifelong endeavor.

64

65
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Chapter Four

1. Marketing research efforts
Around 1953, Masao Okemoto of the Asahi Shimbun
newspaper’s advertising department visited ISM and asked
Chikio Hayashi for help in researching how to optimize
advertising costs and effectiveness using operations research(OR) methods. OR originated as a tool for military planning,and Hayashi’s wartime efforts to enhance the accuracy of attacks
on B-29s through data analysis was pure OR. This technique
was now to be used to maximize the effectiveness of advertising
spending—in effect, to maximize the effectiveness of spendingon marketing research.
The Asahi’s ad department was very interested in bookadvertising. It was decided to divide their subscribers intobook lovers and non-book lovers and use Quantification
Method Type II for the analysis. This was the first exampleof quantification analysis methods being used in the field of
marketing. Moreover, quantification would be used to forecast
readership scores for newspaper ads. Takayoshi Murayama, who
had been with the paper for less than two years, was assigned towork with Hayashi. Hayashi called his analytic results “factor
analysis,” though it was actually Quantification Method Type I.
Hayashi worked extensively with Murayama, and in 1964
they co-authored an account of their data adventures, publishedby the Nikkan Kogyo Shimbun newspaper, titled Shijo-chosano keikaku to jissai (Market survey planning and practice).
This was Hayashi’s first description of quantification theory

68

in print, and the book was read widely, not only by marketing
professionals. Hayashi was surprised to see researchers in otherfields walking around with a copy. As a result, quantification
theory made inroads into marketing and related fields, andHayashi was struck by the widespread hunger to learn more
about data analysis methods.
Japan at the time was pouring effort into developing new
products, and quantification methods were essential for market
surveys. Word spread that Chikio Hayashi from ISM wasplaying a key role in forecasting potential demand for new
products, and requests for help poured in—not only for market
surveys, but often in the field of medical research as well.
2. Program rating projections
With the widespread adoption of television, there was a need
to forecast program viewership. The methods used to survey
and analyze viewership resembled newspaper ad factor analysis.
But with television, there was also a need for detailed programcontent analysis. Also important was the program broadcast.
Meiko Sugiyama of NHK’s Broadcasting Culture ResearchInstitute was working on this problem. Sugiyama was an
enthusiastic convert to quantification theory, and was using it
in her analysis. In the early 1960s, she had used Quantification
Method Type III to analyze radio program preferences, but
gradually she moved into TV program analysis. Her approach
involved taking a viewership survey from a particular point in

69

Chapter Fourtime and establishing the relationship between factor patterns
and ratings. When a new program was broadcast, the projected
and actual ratings matched.
Around this time Jun’ichi Komiya, manager of the advertising
department at Morinaga, the confectionery manufacturer,
was on edge about the ratings of a television program hiscompany was sponsoring. Today, the usual practice is for
multiple companies to sponsor a single program, to distribute ad
investment risk. But in those days, advertisers felt they should
not share their sponsorship, and most programs had a single
sponsor. If the program failed to attract viewers, the reputationof the company’s advertising department would suffer. As
such, there was much more at stake for advertisers than there istoday when deciding whether or not to sponsor a new program.
Komiya had already gotten Hayashi’s consent to use his“sensibility quantification theory” to project program ratings,
and the effort had been successful. Hayashi willingly agreed
to advise Komiya, and Morinaga continues to use sensibility
quantification theory today. The ratings forecasts yielded by thetheory have maintained the advertising department’s reputationand helped the company maintain its market leadership.
NHK and Morinaga obtained good results from quantification
theory, but it was not necessarily the case that companies inevery industry understood what they were doing when they used
quantification theory to forecast program ratings. In Hayashi’sview, the marketing research carried out by people in theadvertising industry, people who were uniformly unable to fully

grasp quantification theory and lacked the vision to anticipate
the future, was a complete waste of time. That was how shallow
their knowledge was when it came to analyzing data of researchsubjects. Marketing research conducted under such conditions
was utterly pointless. As long as no thought was given to theorganizational structures needed in order for marketing research
to shape management strategy, the effectiveness of suchresearch would be extremely limited.
Looking back on his war experience, it seemed to Hayashi
that the military was better organized. The Imperial Army
General Staff Office consisted of three departments, withthe first handling organization and strategy, the second
responsible for information gathering, and the third covering
intelligence. Everyone recognized the importance of theliaison representatives who circulated between departments.
Although the Office was divided into three departments, officers
kept abreast of the situation in other departments throughthese liaison representatives. The officers in each department
listened to their representatives carefully, which enable them toparticipate in strategy from a shared perspective. But marketing
research was not carried out within corporate structures having
the same level of commitment. A large company like Dentsu
had some very capable people, but it simply lacked enoughindividuals who were familiar with the kind of surveys and data
analysis that Hayashi was aiming for. As such, the required
survey systems had to be put in place.
Consequently, survey companies sprang up like mushrooms

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71

Chapter Four

after rain—marketing research institutes, research associations,research service providers. These new players were not directly
connected to Dentsu or university research units. They were
established as the need arose, reflecting the prevailing need forsocial surveys.
In 1985, at age 67, Hayashi became chairman of the Japan
Association for Public Opinion Research. In those early days,
university professors conducting joint research projects with
Dentsu would send their graduate students to learn on thefront lines by participating in research at Dentsu. However,for the students, such work was generally a way to makea bit of money; a student participant might not visit all thehouseholds he was assigned to survey, but instead would spend
the time in a coffee shop, filling out a stack of surveys himself.
When Dentsu’s marketing survey team discovered what was
happening, they realized they needed rigorous survey methods,
and they began visiting ISM on a daily basis.
The rapid propagation of television convinced Dentsu thatratings were important, and they established Video Research.
To carry out field surveys, they established another subsidiary,
Dentsu Research. (This company would later absorb Dentsu
Institute, becoming Dentsu Marketing Insight. In the years that
followed, Dentsu would navigate successive turbulent eras,building headquarters in Tokyo’s Ginza and Tsukiji. Today,
they are based in a massive main office in Shiodome, and are
involved in a wide range of marketing industry fields.)
Video Research’s challenges included sample design for

ratings surveys, data collection, sample secrecy, data analysis,
and the presence of multiple TVs in the home. As they
addressed these challenges, Hayashi carried out extensive data
analysis at Dentsu and propagated his views. Computers wereadvancing and spreading quantification methods, market surveys
were becoming more sophisticated, and market researchrelated departments and their research were strengthening.
Moreover, there was prevailing dependence on American survey
methods. But Hayashi was becoming convinced of the needto develop new, Japanese-style survey methods, to research
their effectiveness, and to use those results to carry out a newJapanese National Character Survey.

72

73

3. The frontline perspective
When launching a new product, one needs to prepare for the
possibility that someday the product will cease to sell, even if
it is initially successful. Hayashi visualized this reality in termsof victory or defeat—that is, in terms of armed conflict. During
the war, he had been part of what was in effect an operations
research unit. There he had dealt with a frustrating amount ofnews of defeat. When he was consulted by Dentsu’s marketing
department, he felt as if he were listening to bad news fromthe front. The process resembled the wartime situation to asurprising degree. In his mind, present and past always seemed
to overlap.
Dealing effectively with defeat is extremely important, but

Chapter Fourthe Japanese spare little thought for it. Such a view strikes themas defeatist and bad for business, and generally they refuse
to discuss it. In particular, given the Japanese temperament
and their tendency to be influenced easily by prevailingmoods, talk of defeat can bring it about, and thus they havea strong tendency to avoid such talk. Because of this, when
corporations fall on hard times, much is demanded of those in
the company who deal in information. If measures are taken
before the situation is serious, problems can be averted. If those
responsible for gathering information devote all their efforts todetecting indicators and judging situations, there is much scope
for them to act. But once the situation has turned negative, only
drastic measures will serve—measures that are beyond theircapacity to carry out.
When conditions are favorable, everything goes well,regardless of how marketing is handled. When conditions callfor being aggressive, one must be aggressive, but with theunderstanding that at some point, one will be forced to go on
the defensive. Marketing is needed that can discern when thattime has come, and can deal with it. This makes it important
to detect indications of the change. Then, while conditions
remain favorable, one must judge what factors contributed tothe success, what actions were taken based on information,
and what actions need to be taken now, before they become
necessary.
Similarly, during the war, it was important to understandthe differences between command structures in Japan andAmerica. In the case of Japan, the higher the authority, themore commands from that authority tended to be motivational
exhortations lacking in specifics. The assumption was that basedon the stated general principles, detailed commands would
be worked out at lower levels and issued as needed, but thesubordinate organizations lacked sufficient personnel capable of
this.
When the product is a hit, aggressive marketing can have
explosive success. But as the product loses popularity,
subordinates are unable to mount a response. Motivational
exhortations may come from above to save the situation, but
the front line lacks the needed training; they retreat and become
progressively weaker as they go down the path to destruction.
In America, if every military detail is planned at the top, thensubordinates follow the blueprints laid out for them. This
strategy is very effective when conditions are unfavorable.
One can take the larger perspective, ignore individual frontline
aspects, determine a line to retreat to, carry out the retreat, andfrom there, counterattack to regain lost ground. The exact same
path can be taken in management.
In Japan, as noted by Hayashi, the view from the front line isgiven priority. In the military, no one will listen to anything said
by a pale, soft officer unburnt by the sun. The frontline fanaticis a difficult problem in the military as well as in the enterprise.
An officer or leader defeated on the field is given a position ofimportance in view of his frontline experience. This makes himvery hard to deal with; he can’t make comprehensive moves,

74

75

Chapter Fourand the situation gradually deteriorates. Hayashi repeatedly
asserted that the same problem continued to plague Japan.
Hayashi mocked the Japanese tendency to think one canunderstand a situation with extensive exposure to different
frontlines. The frontline is important. However, a correct view
does not encompass only the intuitive sense one acquires
from experiencing a wide range of situations. It also demands
considerable scientific expertise. Effecting this change inawareness and belief cannot be accomplished overnight. Froman early stage in education, one must cultivate a generalized
stance that favors not only beliefs, but taking up and actuallylooking closely at the real thing, too and absorb something from
it. Primary education that creates a craving, welling up fromwithin, to absorbing knowledge is manifestly necessary.

Ness Monster, supernatural abilities, etc. using multidimensional
scales.
Originally, Multivariate Analysis had been used extensively
in the field of mathematical statistics. In opposition to this, ISM
was using Multidimensional Analysis methods. While the former
is a method for analyzing quantitative data multidimensionally,
the latter is a method for analyzing qualitative data that isquantified, also multidimensionally. Moreover, this method
resembled Guttman’s Smallest Space Analysis (SSA) approach.
In response to SSA, Hayashi devised a method of analysis hecalled Minimum Dimension Analysis (MDA). The similarityof these method names is tricky, but in the next section we will
delineate and compare SSA and MDA.
[A] Minimum dimension analysis

4. The Multidimensional Analysis Research Group
The Multidimensional Analysis Research Group was formed
at ISM in 1972, led by Chikio Hayashi and Hiroshi Akuto.
The group was fortunate to receive a grant-in-aid for scientificresearch from the Ministry of Education, Science, Sports
and Culture, and as the members debated their first object of
research, it was decided to tackle a theme no one had worked
on before. This was the start of the “bogey” (Japanese: obake)survey. It would attempt to examine and analyze not only
bogeys in the narrow sense—evil spirits, ghosts and so on—
but also UFO, water sprites, the abominable snowman, the Loch

MDA is a multidimensional scaling method devised todiscern the essence of diverse phenomena, consisting ofmultiple events, by discarding the elements of those events
considered to be unnecessary, in order to characterize thephenomena in terms of the smallest number of variables. Thatis, the method summarizes phenomena that contain complex
foreign elements and uses Occam's Razor (parsimony)extensively to express phenomena succinctly. Since the adventof multivariate statistical analysis, the Minimum Dimension
concept, due to the nature of the analytical method, shouldhave been used frequently, and have been a focus of debate.

76

77

Chapter Four

However, the concept has been insufficiently debated since it
was first introduced, and for the most part has been limited tosuch explanatory concepts as the central challenges of reducing
dimensionality, minimum dimensionality, and data reduction.
For example, techniques such as factor analysis simplify
complicated data sets, making them easier to understand.
They seek to express data as two- or three-dimensional planesurfaces. This sort of spatial expression generally is limited to
two-dimensions. Nevertheless, the data is not fully explainedin two dimensions. There are as many dimensions as there arevariables; these are not mathematical dimensions, but simply
empirical class divisions, with the aim of categorizing many
variables as efficiently as possible. The basis for ordination
is to group things with high mutual correlation, or things thatare highly similar, to compare the bases for classification, andto interpret the dimension. Among the variable “items” will
be many that have deep mutual relationships, and throughordination, the number of variables required to explain thephenomenon under examination should be reduced. Mutual
relationships between variables are considered, and variables are
separated into classes to explain phenomena using the fewest
possible variables. If group partitioning can be performed
beforehand, it may be possible to eliminate this effort, but formost complex phenomena, this is seldom possible. Instead,one must rely on analytic methods such as factor analysis inattempting to elucidate phenomena.

78[B] Nonmetric MDS
After Warren S. Torgerson introduced the method of metricmultidimensional scaling (MDS), Roger Newland Shepard
developed non-metric MDS (nMDS), which can be considereda general solution. As an analytic method for finding minimum
dimensions under relatively non-restrictive conditions, it opened
the way to MDS in general. As a method, it is somewhat richer
in conciseness than factor analysis and Torgerson-style MDS. A
target function (normally monotonic) that can maintain a certain
minimum dimension is defined; beginning with one dimension
and proceeding to higher dimensions, conditions applying toeach dimension are considered, with the goal of resolving within
about three dimensions. If three dimensions are insufficient,
one begins again using trial and error, selecting variables andcross-tabulating to consider such factors as interaction between
variables. The approach of Joseph Bernard Kruskal whoorganized the algorithms used in Shepard’s method, begins withmore dimensions (typically three) and proceeds by successively
reducing the number of dimensions while examining conditionsof fit.
With Shepard-Kruskal nMDS, the outlook is meager when
one analyzes data that is seemingly complex unless the natureof the data is expected to be clear. Further, where detailed
projections for a phenomenon are established from the outset,
the results will often be just what one would expect. The
reason for this is that the assignment of initial values is a79

Chapter Fourrather mechanical task that effectively disregards the natureof data itself (Shepard uses vertex coordinates of a regular
simplex in which the mutual distance between two points will
all be the same value; Kruskal uses all random values), andmay not necessarily be compatible with that data. One iterates
forcibly and repeatedly to obtain the minimum dimension—and somehow, a result—without assurance that the result will
be reasonable. This is where the problem of data reproducibility
becomes serious. Furthermore, attempting to explain everything
in three-dimensional space means fixing the number ofdimensions in advance; the applicable data must therefore meetthe prerequisites for resolution in three dimensions. There isthus little possibility that the method would be suitable forthings like complex social phenomena.

Louis Guttman’s SSA and Chikio Hayashi’s MDA wererevolutionary responses to these challenges in the way theyhandled assignment of initial values; values were selected toreflect faithfully the nature of the data. To take the nature of
the data fully into account beforehand, SSA used facet design
to consider data deployment structures and define the factors.
For some data, one approach or the other (SSA or MDA)
may be effective; it is necessary to consider and analyze thecharacteristics of the data. Regardless of the superiority of theanalytic method, the diversity of social data requires an acuteability to discern how the suitability of the method depends
on the nature of the data. Today, in an era awash in complex
data, it is especially important to be fully aware of this; and it
is most important to eliminate pervasive data errors, such asmeasurement error and response error due to response (answers)
and non-response. These processes are indispensable foranalyzing data scientifically. The divergence between raw data
and true data is an eternal challenge. It is natural to look for ananalytic method that streamlines and simplifies complicateddata, but one must also evaluate the simplified data carefully todetermine whether or not it is also the true data, because the true
data may be hidden in the raw, impure data that was discarded.
SSA manipulates facet dimensions to order the data, fully
taking individual differences into account. MDA partitionsgroups so as to minimize response error. Around the time
MDS, which considered individual differences, was at thepeak of its popularity, Guttman’s method, and that of Ledyard
R. Tucker of the University of Illinois, were widely used.
Today, this type of MDS has fallen out of favor. In surveys andexperimentation, an emphasis on simplification seems to havebecome the norm.
Hayashi’s MDA was named with a nod to Guttman’s SSA.
In Hayashi’s development of quantification theory, his mostpressing task was to verify the so-called KL-type quantification
method (based on the computational procedure idea that extends
the dimensions). Since the appropriateness of the successiveapproximation method, as well as the goal of identifying the80

81[C] SSA and MDA

Chapter Four2

None or with diﬀerence

X

Supernatural powers●

Age（0.93）( ) Indicates the rankcorrelation coeﬃcient.
The average rank correlationcoeﬃcient is 0.87.

Wrathful spirit(onryo)Time machine●

0.5● Vengeful spirit
Wrathful spirit● Ghost(onryo)●0● the Abominable snowman

-0.5

0.5

Demon
(oni)

Education（0.91）● UFO (aliens)● the Loch Ness monster
1

Water sprite (kappa)

X

Supernatural powers

Support ofpolitical party

Gender（0.80）

Dragonthe Abominable snowman
the Loch Ness monster
Time machine

Phantoms (yokai)
Ghost

Demon (oni )
2
1

＝0.54

2
2

＝0.83

2
3

＝0.84

-0.5

●

UFO (aliens)

Phantoms●●
Dragon● Water sprite (kappa)

Vengeful spirit

City, country（0.89）

Figure 1

Figure 2

Analysis of bogeys.
Three groups can be seen there.

Items, attributes, and relationships.

82

83

Chapter Fourfewest number of dimensions, were clear, it was when Hayashi
was verifying his method by comparing it with Guttman’s SSA
that he first used the name “MDA.” However, because of thenature of the KL-type quantification method as an analyticaltechnique, the limiting terms are stringent, and the method
could be said to be more metric than non-metric, or perhaps
semi-metric. It therefore has the disadvantage of only being
applicable when the data is clearly organized or the numberof variables is small. In that sense, one can consider MDA to
be a generalized form of the KL-type quantification method.
Minimum Dimension Analysis of ordered class belonging(MDA-OR), MDA of unordered class belonging (MDA-UO),
and other analytical methods were developed successively,
but because they can only be applied to simple, limited data,
development of future MDA methods remains a challenge.

In 1976, the so-called “bogey” survey, a research toinvestigate the foundations of the Japanese national attitude, wasfinally carried out. The survey was conducted in two locations:Tokyo Metropolis and Yonezawa City in Yamagata Prefecture.
The degree of awareness of bogeys was somewhat higher inYonezawa, but the regional difference was not marked.
Bogeys (mysterious or unexplained objects) were organized
into twelve items: the Abominable Snowman (yukio), theLoch Ness Monster (Nessy), UFO (aliens), ghosts (yurei),water sprites (kappa), phantoms (yokai), supernatural powers,
vengeful spirits, wrathful spirit (onryo), time travelers, dragons,
and demons (oni).
Since each item could return one of eight different responses,
the survey took a long time to conduct, but the subjects foundthe content interesting. Response categories were: “Do they
exist?” (Yes, No); “Do you hope they exist?” (Yes, No); and“[What is your] subjective reaction?” (Afraid, Interested,
Uninterested, etc.). The same survey was carried out in1976 and 1978. The survey also asked about beliefs andawareness of beliefs, specific religious practices, degree of
religiousness, daily life customs, conventions, knowledge ofthe survey items, importance of superstitions, importance ofomens, confidence in the existence of bogeys, use of amulets,
supernatural phenomena, fortune-telling, use of folk remedies,etc. Details can be found in Tajigenshakudo kaisekiho—sono
yukosei to mondaiten (Multidimensional scaling analysis
methods: effectiveness and challenges) (Saiensu-sha, 1976),
and Tajigenshakudo kaiseki no jissai (The facts about
multidimensional scaling analysis) (Saiensu-sha, 1984). Bothtitles were edited by Chikio Hayashi and Hiroshi Akuto.
Let us look at the results and consider what the Japanese
think about superstitions. It is generally thought that belief insuperstition reflects a lack of education. But the survey revealed
that the more education a person had, the more superstitious
they tended to be. In a sense, the higher the educational
background, the less scientific the person’s views. In particular,

84

85[D] The “bogey” survey

Chapter Fourawareness was high for telekinesis, time travelers, UFOs, theLoch Ness Monster, and the Abominable Snowman.
Even after the survey, Hayashi maintained an interest insupernatural phenomena. He conveyed his thoughts aboutbogeys in many newspapers and magazines, including the
Asahi Shimbun and Surikagaku. Citing Torahiko Terada’s
essays on monsters, goblins, and ghosts, on uncanny
phenomena and bogey evolution, Hayashi argued that some
aspects of phenomena, which would seem somewhat rationalon the surface, could only be regarded as irrational, andsuch supernatural phenomena would probably be provenscientifically at some point in the far future. The excellence ofthe human intellect, Hayashi said, was due to the passion forthorough inquiry, driven by curiosity and the thirst to uncover
the unknown.
Torahiko Terada believed that there was something in
the human mind called the Old Layer. When triggered, thisunconscious component could affect human behavior. In
psychological terms, it is typically referred to as the collective
unconscious. It causes people to act in incomprehensible
ways, creating a world of irrational phenomena that people arecompelled to regard as mysteries. This irrational world is anever-present element of the individual’s mind. This primitive
world, cultivated over tens of thousands of years through genetictransmission, is the Old Layer, inherited over generations. If
it suddenly manifests, operating in unexpected, unconscious
ways, there is little room for the law of cause and effect. Thesephenomena cannot be accounted for in distinct data, and aresimply factored out. It occurred to Hayashi from time to timethat if this precious, “irradiated” portion of the data was being
ignored, elements required to arrive at true values might be inthis discarded data. This sensibility seems to underlie Hayashi’s
passion for a response error model that would return true values.
Is this primitive, irrational mind something we can simply
laugh at and ignore? Hayashi’s desire to analyze the existence of“quasi-cause and -effect factors” with multidimensional scaling
may have arisen from this murmur deep in the heart.

86

87“My hope is that data science will contribute to the developmentof a science liberated from the curse of cause and effect, onethat through investigation can enhance individual wisdom andbroaden individual knowledge while dealing with complicated,
ill-defined phenomena.” (Chikio Hayashi, Deeta no kagaku[Data science], Asakura Publishing Co., 2001.)

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

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Chapter Five

In January 1947, less than a month after Chikio Hayashi
joined ISM, Soichi Kakeya died. Kakeya had been Hayashi’s
university supervisor and, as the director-general of ISM,
his immediate superior. Kakeya had also been serving aschairman of the National Research Council of Japan, and had
continued to negotiate with the Allied occupation authorities,
resisting American policies aimed at rendering the Japanese
simpleminded and ignorant. Perhaps these difficult negotiations
exhausted him; in any event, his tumultuous life ended at theage of 60. After an interval of four months, Joichi Suetsuna of
the University of Tokyo’s Department of Mathematics, Facultyof Science, stepped in as temporary director-general for about
one year. For Hayashi, who had just joined ISM, the situation
was a source of great anxiety. But in fact, Suetsuna became asecond mentor to him, and Hayashi learned much from his new
superior.
Years later, in 1971, the author of this book, then an
assistant at Aoyama Gakuin University’s College of Literature
specializing in psychology, was sent to work with Hayashi
at the recommendation of Masatoshi Seya, her academic
advisor. Seya stated that if he had been a woman, he would
likely have fallen in love with a man like Hayashi. With thisextremely frank admission of emotion, Seya dispatched his
precious student to Chikio Hayashi, as though relinquishing his
own child for adoption. As part of my major, I had previously

studied mathematical statistics at ISM; now, unexpectedly, I had
become Hayashi’s student.
I was majoring in the field of mathematical psychology, whichattempts to model psychological phenomena mathematically.
Since these phenomena are nonlinear, they require the useof nonlinear mathematics. In addition, spaces populated byhuman beings are non-Euclidian. Euclidian space is a special
case of non-linear space, and elucidating it is a precondition
when attempting to reveal psychological phenomena. However,at the time, computers were still relatively undeveloped, andhand-solving non-linear equations was not at all practical.
Consequently, I performed calculations for a restricted portionof the data, then attempted to apply the results to the data asa whole. Hayashi told me that I would have to discover thesection at which the curve with a single peak (as exemplified by
a Gaussian distribution) transitions to a quadratic curve. It wasnecessary to divide the method into a linear model and a nonlinear model, and examine their structures and relationships.
Hayashi decided that the results of this research should bepresented to the Japan Association for Philosophy of Science,and directed me to do so under the title, “The Problem ofNon-linearity in Psychological Measurement.” At the time,
Quantification Method Type II was widely used to maximize
correlation ratios, and ultimately the problem was resolved
using that method. But today, with advances in computers, theproblem can be elucidated without using that sort of stopgap
measure. The non-linear problem arises in the rudiments of

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91

1. Measuring human behavior

Chapter Fiveexperimental psychology, and the Weber-Fechner Law relating
to thresholds (of stimuli and perception) also ultimately returns
to this problem. In toxicology as well, toxins sometimes have noeffect at all even above a certain level. This problem was treatedas an error term in Probit analysis. At the time, as an academic
tendency, hardly any researchers adopted the non-linear problem
as a research theme.

In 1973, a group of young psychologists asked Hayashi tobecome the first chairman of the Behaviormetric Society ofJapan. The society’s aim was to encourage behaviormetricresearch into human action in a range of specialized areas. It
was the first academic society to advocate for interdisciplinary
research of this kind. The Japan Association for Philosophyof Science was cross-disciplinary in nature, but it had a wider
scope and conducted research into human behavior in general.
The Behaviormetric Society included numerous member
specialists in psychology, sociology, medicine, and marketing
research. Initially the society was presided over by researchersin economics, law, mathematics, and so on, but they never got
used to the idea of behaviormetrics and moved out of necessity
to the Japan Statistical Society. The offices for both societies
were housed with ISM, and in the beginning they were tacitly
in conflict, divided, as ISM had been earlier, into warring

factions, one supporting statistical mathematics and the other,mathematical statistics. Hayashi, who had become somewhat
dissatisfied with such a work environment at ISM, had beenworking hard, even before the Behaviormetric Society wasestablished, on the concept of a “Human Behavior ResearchCenter” to further human-related scientific research.
This idea had been under consideration intermittently since1968. Hayashi had been working to correct the prospectus andrevise the concept in a variety of ways, when in 1974, he wasappointed as the seventh ISM director-general, and the initiativecame to a sudden standstill. Now he was solely responsible
for multiple ISM projects, and these kept him running around.
Hayashi was the first director-general appointed from within
ISM; previously, the position had been filled by professors fromthe University of Tokyo or a similar institution. Hayashi felt
he could not in good conscience refuse the appointment, andhe passed the human behavior research center concept to hiscolleague, Shigeki Nishihira, and the administrative work to
Kinji Mizuno.
Mizuno studied visual perception and response theory under
Torao Obonai at Tokyo University of Education, and became an
assistant at that university. Through his connection with Obonai,
who at the time was chairman of the Japan Color ResearchInstitute, he was assisting with color research. He and Hayashi
were acquainted, and Hayashi discerned his meticulousness,
his scrupulousness, his data sense, and his excellent eye forphenomena. Impressed by his talents, Hayashi waited until

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2. Kinji Mizuno and the founding of the BehaviormetricSocietyChapter Five

Mizuno’s term as an assistant was ending, and recommended
him for a position as researcher at the Institute of Behavioral
Science. Moreover, several years later, he recommended him
for a position as professor in the School of Education at Nagoya
University. Such was his admiration for Mizuno’s talents.
Haruo Yanai was the Behaviormetric Society’s first secretarygeneral. At the time, Yanai was an assistant at the Universityof Tokyo’s Epidemiology Department, Faculty of Medicine.
Consequently, the secretariat was initially based in thatdepartment. However, since the society’s chairman was basedat ISM, and it was the job of the secretary-general to assist thechairman, his presence at ISM was desirable. As such, Hayashi
recalled Kinji Mizuno from Nagoya, as he was the perfectperson to serve as secretary-general. In October 1975 Mizuno
became attached to ISM, and in April 1977 he became secretarygeneral of the Behaviormetric Society. Beginning in 1991,
Mizuno served for three years as the Behaviormetric Society’s
third chairman.
For five years until 1982, when he went to the US as a visiting
researcher, Mizuno devoted himself to his duties as secretarygeneral of the Behaviormetric Society. He organized thesecretariat, which was being run in a haphazard way by young
researchers, and put the management of the society on a solid
footing. He applied successfully for a grant to enable the society
to publish its journal in English, and in close collaboration
with the chairman, he worked hard to train young researchers.
He was succeeded as secretary-general by Shuichi Iwatsubo,at which time the society delegated part of its operations to
the Center for Academic Societies. To negotiate the transfer
of functions, Mizuno accompanied Iwatsubo to the Center on
numerous occasions before his departure to the US, and hehelped ensure that the various aspects of the handoff between
the two organizations proceeded smoothly.
This is the sort of task that requires an experienced individual.
Iwatsubo was still young, a researcher in his thirties with astrong sense of justice, and was inexperienced in the ways of
the world. He was therefore deeply indebted to Mizuno for thelife lessons he learned and the considerable wisdom he gained
as a result; for Hayashi, it was as if Mizuno never left. Mizuno
later, as ISM director-general Hayashi’s right-hand man,devoted himself with all his energy to the development of the
Behaviormetric Society. Mizuno’s self-sacrifice, and his all-out
efforts on Hayashi’s behalf, are a lesson for us all.
Mizuno was also a social survey expert. He was involved
with all aspects, from implementation, tabulation, to analysis,of the first of the cross-national surveys of national character,which began in 1978 to investigate Japan’s rising position in
the world. He was also busy with the human behavior research
center concept, which had been pending for many years, thoughultimately the plan was not realized. Mizuno’s labors ended in
1999 when he passed away at the young age of 67. After his
death, Hayashi is said to have stood before his grave and vowed
to devote the rest of his life to building the science of data.
In 1988, Mizuno penned the preface to Issue 45 of the94

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Chapter Five

Behaviormetric Society’s journal. This could almost be
considered his last testament to the society. It is also relevant toresearchers belonging to the society today in considering where
they should be placing their energies. The spirit of the content isvery similar to the 2014 statement by society member emeritus
Ryozo Yanagihara, titled “Has the Survey Died?” Thirty-two
years have already passed since Mizuno wrote his “last will andtestament.” His passion for behaviormetrics is just as relevant tothe society’s activities today.
“Do our society’s activities have the wind at their back? Are
there no challenges as we move toward the future? I believe
there is a need for us to review the past.
“First, it is my belief that recent research on analyticalmethods overemphasizes the numerical at the expense of
attention to whether or not the method in question is effective
for the actual objective. Might this not be true? If today’s
new, sophisticated analytical methods are unable to extract
new knowledge and discoveries that could not be obtained bytraditional methods, they are meaningless. In that case, do we
not need to make the effort to develop analytic methods specificto a particular problem and its associated data, however difficultthat may be? Perhaps my expectations are high, but I would atleast like researchers to have the needed mettle. Second, withrespect to the foregoing, I hope researchers will be intenselyinterested in the phenomena they study. Rarely seen majorchanges are underway in society today, and a huge number ofchallenges are waiting to be addressed. Especially in the field of

humanities studies, we must bring diverse and large amounts ofdata to achieve effective analyses that can elucidate problems.
At the same time, while we call our era the “era of information,”carrying out a single survey has become difficult when personal
data must be kept confidential and managed properly. I therefore
hope that researchers will collaborate to meet this challengewith vigor. I think that the sense of aesthetics widespreadin Japan is the reason we tend to be drawn to mathematics;
however, I believe the quantitative methodologies that are theaim of this society are the scientific, integrated approaches thatcan solve difficult real problems, even if they might seem rougharound the edges compared to mathematical beauty.”
What would Mizuno say if he saw the intricate reality ofsociety in the 21st century? Ryozo Yanagihara’s essay, “Has theSurvey Died?” was based on conversations with Mizuno that
he had held in his heart for 20 years. Yanagihara has stated that
his title is a testament to Mizuno’s passion as well as his despair
regarding surveys, which inherently demand straightforward
powers of perseverance. This is what drove Yanagihara to writewith the feeling that straightforward perseverance of the kind
Mizuno advocated continues to be necessary if surveys are to
survive.

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3. Growth and evolution of the Behaviormetric Society
A year before Hayashi became director-general of ISM,
in 1973, the author and Haruo Yanai hosted a “MultivariateChapter Five

Analysis Symposium” at Gakushikaikan in Hongo, Tokyo. Yanai
told me privately that he hoped the symposium would become
an academic society with a membership in the range of 2,000,
and he wondered whether Hayashi would consent to head thenew organization. Both of us were still in our twenties. Today,
capable people are encouraged, regardless of their age; backthen, however, the idea of young people founding an academic
society would have invited contempt. Consequently, though weapproached Hayashi with the idea, he dismissed it. When we
persisted in approaching him a second time, he generously gave
his consent, telling us he expected much from a society foundedby young people, and he gave the society its name.
This was the birth of the Behaviormetric Society of Japan. The
society named its English-language journal Behaviormetrika,
reflecting the influence of Psychometrika (the official journal of
the Psychometric Society). The society’s goal was to quantify
human behavior and elucidate a wide range of such behavior
from diverse philosophical perspectives. Rather than collect data
only and subject it to mechanical analysis, structures hiddenin the data would be extracted to enable phenomena to beobserved logically. This was true interdisciplinary research, andresearchers from many fields joined the society. The membersof the society secretariat worked tirelessly to gather people
to join the society. One memorable part of this effort was the
publishing of an invited paper in the first issue of the society’s
English-language journal, Behaviormetrika, by Naoki Komuro,the then-popular, proud, and independent social scientist witha fighting spirit. Komuro was respected by warriors of the AllCampus Joint Struggle Committees, which had sparked campus
activism, as the hope for the movement. He ran an independent
seminar at the University of Tokyo, and became popular withthe students with his displays of erudition.

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It was my job to convince Komuro to write for the journal. I
met with him frequently, and he consented finally to contribute a
paper titled “On the Concept of ‘Marginal Function’ Especiallyin Reference to the Sociology of Law.” This proud, independent
genius became a member of the Behaviormetric Society, but
papers for publication in the journal must go through a screening
process, and after certain papers by Komuro became subjects ofcriticism, he withdrew from the society in resentment. But hewas a fervent supporter of Hayashi, and even after his departure,
Komuro maintained ties to ISM.
In the beginning, the society was full of passionate energy,
perhaps because it was steered by young members. But
rapid advances in computer technology drove an increasing
tendency to rely on them. As chairman of the society, Hayashi
was frustrated by the indiscriminate use of computers andthe bootstrap method in data analysis by young researchers.
Often and everywhere, he continued to question whether or not
behaviormetrics had a guiding philosophy. But this awarenessthat a problem existed is absent in today’s young researchers,
and Hayashi’s theories gradually lost their influence.
Behaviormetric Society membership has largely becomeChapter Fivefocused on frontline social surveys; moreover, there is little
room to debate philosophies and ideas. Investigations of detailed
mathematical mechanisms for handling big data are beyond thebounds of the behaviormetrics of the past. Times have changed,
and researchers’ awareness has changed along with them.
We live in an era when someone who might never haveheard of Chikio Hayashi styles him- or herself a data scientist.
There are likely also those who think that recalling a time
when Hayashi’s theories were passionately debated is nothing
more than sentimentality. But I would like them to reconsider
and remember. Human beings are not robots (unless one
contemplates a replication of the human brain). To survive,
human beings must, by nature, face existential questions.
The various services, and the—likely temporary—shopping
conveniences offered through today’s internet will eventually
become obsolete. Those who seek to amass knowledge
through their own efforts will go in search of challenges, climb
unexplored peaks, and peer toward far-distant horizons. The
gift of human wisdom, which will never be exhausted, is hidden
therein.
“It is a pleasure to be moved while reading an academic paper.
If I may relate a personal experience, there is one paper I readthat deeply moved me, that revealed to me just how fascinating
mathematics is. When I was studying probability, I read Richardvon Mises’s first paper on that subject. I was deeply moved.
John von Neumann’s "[Zur Theorie der] Gesellschaftsspiele"
struck me exactly the same way, as did his proof of his ergodic

theorem. Reading Abraham Wald’s proof of the existence ofa collective in probability theory, and the first editions of vonMises’s Probability, Statistics and Truth, made we want to dancewith excitement.
“I knew that probability and statistics were extremelyinteresting mathematic fields that would be my path in life. I
remember spending days where I felt intoxicated.
When my teacher told me to study statistics, I read books
and papers on the subject. They impressed me as being truly
absurd and uninteresting. But when I discovered that the allureof statistics is the unity of knowledge and practice—when I
experienced it, and not simply read about it—my heart was
moved. Since then, my research has had nothing to do withempty theory. Knowledge and practice are always unified. I
concentrate and observe. I do nothing other than that.
“Writing an academic paper is like being the conductor of anorchestra; you want to present superior results in the best way
possible. A performance that fails to thrill is a poor performance.
Just as a poet may write a poem that brings the poet himself totears, it is wonderful to be a researcher writing up your results,
and moved so much that you leave the tracks of your tears on
the manuscript. In today’s world of word processors, wouldn’t it
be lovely if your tears ruined your keyboard?” (Chikio Hayashi,preface to Nihon kodo keiryo gakkai kaiho (Behaviormetrics
Newsletter), No. 84, 2000)

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Chapter Five

4. Hopes for behaviormetrics
Today, a researcher is often measured by their numberof publications. Regardless of the content, or whether they
are empty or meaningless, they only need to pass review. If
reviewers are permissive, the output of publications will keep
rising, creating mountains of paper. This aspect of Japanesesociety, and its effect on universities and research institutes,
usually hinders high-quality research. Hayashi grieved overthe state of research in today’s Japan, which ruins superior
researchers and prevents superior research from being carriedout. He felt that the solution to this problem could only befound in the hearts of individuals. And today, when connections
between people have become fragile, there is a world fromwhich we can gaze steadily at reality and elevate the humanspirit. It is not only the world of personal computers, nor theworld of e-books, but the world of ordinary books—pageafter page, filled with writing—and our encounters with them.
Hayashi prayed that the finely honed sensibility capable ofperceiving this reality would arise among young researchers.

102“Kappa Paw” stamp from Hayashi’s collection(Maruyama 2015)

103
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

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Chapter Six

The first Japanese National Character Survey was carried out
by ISM in 1953. I have noted that since then, the survey has
been carried out every five years. When ISM was conducting
the survey at its own expense, it was a process of trial and error,and survey staff had to undergo detailed training. Later, work on
the survey was subcontracted to a variety of survey companies
through competitive bidding.
After the fourth Japanese National Character Survey wascomplete, Chikio Hayashi began to realize that there was aneed for a cross-national comparative attitude survey. A roughidea had been formed regarding the Japanese national characterand the people’s deeply-rooted temperament and values. Now
there was a need for an external perspective on the Japanese.
That is, while Japan was small among the nations of the world,
it had become the world’s second-largest economic power. It
was decided that a cross-national survey, by comparing Japan
with other nations to identify differences, would bring a clearer
picture of the Japanese into relief. But to simply apply thesame metrics to the Germans or the French in one large survey
and compare the results would not be compelling. Unless thesurvey took a detailed approach, the results would lack valuable
insights. Japanese-Americans living in Hawaii were therefore
chosen as the first subject of the cross-national attitude survey.
The survey was conducted in 1971, focused mainly on

Japanese-Americans in Honolulu, since voter registration rolls
were available. Through Yasumasa Kuroda, a professor at the
University of Hawaii who jointly conducted the survey, an
English-language version of the survey questions was preparedby a local expert. The survey results, however, revealed thesame degree of difference between the Japanese in Japan andJapanese-Americans as had been seen previously between
Japanese living in different prefectures of Japan. It was asthough Hawaii were nothing more than another Japanese
prefecture. However, while concepts of tradition and culture
were common among the Japanese, Japanese-Americans living
in Hawaii did not display that tendency. It was here that afoothold for considering differences began to come into view.
The conclusion was that Japanese-Americans had no habit ofconsidering behavior in light of tradition and culture, whereas
for the Japanese, tradition and culture were always top of mind.
In 1978, not only Japanese-Americans but the entire
population of Honolulu was surveyed. Interestingly, the results
indicated the presence of opinion differences between JapaneseAmericans born in Hawaii and those born in Japan. In turn, thedistribution of opinions of Japanese-Americans born in the US
and those born in Japan who immigrated to the US could besummarized and highlighted as a chain. This new approach iscalled Cultural Link Analysis (CLA).
When comparing Americans living on the mainland with theJapanese, with the exception of questions relating to personal
relationships, Quantification Method III analysis indicated

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1. From the Japanese National Character Survey to the
Cross-national Comparative Attitude Survey

Chapter Six

similar attitudes in areas common to highly industrializedsocieties, including the role of trust in human relationships,
terms of employment, attitudes toward work, and attitudes
toward science and civilization. Japanese-Americans
therefore played an important role in the survey, occupying anintermediate position between Japan and the US in terms of
attitude differences.
Today, attitudes in foreign countries are still surveyed forcomparison with those in Japan; these surveys stand on the
results of this first comparative survey of Japanese-Americans in
Hawaii, which was critical in demonstrating how such surveys
could identify differences in attitudes.
As can be seen from the figure, the survey was also importantin forming an understanding of the American temperament. This
investigation of the Japanese and American temperaments was
carried out in sequence: first, Japanese-Americans in Hawaii;then, non-Japanese Americans born in Hawaii; non-JapaneseAmericans born on the mainland; and Americans born on themainland.
Subsequently, the scope of the surveys was widened toinclude the United Kingdom, Germany, France, the Netherlands
and other Western European nations; Thailand and Singapore,
Indonesia, Malaysia, and other countries in the Southeast Asian
region; and China and Korea, among others. There was a clear
difference between national characters in Western Europe andSoutheast Asia, with the basic character of the Japanese being

108

NumberofsurveysJapaneseJapaneseAmericans

Hawaiiborn

NonJapaneseAmericansborn in thecontinental
United States

7

3

2

2

Americans(a) One-dimensional chain

B

A

C

The ﬁgure is two-dimensional, but becomes
multidimensional as the number of groups increases.

(b) Multidimensional chain   109 

Chapter Six

extremely similar to the results found in the latter. At the time,
the US was principally a mixture of peoples from Western
Europe, and exhibited almost the same attitudinal structures asthat found in those countries.
The Japanese people under American occupation barely
studied Japanese history and Southeast Asian culture. Instead,they focused on Western culture, literature, and world history.
I myself did not begin studying the origins of the Japanesepeople, Japanese culture and cinema, and Japanese folk beliefs
until 1980, when I was a student in the US. At the time, Japan’s
economy was growing rapidly and was poised to overtake thatof America. As such, the Japanese were held in extremely highregard in foreign countries.
Today it seems rather odd, but at the time, many Americans
majored in business in order to study Japanese corporations.
It seemed difficult to grasp that the Japanese seniority system,and the loyalty that led employees to stay in one company
for a working lifetime, could be behind Japan’s economic
development. In America, the land where meritocracy reigned
supreme, works like Max Weber’s The Protestant Ethic and theSpirit of Capitalism were held up as proof that those who arecapable and work hard should earn a reasonable profit. For the
weak, and those who fell through the cracks of the system, there
were some charitable groups. Fundamentally speaking, America
had to be a strong nation that scorned the weak, because it was
this attitude that was encouraged by the pioneer spirit. But
eventually America’s economy stagnated and the country fell

into a demoralized mood. This had its effect on Japan as well,because for many years, Japanese society has been guided along
the path of American values and meritocracy, and the weak
have been sacrificed unmercifully. The independent contractor
system and other measures have been introduced, and new
graduates struggle to find employment. With the economic andsocial changes, there have been reports of weird and bizarre
crimes. Such a society would have been unthinkable during theperiod of strong economic growth. It seems that now the pain ofsacrificing the weak has led Americans to yearn for Japan-stylecompany loyalty and job security.
Hayashi had this to say about the survey results.
“With respect to survey questions addressing issues common
to modern society, the Japanese display the same attitudinal
structures as Americans. Europe does not, however, necessarily
always display the same structures. This aspect of Japandemonstrates the astonishing degree to which Japan has been
Americanized. However, Japan and America are poles apartwhen it comes to human relations, with clear differences
between warm Japanese beliefs and dry, impersonal American
beliefs in this area. Europe is between Japan and the US in thisrespect. A tripolar structure is visible that encompasses Japan,
the US, and Europe. We are proceeding our analysis to elucidate
clearly how similarities and differences emerge in each area”(Hayashi 1996).

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Chapter Six

Chikio Hayashi was active on the international scene. His
relationship with Jean-Paul Benzécri of the University of Paris
VI—that eccentric, cult-like figure—bears special mention.
In 1975, Hayashi was astonished by a paper written
by a Frenchman. The paper included a method of patternclassification that closely resembled Quantification Method
Type III. The method of data analysis, called correspondence
analysis (CA), was originated by the statistician Benzécri. At
the time, the majority of such methods had been devised in the
US or UK; Hayashi had not been aware that data analysis was
such an active field in France, much less in Germany, which
had been defeated in the war. Hayashi made inquiries with anumber of well-informed friends, but none was acquainted with
Benzécri.
Around 1970, the multi-volume series Elements ofMathematics by Bourbaki was translated into Japanese fromthe French, to widespread acclaim. Hayashi initially assumed
that “Nicolas Bourbaki” was an individual, and was duly
impressed. In fact, Bourbaki was the pen name of a groupof young mathematicians whose aim was to write a seriesof treatises on mathematics. Apparently, there was a custom
in France in which certain cult-like “gurus” in a wide rangeof fields—for example, in mathematics, the entire range ofmathematics including topology, abstract algebra, and soon—put together a team of disciples, who would produce ahuge amount of work to be published under the guru’s name,while the guru himself was rarely seen. France seemed to bea nation where astonishing jokes were permitted. In the sameway, thought Hayashi, Benzécri rarely showed himself, but his
students must be active under his name. “Guru” has a dubious
ring, but Hayashi referred to Benzécri as such without irony.
In 1978, a Japanese-French joint research seminar was heldat the University of Paris VI under the Japan Society for the
Promotion of Science’s Bilateral Joint Research Projects/
Seminars program, as part of the society’s international
exchange efforts. Attendees from Japan included Kameo
Matsushita, Shigeki Nishihira, Takeshi Hayakawa, Hirotaka
Sakasegawa, Nobuo Inagaki, and Noboru Ohsumi (all fromISM), Yukio Suzuki (University of Tokyo), and Meiko
Sugiyama (NHK Broadcasting Culture Research Institute).
Daniel Dugué led the French participants. Hayashi wasinterested in French probability theory, not mathematicalstatistics, and did not participate in this seminar. However,
because Hayashi was curious about Benzécri, he took thisopportunity and asked Sugiyama to look into his situation; andshe reported back that indeed, Benzécri was a guru-like figure
who rarely appeared in public.
In 1979, the 2nd “Data Analysis and Information Science”
international symposium was held at a hall near the Palace ofVersailles, under the auspices of France’s Institut National deRecherche en Informatique et en Automatique (INRIA).
The invited lecturers were Chikio Hayashi and John Tukey

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2. A leading French classification expert

Chapter Six

from America.
Hayashi learned how data analysis was practiced in France
by attending the symposium. Benzécri appeared before him,smiling and accompanied by his students. The Frenchman was
indeed quite eccentric-looking, with a long white beard and abald head that made him seem like a guru. Impressed by his
elegance, Hayashi recalled that this exceptional statistician
reminded him of a hermit living deep in the mountains, aloof
and proud, or a poet depicting invisible landscapes in words.
When they shook hands, it was as if Hayashi was grasping apale, soft, waxen tentacle of an octopus. Hayashi had requested
an appointment to meet Benzécri before he traveled to France.
The response, written in a halting English, was almost like alove letter, and Hayashi had laughed out loud as he read it.
This is certainly the reason he came to view Benzécri as akind of poet. Hayashi had never encountered anyone like him.
Benzécri’s students—they numbered no less than 200—were
scattered all over the world, and were influential not only inFrance but in Italy, the former Soviet Union, Eastern Europe,
Africa, and Latin America.
Benzécri stood at the pinnacle, hidden away in his library,with his students active widely. Hayashi realized it was asituation that would be unthinkable in Japan.
3. Benzécri’s view of statistics
M. Hill, one of Benzécri’s students, introduced his mentor’s

114

data analysis more widely in Europe and America. Hayashi
learned of Benzécri from reading Hill’s papers, and after
that, Benzécri’s students often requested copies of Hayashi’s
papers. Around 1973, they had informed Benzécri of Hayashi’s
pattern categorization method (Quantification Method Type
III). Consequently, Benzécri must have felt perfectly at easemeeting Hayashi.
It was surprising that Benzécri’s approach was based on
Hayashi’s statistical mathematical ideas. Hayashi was deeply
impressed, and regretted not having paid attention sooner to
French statistical mathematics. That he did not do so may wellbe due to the fact that after the war, Japan was gripped by thedelusion that the world revolved around the US and Britain.
Benzécri’s fundamental stance regarding statistics was
to raise a banner of resistance against the prevailing USand UK-style mathematical statistics. His basic idea that“statistics is not probability” startled Hayashi, who hadstudied probability. According to Benzécri, since 1930, thesophisticated, exhaustive theory of mathematical statistics was
based entirely on probability, and as long as one was caught
in that exhaustive framework, one would never be able toenter the world of analysis of phenomena. Data is messier,and discovering truth amid that messiness is true statisticalmathematics, true statistical analysis rooted in data. It is betterto forget that exhaustive framework. Statistical models do
not come from conjecture but from data. Today’s statisticalanalysis, in which data is forced into exhaustive models, is not

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an entirely accurate approach. Benzécri insisted that statisticalmodels should be inferred from data. Moreover, he preached
the barrenness of statistical testing and lamented the way that
intricate statistical testing theory led one into confusion, until
one’s eyes were swimming. He stressed the importance of
data, saying that testing is not important; what is needed isuseful data that help us construct logical hypotheses. This was
the view of the guru of French-style data analysis. Hayashi
was astonished, and was immediately captivated by thiseccentric-looking individual.
One American statistician shared Benzécri’s view: John
Tukey, who advocated exploratory data analysis. Nevertheless,Hayashi found that the fundamental concept of quantifying
data was absent from the outset. It struck Hayashi that Tukey’s
sense of the essence of data differed from how Hayashi
understood it.

Once the context for Japanese-French collaboration had
been established, Hayashi was eager to invite Benzécri toJapan by any means. However, Benzécri had a rule against
flying. Hayashi suggested he take the Trans-Siberian Railway,
but Benzécri continued to refuse. Hayashi received only
sentimental correspondence via Benzécri’s students. Benzécri’s
stance as a guru in retreat was unshakeable. Hayashi was not somuch astonished as impressed, and he was often to ponder thisdecisive difference between himself and the “guru” in France.
French-style data analysis was eventually translated into
English and quickly achieved recognition, but Japanesequantification theory never achieved the same global exposure.
There were too few students of the method, and Hayashi did
not spread his theory to his students and from them, across theworld, while keeping out of sight himself, as Benzécri did.
Later, at Noboru Ohsumi’s initiative, two of Benzécri’s
students, Maurice Roux and Ludovic Lebart, were invited
to visit Japan as researchers by the Japan Society for the
Promotion of Science (JSPS). The two men traveled around
Japan, lecturing on data analysis as practiced by Benzécri and
Hayashi.
Japanese and French statistical mathematicians, having
become fast friends through data analysis, held a joint seminarat ISM in 1987.
The seminar, which focused on recent developments inclustering and data analysis, was held with support from JSPS,
private enterprise, and other organizations. Nine preeminent
experts in data analysis and clustering gave presentations,
including organizing committee members Hayashi and Ohsumi
representing Japan, and Edwin Diday and Michel Jambu
representing France. With around 180 attendees, the venue
was filled to overflowing, testifying to the degree of attention
data analysis was receiving.
To achieve truly useful data analysis, it is necessary to havea comprehensive understanding of analytic methods as well as

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Chapter Six

the capacity to develop superior statistical software. In addition
to computer literacy, software developers must be thoroughly
conversant with the fundamentals of data analysis. Perhaps
this attitude is a product of the 20th century, when Hayashi
was active. Even with the advanced processing capabilitiesof 21st-century computers, there are numerous restrictions
on data collection, and the capacity to distinguish decent data
usable for analysis is not always possible. There was once an
era when splendid exchanges with international researchers
were frequent; today, data analysis is largely inward-looking
and somewhat stale. Despite the broad propagation and use of
Hayashi’s quantification theory, its fundamental spirit is still
not well understood. In the present era, not only data analysis
but numerous other aspects of statistics are trapped in a cul-desac of confusion and uncertainty, with no clear way forward.

Data science today seems to begin and end with technicalmanipulations aimed at smartification (classification) of big
data. Indeed, with technological advances and revolutions
that facilitate big data information summation leading to thedevelopment of supercomputers and the internet, there is aconspicuous tendency to value indirect human connection
through the internet more than direct dialogue.
Data itself is something that flows back and forth acrossthe internet, not something gathered and handled directly byhuman beings. Methods of extracting data for analysis arecompletely different now. Gathering data in the conventionalway, with rigorous population sampling methods, would likely
require enormous amounts of money and time.
Previously, social and other surveys were carried out withconsideration of sampling, survey design, response error,
survey error, and other factors. Today, conducting this sort ofsocial survey is close to impossible. There is no support forsurveys requiring extensive care.
Ours is an era when the merest remark on Twitter can turn
directly into data and leak, resulting in an unprecedented
flooding of data. Some of those who are swallowed up in
the flood can even commit criminal acts that shock society.
Unintended consequences may lead people to find themselves
designated criminals. These conditions are being skillfully
used to commit an increasing number of unprecedented
crimes. Statistics should be a predictive science that enables
us to prevent such crimes before they happen. Flesh-andblood human beings must be very careful of the negative
legacy that advanced civilization is bearing, and look closely
to ensure that they do not unconsciously fall into the trap. The
fundamental ideas of quantification theory are sounding thewarning we need; this must be understood.
In a broad sense, data mining consists of data analysis
that aims to sort out or categorize data. However, this isperformed using newly developed algorithms that leveragecomputer capabilities; the mediation of computers makes

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5. The importance of data mining methods

Chapter Six

slight but unmistakable differences from conventional data
analysis in technical terms. Data on the internet has developed
dramatically, and it is unclear what form it will assume going
forward.
Ohsumi has stated that one key to future directions of data
analysis may be, for example, the handling of data gathered
through web-based survey methods that have become standard
in marketing research and other fields, as well as the analytic
methods of the huge amounts of text data found in on social
media blogs, Twitter, Facebook, and elsewhere.
Curiosity—the eternal aspiration for the new—is like a
mission that human beings must pursue eternally. Old words
return to the present in new forms, and “data mining” methods,
which have long been with us, seem to have appeared before
us for the first time. Moreover, since the Japanese have aweakness for foreign terms, we use the English term “datamining,” rather than the Japanese term for data analysis,
perhaps creating the illusion that some new and more useful
method has been born. Distinguishing the differences between“data mining” and “data analysis” requires the capacity for
penetrating observation.

upon to have when we conduct surveys and use them. In thatcase, what is it that enables us to have a passion for the data?
Without conviction, passion is impossible. Therefore, to havea passion for data, one must experience it first-hand and be
convinced that data is something truly useful.”(Chikio Hayashi, Deeta no kagaku [Data science], Asakura
Publishing Co., 2001)“Social surveys are one method for understanding society.
They are truly broad and deep in scope. Data theory forms thefoundation of surveying. Actual surveys validate and utilize
this theory, but the theory must be applied by none other thanhuman beings. A passion for the data is what we are called

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Chapter Seven

1. Final life work
Chikio Hayashi retired in 1986, after forty years at ISM
and a professional life spent devoted to research. For the next
three years, he lectured in data analysis at the Open Universityof Japan. Following this he set up an office for research in anapartment building in the Sakuragaoka district of Shibuya City,
Tokyo. There he worked vigorously on numerous cross-national
comparative research surveys, including surveys of Japaneseimmigrants in Brazil and on the west coast of the US. He also
hosted a morning research seminar roughly every month foran adoring circle of researchers. There was also the “Thursday
Association,” an evening study meeting attended by numerous
practitioners and researchers. This too was originally held
approximately every month, but the number of attendees kept
rising, and finally the meetings were moved from his office
to a room at ISM. These meetings were hosted by Masakatsu
Murakami of ISM, with enthusiastic participation from Ryozo
Yoshino. Both men provided Hayashi with thorough support.
Also at that time, Shuichi Iwatsubo, an assistant professor
at the National Center for University Entrance Examinations,and other young colleagues and associates were holding
study meetings at Hayashi’s office in Shibuya. Because many
attendees were from the Center, issues relating to university
entrance examinations were a frequent topic. Themes frommany other fields were also subjects of discussion, andHayashi participated from beginning to end with just as much

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enthusiasm as the young researchers. When time permitted, thegroup would share a meal of tempura at Tenmatsu, a restaurantin the neighborhood, and afterward return for more animateddiscussion. Eventually, Hayashi’s research room became known
as “Hayashi’s cram school.”
2. Promoting international research exchange
In 1983, the study meetings on classification theory and its
applications became the Japanese Classification Society, withHayashi as its first president. The society placed particularemphasis on international exchanges, built close ties with theforemost members of classification societies in France andFrench-speaking Belgium, and promoted information exchange.
Hayashi was eager to create an international classificationsociety in Japan as well. Based on pertinacious, passionate
discussions with the principal members of classificationsocieties in West Germany, the UK, Italy, the Netherlands,
Poland, Portugal, France, Belgium, the US, Canada, and othernations, as well as Slovenia, which at the time was an associate
member nation, the International Federation of Classification
Societies (IFCS) was at last established.
The first international IFCS meeting was held in 1987 at
RWTH Aachen University in West Germany, the same year thatHayashi stepped down as ISM director-general. The fifth IFCS
meeting was held in 1996 at the Kobe International ConferenceCenter in Japan, and as chair of the organizing committee,

125Chapter Seven

Hayashi worked with furious energy. His data science-relatedpresentation at the meeting was an extremely important one. He
summarized the fundamental concepts of modern data scienceand traced the path of its development from data analysis toscience. Already around this time, Hayashi was using the term
deeta no kagaku (“data science”).
Starting in 1998 and for the next two years, Hayashi served
as president of IFCS. Noboru Ohsumi and Yasuo Baba, bothfrom ISM, worked to obtain the needed funding. Both men wereburning with passion to spread recognition of the importanceof the new developments and directions that awaited data
science in the future, as well as the role and importance of theclassification concept, and they provided close assistance toHayashi.
There is another important matter that should be remembered.
Before and after he assumed his post as IFCS president,
Hayashi established the Travel Award Program (TAP) for young
researchers. He served as chair of the TAP committee, which
endeavored to offset a portion of young researchers’ researchexpenses. Despite the severe challenges facing this effort toprovide partial funding in place of the government, Hayashi
considered it his mission, based on his own experience as ayoung researcher studying abroad. These grants of aid were later
dubbed “Hayashi Prizes.”
Hayashi had given thought to somehow creating a Chikio
Hayashi Prize during his lifetime. He was eager to subsidize
part of the research expenses for outstanding young researchers,

so they could do research in directions of interest to him. This
“Hayashi Prize” would be different from the Chikio Hayashi
Prize awarded by the Behaviormetric Society, in that it would
provide assistance for travel expenses, and was a technicalincentive award perfectly befitting the IFCS.
The Behaviormetric Society had administered an achievement
prize from the beginning, but after Hayashi died, it was renamed
the Hayashi Chikio Award (Achievement Award / ExcellenceAward). The society’s first Hayashi Chikio Award (Achievement
Award, 1986) went to Yoshio Takane of McGill University in
Canada. Since before the society had been founded, Takane had
been a participant in the Multivariate Analysis Study Group,
where he had made a presentation on the massive tome on threemode factor analysis by Tucker of the University of Illinois.
That had been at the height of the student movement and theuniversities were closed. Despite the situation where students
could only either participate in the student movement or takea noncommittal attitude, Takane was burning with academic
fervor.
After that, it established numerous other awards, including
the Hidano Tadashi & Mizuno Kinji Award (Encouragement
Award) in 1999, and a publication award (Sugiyama MeikoAward) in 2011. The society adopted a wide range of measuresto accelerate the appetite of young researchers for pure research.
After Hayashi retired from ISM, he engaged in large amounts
of international information exchange. He set up an office in
Shibuya and made frequent trips abroad in order to actively

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Chapter Seven

Three months before the events of September 11, 2001, andfor no particular reason, Hayashi was seized with feelings ofnostalgia. Thinking deeply about the past and the future, hefound himself considering carefully the systematization of data
science, which he was intending to promote. This was without adoubt a magnificent conception, one that ordinary persons could
not grasp.
Deeta no kagaku, published in 2001, discusses the subject of
accuracy rates extensively. Hayashi’s blood-stained experienceduring the war had convinced him of the effectiveness of data.
After the war, that experience became the motive force for a
lifetime devoted to researching data. This was not something
he was taught by others. It was something cultivated by thejoy of guessing right and the sorrow of guessing wrong. Data
science was possible because it was backed by data theory.
Discovering the concepts behind Hayashi’s data theory requires

a close reading of Deeta no kagaku and an understanding of
its theoretical methods. In addition, to seek and extract further
truths from data that has already been analyzed, one must makeextensive use of data mining techniques, look at the data from ahigher perspective, and mine the vein of gold to be found there.
This will be a task for those he left behind. Hayashi’s sincere
wish was for young researchers, who occupy a place at the heartof modern society, in which diverse theories relating to data
circulate, to share their ideas with each other, and build a moreexhaustive theory of data from data science.
Hayashi was to give the keynote lecture at the 29th annual
meeting of the Behaviormetric Society, held at over four days
at Koshien University from September 13, 2001. But he stayed
up all night watching reports of the terror attacks of September
11; the next day, he became ill and had to be hospitalized. His
wife called the society and told them he would have to cancel.
Hayashi had never missed a lecture under any circumstances,
but in this instance, he had no choice.
On May 11, 2002, to make up for missing his keynote lecture,
he spoke at a Behaviormetric Society symposium held at ISM.
After an introduction from Iwatsubo, Hayashi gave a lecture thatwas full of fighting spirit. Based on his grand vision, he lectured
on the systematization of data science.
He stood at the lectern with a plastic bottle of water in his
hand, which was something unprecedented with him, and hebegan the lecture with an embarrassed chuckle.
“What’s different from last year is that I have this [bottle of

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propagate his research activities, and his quantification theory
in particular—a plan that had been difficult for him to carry out
during his time at ISM. But now, Chikio Hayashi’s name had
started spreading around the world.
His last trip abroad, accompanied by his wife Reiko, was inNovember 2000, to Germany. After his return, he wrote andpublished Deeta no kagaku (Data science). It was his final
single-authorship work.
3. Last message

Chapter Seven

water]. The reason is that after I left the hospital, I spoke at a
medical conference and my doctor happened to be there. ‘If you
don’t drink plenty of water,’ he told me, ‘it’s bad for your heart.
So, you should drink as you lecture.’” (Lecture record, 2003)
He did exactly as the doctor recommended. For a physician,he was the perfect patient; he had respected specialist
knowledge in others strongly.
Before a month had passed, his condition changed suddenly,
and he was readmitted to the Department of Cardiovascular
Medicine at Nippon Medical School Hospital. His physician
was a student of his old acquaintance, Eiichi Kimura, a leading
authority in electrocardiology who had served as the presidentof Nippon Medical School. Kimura was no longer alive, but his
pupils had learned much from Hayashi concerning data analysis.
His initial exam upon admission was overseen by Hirokazu
Hayakawa, Kimura’s foremost student, and for two months
Hayakawa and his own students cared for Hayashi.
Hayashi’s heart condition had been worsening, but was
not yet able to extinguish the fire of life that burned withinhim. Hayashi, indifferent to it, was making final corrections
to the manuscript of his last work, Suji ga akasu nihonjin no senzairyoku (Numbers reveal the hidden strength of
the Japanese), dealing with the true nature of the Japanese
as demonstrated by fifty years of national character survey
data (published in 2002), so that the writing would be easily
accessible to all. The book is a comprehensive summary of
50 years of Japanese National Character surveys and presents

Hayashi’s final theories on the Japanese people as extracted
from those surveys.
After the defeat in World War II, the spiritual strength of theJapanese, who had never yet experienced defeat, was tested asnever before. How did they overcome the long testing periodof the postwar era? Hayashi attempted to demonstrate thefundamental strength of Japan’s people, or as he put it, theirhidden capabilities, as shown by the survey data.
After the war, the Japanese labored like worker bees, pushingdevelopment forward until Japan became the world’s number
two economic power. But the economic bubble collapsed in theearly 1990s, and there were many who yearned for Japanesetraditions of diligence and frugality. For those born before thewar, this national character was taken for granted, but since
the end of the war, people had been caught up in living like theproverbial grasshopper. Everyone agreed it was time to startliving like the ant, and there were many who were willing towork hard to restore the fundamental character of the Japanese.
But for some middle-aged and younger people born after thewar, the post-bubble times were difficult to cope with, andJapan’s suicide rate rose to the third-highest in the world.
Moreover, the nation was totally at a loss faced by a daunting
array of political, economic, and diplomatic challenges. The
data Hayashi presented was his attempt to provide a spiritualprescription for the Japanese. It was a work of hope, almost
amounting to a prayer.
Furthermore, Hayashi invested particular effort in studying

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Japanese religious piety. Japanese traveling abroad are often
asked what religion they adhere to. Most will say they do notbelieve in any religion. Some foreigners can only shake theirheads dubiously and assume that the Japanese are atheists or
uncultured. But on this point, the data demonstrate that Japanesereligious piety is truly profound. For many non-Japanese,
religion is a matter of belief. According to Hayashi, the Japanese
have spiritual leanings that are both subtle and natural, which
they possess without being taught.
Whenever Hayashi traveled outside Japan, there wassomething he pondered. For instance, believers in Christianity
say a prayer before each meal; but this must be the result of
religious training. On the other hand, before each meal, theJapanese will say “itadakimasu (I receive [this food])” andplace their palms together. This is the artless spirituality that isstrongly rooted in the character of Japan’s people. Moreover,though religious feelings tend to strengthen with age, it is
not uncommon to hear that most Japanese seem not to havenoticed that they are religious by nature. “The Japanese areoften said to lack a religious sense, but they are unique among
the peoples of the world for continuing to retain such feelings
without any religious education” (Hayashi and Sakuraba,
2002). Using analysis of the data from the Japanese National
Character Surveys and the Cross-national Comparative Attitude
Surveys, Hayashi penned a heartfelt spiritual prescription for
the Japanese of today—a guide showing what they needed to do
to recover from an almost complete loss of vigor—and to takethe initiative to enhance international mutual understanding.
Hayashi’s wish—and he left numerous suggestions as to how
this might be accomplished—was for the Japanese to flourish
in the 21st century by working for international harmony
while valuing their unique qualities, and realizing that froma historical perspective, even with the ordeals of defeat andoccupation, few nations had been as fortunate as Japan.
But his time had run out. Even in the intensive care unit,
his mind was fully sharp enough to correct the manuscript,
but whether or not he was aware that his body was failing is
uncertain.

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4. The idea and future of data science
While the significance of data science has been a constantrefrain, we may roughly summarize it as follows.
Statistics focuses on the collection, organization, analysis ofnumerical data, as well as drawing conclusions. In particular,
the following points are important for the systematic analysis of
data: sampling theory, which deals with the organization of data,and sampling theory’s survey metrics of universe (the set of all
experimental units), population, and sample, or UPS.
Inferential statistical theory initially retained its own scientificadequacy. However, as the theory progressed, it became
more mathematically detailed and less grounded in reality.
An identical tendency can be seen in the field of experimental
design methods. Even the fundamental UPS concept is often

Chapter Seven

ignored. As such, advanced mathematical statistics (thestudy of statistics from a mathematical perspective) became
progressively irrelevant to the elucidation of phenomena.
Furthermore, some researchers applied the theory without
understanding of how data should be organized; others used
calculations based on hastily collected data, and tried to fit thedata to a simple conventional model. Those without access todata often used the computer-simulated (artificial) data withsimple structures to explore the nature of existing theories.
That their data analysis methods were inappropriate is obvious,
resting as they did on the assumption that the mechanism thatgave rise to the data was unknown. Some good predictions
were based on incorrect assumptions. Software development isessential to encourage the propagation of data analysis to otherdomains, but is not always useful for the development of new
efficient methodologies and theories appropriate to the studied
phenomena.
What, then, is to be done? New paradigms are needed. The“data mining” paradigm is like that of “soft sciences” and “lifesciences”—the scope was already present, but the paradigm
is new. By creating new paradigms, things that were invisible
under existing paradigms become visible. I believe that new
scholarly directions and developments require new paradigms.
Data scientists must cope with the oncoming data flood bygrasping fully the meaning of what Chikio Hayashi asserted
and envisioned. The propagation of the internet and computer
infrastructure will cause an unimaginable flood of data, andsociety’s expectations of data science are growing. The worldis waiting for the elaboration of fundamental concepts and thephilosophical ideas relating to statistical analysis that will meetthose expectations. In a sense, a restructuring of the overall
concept of data is probably necessary. To achieve this, therole of data science will likely be essential in an increasingly
complex society. We must think deeply about these conditions
and work diligently and patiently. Chikio Hayashi yearned forresearchers with the abilities to provide an integrated, unified
view of theory and practice, in order to achieve knowledge inthe constantly evolving data science world.
“To every thing there is a season, and a time to every purpose
under the heaven: a time to be born, and a time to die,” (KingJames Version of the Bible, Ecclesiastes 3:1-2) and in the fullnessof time, what is needed will come into being. Today’s data
flood—Big Data—will ultimately pass, in the infinite universe.

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5. We will surely meet again in the Pure Land
Hayashi passed away at 8:00 a.m. on August 6, 2002, on
the same date as Joichi Suetsuna had died 32 years before. He
was 84 years old. Nine days later, on August 15, his final work,
Suji ga akasu nihon-jin no senzairyoku, was published. His coauthor was Masafumi Sakuraba, who had strongly urged him
to write it. Fifteen days after Hayashi died, on August 21, theevening edition of the Asahi Shimbun newspaper commented
that the book was his final testament.

Chapter Seven

The then-director-general of ISM, Genshiro Kitagawa, penned
a tribute to Professor Chikio Hayashi that was published inVol. 50 of Tokei suri (Proceedings of the Institute of Statistical
Mathematics), ISM’s official publication.
“[Dr. Hayashi] was a constant advocate for data science,and I heard that just before his passing, he was hard at workon a book. Despite the fact that he was singlehandedly active
as a researcher in an incredibly wide range of areas, to die
before completing his deployment of a new concept can only
be regarded as truly regrettable… Throughout, he promoted
the analysis of data based on an investigative standpoint,
originating and practicing the research style that became thetradition of ISM. Against the traditional scientific methodology
of understanding phenomena through theory, he insistedon doing so through data. That stance was embodied inquantification theory, behaviormetrics, and data science. If I
may summarize his approach without fear of misstatement, I
would say that it favored data over theory, quantified expressionof collective characteristics over cause-and-effect relationships,
macroanalysis and an integrated view over microanalysis,
and hypothesis discovery over hypothesis and verification.
As the realization of a stance that emphasizes real problems
in statistical science, two currents came into being at ISM:
investigative data analysis and statistical modeling. With
his leadership in data analysis and data science methods, hedemonstrated particular influence with respect to complex, hardto-quantify subjects of research such as national attitude, andcontributed to a dramatic expansion in the scope of statisticalscience and applied fields.”
Chikio Hayashi was laid to rest in the cemetery at Keifukuji,a Zen Buddhist temple deep in the mountains in HinodeTown, Nishitama District, Tokyo. Instead of the conventional
inscription, “Hayashi Family Grave,” the tombstone is inscribedwith four Chinese characters—a line from the Sukhavati sutrathat means “We will surely meet again in the Pure Land,” inHayashi’s own calligraphy. Each year in early August, Hayashi’s
beloved students gather in that cemetery deep in the mountains,take photographs before the grave, and observe a moment ofsilence before retiring to Kurochaya, a nearby Japanese-stylerestaurant, to exchange memories of Chikio Hayashi in bygone
days. Might Hayashi perhaps be there next to us, watching thesescenes of reunion? One is reminded of how he would often
encourage us, in his openhearted, placid manner, saying, “Hey,hey, right there. Here’s how you do it. Just like that.”“Just as the fire in my soul will never be extinguished, may
you too walk the same path.”
We listen carefully for the whispers of Chikio Hayashi’s soulin our ears. Under the midsummer sun, in a tranquil setting
where the only sound is the distant calling of cicadas, we sharea brief interlude over lunch. Somehow it has become a tradition.
There we tell each other of the big problems we are facing,
discuss them, share opinions, and then in twos and threes,
followed by the clamoring of the cicadas, we make our way
home.

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Epilogue

The litmus test for a statistician seeking to achieve excellenceis the penetrating insight, and the clearly articulated supporting
ideas and philosophy, that are required to discern truth in hugeamounts of social survey data.
This is why Chikio Hayashi states repeatedly, in Deeta no
kagaku (Data science), that simply running data through a
computer will miss the essence it contains, and lead to incorrectconclusions. Hayashi was a statistical mathematician of global
importance who trained himself to develop the insight andsensibility needed to see through the data to its true essence; his
focus was always to grasp the phenomena hidden in the data,
to discover hypotheses from the data. We have traced how hecultivated this sense by examining his development, the era ofsocial upheaval during which it took place, and his response tothat era, as well as his ideas concerning data. Hayashi spent half
his life at the Institute of Statistical Mathematics (ISM), where
he was known for his responsible, patient, and optimistic nature,and he stimulated and educated numerous young researchers. He
had a kind of aura that attracted numerous scientists, marketing
professionals, journalists, and others, who would make many
contributions to society under Hayashi’s influence.
It is rare for individuals to be faced with complex data,
wrack their brains and agonize sincerely, burn with passion,and respond with joy when arriving at the truth. Just as Hayashi
himself was this sort of individual, all of his students were140

baptized and trained in the same way.
When faced with a large data set, Hayashi would rub hishands with anticipation, gaze at the pages of data lists likea puzzle, and exclaim that examining data was his greatest
pleasure. He would then settle gradually into a state of deep
contemplation, allowing his mind to roam freely and consider
all possible ways to capture the truth hidden in the data.
What one might call the “twilight years” never applied
to Chikio Hayashi. If they did, it was only a period after his
hospitalization on September 14, 2001, and the cancellation of
his keynote address.
As stated previously, in 2001, when Hayashi was designateda symposiast of the 29th annual meeting of the BehaviormetricSociety, which was held at Koshien University, he experienced
chest pains on the way to the conference, and went to thehospital for an examination, which uncovered a heart defectrequiring immediate hospitalization. The meeting was throwninto a state of extreme confusion, but managed to continue.
Hayashi was probably overwhelmed by the events of September
11, 2001. His own battle with illness began. He managed torecover quickly and gave lecture after lecture. He worked
tirelessly with Masafumi Sakuraba to compile the definitive
statement on the Japanese national character into a single workthat was easy for anyone to grasp. His illness assailed him, yeteven in the intensive care unit, he calmly worked to correct the141manuscript. But in the end, this intellectual giant exhausted his
strength, and at the age of 84, at the height of summer, his life
drew to a close. The cause was multiple organ failure.
A single body with a superior brain, buried and done with.
We will surely never again encounter such originality. If an
individual with a singular uniqueness identical to that of Chikio
Hayashi ever appears again, it will likely take an unimaginable
number of years, and then be a special dispensation, a genuine
gift from heaven.
But we will not be so blessed. We can only watch, dazed, as
Hayashi’s soul disappears into an unimaginable distance, likewild dandelion seeds borne away by a strong gust of wind. The
potential of the Hayashi’s ideas has not been exhausted yet.

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Acknowledgments
Haruo Yanai, my comrade-in-arms during our younger
days when we shared the travails and joys of founding the
Behaviormetric Society, and who urged me strongly to publish
this book, passed away in December 2013. I pray for the reposeof his soul and dedicate this book to him.
I received encouragement and assistance from many
individuals during the writing of this book. I would particularly
like to thank my colleagues at the Behaviormetric Society for
their kind advice. I would also like to thank my good friend
Shuichi Iwatsubo (Professor, Waseda University), who readthe draft, and Kazufumi Manabe (Professor, Aoyama Gakuin
University), who was generous in providing documents. I would
like to express my gratitude to Kazuhisa Takemura (Professor,
Waseda University), Fumi Hayashi (Professor, Toyo Eiwa
University), and Noboru Ohsumi (Professor, ISM).
This text draws on Hayashi Chikio no shogai: deeta saiensu
no kaitakusha ga mezashita mono (The life of Chikio Hayashi:
What the founder of data science pursued), published in 2015
by Shin-yo-sha, and was prepared specifically for publication in
English.
I would like to express my heartfelt thanks to Yasuto Yoshizoe(Professor, Aoyama Gakuin University) for his tireless efforts
with respect to publication of the English version.

143

I want to thank Ryozo Yoshino for his insightful perspective
and commentary (Professor, The Graduate University for
Advanced Studies, ISM).
I am extraordinarily grateful to Tohoku University, theBehaviormetric Society, and Dr. Lubov V. Kopenkina for
their very important pictures contained in this book. I am
enormously indebted to members of Shigematsu Team for theirprofessionalism and efficiency. I would like to express my very
deep appreciation to all who have been involved in the process.
I would like to thank Takako Sinzi for her invaluable practicalhelp, sharing thoughts and encouragement during the work,and Dr. V. Kopenkin for helpful comments, advice, and usefulsuggestions.
The author and publishers would like to thank Professor
Yadolah Dodge of the University of Neuchâtel in Switzerland
for permission to use the image "Chikio Hayashi writes on Data
Science" in part of the cover art work.
I am also particularly grateful to Masato Aida and members of
the Statistical Information Institute for Consulting and Analysis
for their invaluable help, support, and encouragement.

Appendix

Kumiko Maruyama
March 2021

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Appendix

Chikio Hayashi’s way of thinking
It is no secret that the past has lessons for the present.
Throughout his long career, Hayashi used flow charts andschematic views to illustrate and visualize his way of thinking.
New perceptions of the same phenomenon are always possible.
For instance, a series of images can tell a story.
Hayashi clearly followed his own philosophy in his work.
The intention was not to define every term, but rather tolook at them through the perspective of his thought process(multidimensional, dynamic and flexible). Hayashi's work
led to the development of many methodologies and analyticaltechniques. Many scientists felt that his unique style wasdifferent from traditional approaches. Some researchers did
not share the same understanding of the terms and concepts.
There was also a barrier of language. Hayashi considered Data
Science as a new concept. It combines statistics, data analysis,
their related methods, models and techniques. According toHayashi, it also includes its results (related to theory, method,
methodology, informed decisions and testable predictions). The
goal is to understand actual phenomena through data.

He believed that even if there is no underlying theory, thehidden structure (pattern and dynamics) of natural, human andsocial phenomena can be determined by data analysis.
The behavior of objects is complex and multi-scale in nature.
The exact mechanisms or models for a given phenomenon
are not always clear. This does not mean that he did not trust
the theories of the past. Scientists who advocate the scientificmethod need scientific wisdom to withdraw the theories they
have believed in and create new ones.
In his analysis, Hayashi goes back to the starting point manytimes. His process is fluid and capricious.
Make and break, rethink and break. In other words, his
approach tries to produce accurate and testable understandingof the phenomena without constraints of traditional (formal
modeling or hypothesis testing) approach. If the given data set
is not enough, one can repeat the process, expand the data set,
fill in gaps in the data.
This data-based approach can open new research possibilities,
widen the scope of studied phenomena, and lead to thedevelopment of new theories and methodologies.

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Appendix
Population
EconomySociety
Organism
Human race

Descriptive
statistics

Actuarial
science

Theory of games
Statistical decision

Probability theory,
Bayesian inference

Agriculture
Organism

FisherDesign ofexperimentIndustry
Sociology

NeymanEstimate / Test /Mathematicalstatistics

Inductive
Statistics(in a broad sense)

Mathematicalstatistics(Narrow meaning)

SpecimensurveyScience andengineering
VariousModelselection
AIC / BAIC

Data analysis
Scaling, NDS
Analyse des données(Correspondence analysis)

Exploratory data analysis
classiﬁcation
Behavioral metrology Flow of statistical thinking

Closely
relatedComputing statistics(including computer-basedgraphics)

        
               

148

149

Current
situation

Appendix

   



        

Diversiﬁcation
Find and consider deviations(individual points, class,structure)

Multifariousness

Structure-ﬁnding
Conceptualization

Classiﬁcation
Multidimensional
data analysis
Variety of statisticalmethods         

Simpliﬁcation   


      


             
        


   

      

150

151

Appendix

The arrows between the levels represent
the cyclic nature of the process

Level of wisdom

Level of knowledge

Specialization

Generalization

Problem solving

Abstraction, induction of ideas,
development of theories andmethodologies

Informationacquisition
From an objective
point of view,
it is called informationtransmission.



Data science and the expanding understanding   
   
  
       

152



  153Chronology

Chronology

1918
Birth
June 7: Born as the fourth son of Ryuichi and Chiyo Hayashi in Komagome,
Hongo Ward, Tokyo City (today’s Bunkyo City, Tokyo Metropolis).
The family moves to Higashi Nakano 4-chome, Nakano Ward.
November 11: End of World War I.
1923
Age 5
September 1: The Great Kanto Earthquake.
1925
Age 7
April: Enrolls in local elementary school.
1931
Age 13
April: Enrolls in Tokyo Kaisei Middle School.
1932
Age 14
Reads Variété by Paul Valéry (translated by Masaaki Sato and Kenzo
Nakajima, Hakusuisha Publishing Co., Ltd.) and is deeply moved.
1933
Age 15
His essay titled “The Pleasures of Reading” is selected for publication in theschool’s bulletin Wakaki kokoro (Young minds), Showa-period edition.
1936
Age 18
April: Enrolls in Department of Science, Seijo High School
December 20: Father, Ryuichi, passes away after illness.
Oldest brother, Hiroaki, takes over management of Ryuichi’s steel company,
and is joined by younger brothers Fumihito and Naoji.
Hiroaki raises Chikio like a father.
1937
Age 19July 7: The Second Sino-Japanese War breaks out.

154

1939
Age 21
April: Enrolls in the Department of Mathematics, Faculty of Science, Tokyo
Imperial University, majoring in probability, with Prof. Soichi Kakeya as hisacademic advisor.
September 1: Nazi Germany invades Poland, leading to the outbreak of WorldWar II.
1940
Age 22
Japan signs Tripartite Pact with Germany and Italy.
1941
Age 23
November 26: The Japanese task force sets out for Hawaii.
December 8: Japan declares war on the U.S. and Britain; Pacific War breaks out.
1942
Age 24
October 20: After graduating ahead of schedule from Tokyo Imperial
University, becomes a technical cadet at the Imperial Japanese Army’s Mito
Flying School.
1943
Age 25February 20: Graduates and becomes a first lieutenant (aeronautics) assigned
to the army’s air service headquarters.
1944
Age 26
October 25: Divine Wind Special Attack Unit makes first suicide attacks at
Leyte Island.
December 7: Tonankai Earthquake and tsunami.
1945
Age 27
March 10: Firebombing of Tokyo; the family home in Higashi Nakano is
destroyed; he moves to a rented house in Meidaimae.
April 8: Assigned to Imperial Army Air Service Headquarters.
August 6: Atomic bomb dropped on Hiroshima.
August 9: Atomic bomb dropped on Nagasaki.
August 15: Radio broadcast of Emperor’s announcement of surrender.
August 20: Promoted to captain (technical).
September 2: Japan signs articles of surrender.
October 15: Disarmament completed.
December 28: Begins work as Research Assistant at Faculty of Science, Tokyo
Imperial University. Begins practicing the flute on his own.

155

Chronology
1946
Age 28December 16: Joins the Institute of Statistical Mathematics (ISM) as Technical
Officer, Ministry of Education.
1947
Age 29January: Death from pneumonia of Soichi Kakeya, the first director-general of
ISM.
May: University of Tokyo Prof. Joichi Suetsuna is appointed as the second
ISM director-general (concurrent).
1948
Age 30April: Suetsuna resigns as ISM director-general, to be replaced for the time
being by Toshio Kitagawa.
August: Participates in National Literacy Survey; develops sampling andQuantification Method Type I (to 1951). Begins providing guidance to Asahi
Shimbun opinion survey department, participates in election forecasting.
December 15: Ministry of Education instruction officer (concurrent).
Begins parole research and development of quantification theory. Develops
Quantification Method Type II (to 1950).
1949
Age 31
November 17: Tadahiko Kubota is appointed as the third ISM director-general.
December 15: Ministry of Education instruction officer (concurrent), theresearch and information section, National Institute for Educational Research(to 1953).
1951
Age 33June 1: Director, ISM Research Dept. 3 (statistical theories for the social
sciences).
1952
Age 34
Participates in research group with Hajime Ikeuchi, Yoshitaka Umeoka, Taro
Indo, and others; encounters the Guttman scale. Expands Quantification
Method Type II to its current form during a survey of attitudes toward
American and French culture; develops Quantification Method Type III during
a survey of National Railway line maintenance personnel.
September 1: Tatsujiro Sasaki is appointed as the fourth ISM director-general.
General-purpose relay computer capable of generating tables of random
numbers is developed.

156

1953
Age 35
August: Ministry of Education instruction officer. Begins National Character
Survey research.
Family residence in Higashi Nakano is rebuilt; his older brother and his
family settle there, while Hayashi moves with his mother and older sister to
Mitakadai, Musashino City.
1954
Age 36
October: Receives Ouchi Prize (the most prestigious award in the field ofstatistics in Japan).
1955
Age 37January: “Research on Quantification Theory in Psychology” (edited by Sadaji
Takagi) is published.
April: Marries Reiko Ohashi of the Ohashi zaibatsu (co-founder of Kurashiki
Spinning Works [currently Kurabo Industries]). Takes up residence in a newhome next to that of his mother and sister in Mitakadai.
September: Director, ISM Research Dept. (natural and social science theories).
1956
Age 38January 20–May 15 (116 days): Dispatched to University of London as aforeign research student. Before arriving in London, visits Hebrew Universityof Jerusalem and the Israeli government’s social policy research institute.
Attempts to meet Guttman, but the latter is absent; visits Istanbul University,where Richard von Mises and Hans Reichenbach had taught after fleeing
the Nazis, before proceeding to London. On his way back to Japan, stops by
Sweden, then on to America where he meets Guttman. After a three-monthstudy stay, returns to Japan, where his pregnant wife is waiting. His first child
Sachio is born in March; in June, he receives his doctorate in science from the
University of Tokyo. His thesis is entitled “Statistical Mathematical Methods
for Qualitative Quantification.”
1957
Age 39Development of Quantification Method Type III (during canned goods
label design research at the request of Keinosuke Sato); carries out Asahi
Operations Research (AOR) mass media contact research; begins joint ECG
analysis research with Eiichi Kimura of Nippon Medical School Cardiology
Department.
October 18: Tatsujiro Sasaki takes leave of absence from ISM due to illness.
December: Birth of second son, Yukikazu.

157

Chronology
1958
Age 40April: University of Tokyo Professor Joichi Suetsuna is appointed to serve
concurrently as ISM director-general.
1959
Age 41March 31: Joichi Suetsuna is appointed as the fifth ISM director-general.

1970
Age 52
Member, NHK Broadcasting Culture Public Opinion Research Committee (to1986).
August 6: Death of Joichi Suetsuna.
September 1: Louis Guttman and his wife visit Japan.

1963
Age 45
March 30: Opening of ISM computer room.
Member, NHK Broadcasting Culture Research Institute (to 1969).

1971
Age 53February: Technical advisor, Science Council.
In anticipation of first research survey of Japanese-Americans in Hawaii forcross-national comparative character survey, discovers viable approach using
Quantification Method Type III (pattern classification); realizes effectivenessof the method.
March 1: Prof. Yoshitaka Kawada, Faculty of Science, the University of Tokyo
is appointed as the sixth ISM director-general (concurrent).

1964
Age 46
Publishes Shijo-chosa no keikaku to jissai (Market survey planning andpractice). Quantification theory-based computer program is developed andsold to Morinaga and NHK.

1972
Age 54
February: Film production of Nousagi wo kazoeru (Counting wild hares)(NHK).
President, Japan Association for Philosophy of Science (to 1984).

1965
Age 47Chair, Japan Association for Forestry Statistics (to 1984).
Begins wild hare population survey.
March: Receives NHK Broadcast Culture Prize.
October: Undergoes operation for glaucoma.

1973
Age 55President, Japan Color Research Institute (lifetime).
Founding of the Behaviormetric Society of Japan, Hayashi becomes the firstchairman (to 1988).

1962
Age 44
Participates in research relating to circulatory organs and medical
examinations.

1966
Age 48
July: Domestic survey researcher, Small and Medium Enterprise Agency (one term).
Japanese Society for Hares founded.
1968
Age 50Moves to temporary offices due to new ISM building construction.
June 6: Death of mother, Chiyo.
1969
Age 51March: Technical advisor, Science Council.
July: Member, Health and Welfare Statistics Council, Ministry of Health andWelfare (to 1971).
October 30: Moves to new ISM building.

158

1974
Age 56
February: Death of elder sister, Yoshiko.
March 1: Becomes ISM director-general (to 1986).
1975
Age 57January: Japanese Language Council member (to 1981).
US-Japan seminar on multidimensional scaling analysis and related methods.
Director, Japan Marketing Association.
October: The ISM Research Dept. 6 (statistical theory of behavior) is
instituted.
December 1–9: Trip to Philippines.
Observes cultural friction in the South East Asia region.

159

Chronology
1976
Age 58British mathematical statistician D. J. Finney (1917-2018) visits Japan.
Honorary Fellow, Royal Statistical Society (UK).
Technical advisor, epidemiological statistics (to 1977).
Multidimensional scaling research on the basic attitudinal structure of theJapanese people (the “bogey” survey) (to 1978).

1982
Age 64
February: Technical advisor, Science Council (Science Research Budget
Subcommittee) (to 1986).
March: Member, Japanese Language Council (to 1984).
September: Member, History and Philosophy of Science Research
Coordinating Committee (to 1987).

1977
Age 59March 9–22: Trip to the United States.
April: Member, Japanese Language Council (to 1979).
Chair, Arteriosclerosis Prevention Research Association (lifetime).

1983
Age 65President, Japanese Classification Society (to 1986, 1991–1992, 1992–1999).

1978
Age 60
October 26–November 11: Trip to Federal Republic of Germany.
1979
Age 61February: Chair of Prime Minister Masayoshi Ohira’s social policy researchgroup to investigate people’s interest concerning life in diversified society (to1980).
March: Member, Japanese Language Council member (to 1986).
June: Director, Japan Marketing Association (executive director from 1986)(lifetime).
October 15–26: Trip to France and Belgium. Meets French statistician JeanPaul Benzécri.
Chair, Japanese Society for Hares (becomes the Japanese Wildlife ResearchSociety in 1991).
Plays flute for the Atago Concert (to June 2002).
November: Completion of Information Statistics Research Building.
1980
Age 62President, Biometric Society of Japan (to 1986).
Director, International Biometric Society, Japan Chapter (to 1986).
Computer Operations Committee is formed.
1981
Age 63
June: Member, Statistics Research Liaison Committee, Science Council ofJapan (to 1987).
November: Member of the Board of Trustees, National Institute for
Educational Research (to 1985).
Receives Japan Medal of Honor with Purple Ribbon.

160

1984
Age 66
April: Visiting professor, Open University of Japan (one year).
Chosa no kagaku (Science of survey), Kodansha Bluebacks.
The International Biometric Conference held in Japan.
Switches from flute to French horn.
Studies the instrument on his own while listening enthusiastically torecordings by renowned horn player Dennis Brain (1921-1957).
1985
Age 67
July: Member, Science Council of Japan.
President, Japan Statistical Society (to 1986).
Chairman of the Japan Association for Public Opinion Research (to 1999).
1986
Age 68
ISM professor emeritus; professor, Open University of Japan (to 1996).
Advisor, Japan Marketing Research Association (participation from 1999)(lifetime).
Hirotsugu Akaike is appointed as the eighth ISM director-general.
1987
Age 69Japan-France scientific joint seminars (1st seminar, Tokyo; 2nd seminar,
Montpellier, 1992).
Begins national character study of Japan, the US, Italy, France, Germany, theUK, and the Netherlands (establishes chain-sequence comparative survey
analysis method).
Sets up office in an apartment building in Sakuragaoka, Shibuya City anddevotes himself to international surveys and analysis.
His office becomes a gathering place for numerous researchers.
October 25: Louis Guttman dies at 71.

161

Chronology
1989
Age 71
Receives The Order of the Sacred Treasure, Gold and Silver Star.
Senior editor for the works of Joichi Suetsuna (three volumes, Nansosha).
1991
Age 73
Visiting professor, Open University of Japan (to 1996).
Begins survey of Japanese-Brazilians.
1992
Age 74Chair, Public Opinion Research Center.
Advisor (lifetime), Institute of Social Research, Institute of Nuclear Safety
System, Incorporated.
1994
Age 76
Ryoichi Shimizu is appointed as the ninth ISM director-general.
1996
Age 78International Federation of Classification Societies (IFCS) conference held in
Kobe.
1997
Age 79Japan Statistical Society Award.

annual meeting of the Behaviormetric Society at Koshien University, his firstsuch cancellation.
2002
Age 84
March 31: Genshiro Kitagawa is appointed as the tenth ISM director-general.
May 11: Lectures on “Data Science and Survey Methods” at BehaviormetricSociety Symposium.
June 8: Plays horn (“Memories of the Alhambra" for the Atago Concert.)
July 6: Publishing committee for the complete works of Chikio Hayashi holds
its first meeting. (At this meeting, Hayashi’s third admission to the hospital on
June 29 was announced. A list of Hayashi’s works, prepared in his own handwriting, is distributed to the committee.) He continues to polish the manuscript
while in the ICU.
August 6: Dies of multiple organ failure; posthumously awarded the Senior
Fourth Rank (court rank).
August 15: Suji ga akasu nihon-jin no senzairyoku (Numbers reveal the hidden
strength of the Japanese), co-authored with Masafumi Sakuraba, is publishedby Kodansha.
2004
November 30: Hayashi Chikio chosakushu (The complete works of Chikio
Hayashi) in 15 volumes is published by Bensei Shuppan.

1998
Age 80President, IFCS (to 1999).
1999
Age 81
Survey of Japanese-Americans on US West Coast.
2000
Age 82
Enjoys trip to Germany with Reiko.
2001
Age 83June 1: Deeta no kagaku (Data science) is published by Asakura Shoten.
Hayashi entrusts Ryozo Yoshino to deliver the book to Hiroshi Mizuno, andwrites on its cover, “Finally, I came this far.” (Mizuno himself would passaway in September 2003, only a year after Hayashi.)
September 11: Terror attacks on the United States.
September 14: Emergency admission to Nippon Medical School Hospital
due to cardiac dysfunction; is forced to cancel his keynote address at the 29th

162

163

References

References

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to social behavior research). Nenpo shakai shinrigaku (The Annual Report of Social
Psychology), Keiso Shobo, 5, 73–103. (in Japanese)
Akuto, H. 2018. “From Multi-variate Analysis through Quantification-Theory
to Multi-Dimensional Scaling.” Kodo keiryogaku (The Japanese Journal of
Behaviormetrics), 45(2), 75-89. (in Japanese)
Behaviormetric Society of Japan 73rd Symposium. 2003. “Deeta no kagaku to chosaho:
Hayashi Chikio sensei koen kiroku” (Data science and research methods: transcriptof a lecture by Chikio Hayashi). Kodo keiryogaku (The Japanese Journal of
Behaviormetrics), 30(2), 179–189. (in Japanese)
Behaviormetric Society of Japan, 2008. 35th Anniversary Commemorative Issue.
Behaviormetric Society of Japan. (in Japanese)
Benzécri, J.-P. 1973. L'Analyse des Données. Tome 1: La Taxinomie. Tome 2: L'Analyse
des Correspondances, Dunod. (in French)
Couper, M.P. 2008. Designing Effective Web Surveys. Cambridge University Press.
Couper, M.P. 2013. “Is the Sky Falling - New Technology, Changing Media, and the
Future of Surveys.” Survey Research Methods, 7(3), 145–156.
“Forum. 21 seiki no shakai to ningen: kagakuteki shuho ni yoru yosoku ha doko madeyuko ka. Paneru desukasshon” (Forum. 21st-century society and human beings:
how valid are projections based on scientific methods - Panel discussion.). 1996.
Kei Takeuchi, Chikio Hayashi, Shigemi Kono, Takamitsu Sawa, Takashi Sakamoto,Toshiyuki Furukawa, Yasuto Yoshizoe (moderator). Makuhari Messe International
Conference Room. (in Japanese)
Greenacre, M. J. 1984. Theory and Applications of Correspondence Analysis. Academic
Press, London.
Guttman, L. 1954. “The principal components of scalable attitudes.” In: MathematicalThinking in the Social Sciences. P. L. Lazarsfeld (ed.). New York: Free Press (pp.
216-257).
Guttman, L. 1968. “A general nonmetric technique for finding the smallest coordinate
space for a configuration of points”. Psychometrika, 33(4), 469–506.
Hayashi, C. 1952. “On the Prediction of Phenomena from Quantitative Data and the
Quantification of Qualitative Data from the Mathematico-Statistical Point of View.”
Ann. Inst. Stat. Math, 3(2), 69–98.
Hayashi, C. 1957. “Note on Sampling from a Sociometric Pattern.” Ann. Inst. Stat.

Math, 9(1), 49-52.
Hayashi, C. 1959. “Fundamental Concept of the Theory of Quantification andPrediction.” In: The Proceedings of the Institute of Statistical Mathematics, Z.
Suetuna, K. Matusita, C. Hayashi, H. Aoyama (eds.). 7(1), 43-64. (in Japanese)
Hayashi, C. 1973. “Igaku houmen ni okeru toukeiteki houhou no tsukaikata ni tsuite”(How to use statistical methods in the medical field), Nihon Koshu Eisei Zasshi(Japanese journal of public health), 20(2), 27-28. (in Japanese)
Hayashi, C. 1974. “Minimum Dimension Analysis MDA: One of the Methods ofMultidimensional Quantification (MDQ).” Behaviormetrika. 1(1), 1–24.
Hayashi, C. 1976. “Minimum Dimension Analysis, MDA–OR and MDA–UO.” In:
Essays in probability and statistics: a volume in honor of Professor Junjiro Ogawa.
S. Ikeda et al. (eds.). (pp. 395-412). Tokyo: Editorial Committee for Publication of
Essays in Probability and Statistics : distributed by Shinko Tsusho.
Hayashi, C. and Akuto, H. (eds.) 1976. Tajigen shakudo kaisekiho: sono yukosei tomondaiten (Multidimensional scaling analysis methods: effectiveness andchallenges). Saiensu-sha. (in Japanese)
Hayashi, C. 1977. Deeta kaiseki no kangaekata (A way of thinking about data science).
Toyo Keizai Shinpo-sha. (in Japanese)
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sekkin” (Statistical approach for multidimensional analysis). Institute of Statistical
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Hayashi, C. 1980. “Nihon-jin no ishiki no soko wo saguru” (Attempt to explore theorigin of Japanese interest in ghosts). Suuri-kagaku (Math. Sciences), 18(6), 23-32.
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multidimensional scaling analysis). Saiensu-sha.
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kataru” (From data analysis to data science —Talking about data as science), Deeta
wea hausu ga bijinesu wo kaeru (Data warehouse changes business), Nikkei BP
Mook. (pp. 82-87). (in Japanese)
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example.” In: Data Science, Classification, and Related Methods. Proceedingsof the Fifth Conference of the International Federation of Classification Societies(IFCS-96), Kobe, Japan, March 27-30, 1996. C. Hayashi, K. Yajima, H.-H. Bock, N.

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Ohsumi, Y. Tanaka, and Y. Baba (eds.). Springer, Japan. (pp. 40-51).
Hayashi, C. 1998. “The quantitative study of national character. Interchronological andInternational Perspectives.” In: Values and Attitudes Across Nations and Times(International Studies in Sociology & Social Anthropology), M.Sasaki (ed.). Brill
Academic Publishers. (pp. 91-114).
Hayashi, C. 2001. Deeta no kagaku shiriizu: deeta no kagaku 1 (Data Science Series:
Data Science 1). Asakura Shoten. (in Japanese)
Hayashi, C. and Sakuraba, M. 2002. Suji ga akasu nihonjin no senzairyoku (Numbers
reveal the hidden strength of the Japanese). Kodansha. (in Japanese)
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166

167

IndexIndex
Abbreviations:
AOR

Asahi Operations Research

CA

Correspondence Analysis

CIE

Civil Information and Educational Section

CLA

Cultural Link Analysis

ICU

Intensive Care Unit

ECG

Electrocardiography

ISM

Institute of Statistical Mathematics

IFCS

International Federation of Classification Societies

INRIA

Institute National de Recherche en Informatique et en Automatique

JCS

Japanese Classification Society

JSPS

Japanese Society for the Promotion of Science

MDA

Minimum dimension analysis

MDA-OR Minimum dimension analysis of ordered class belonging
MDA-UO Minimum dimension analysis of unordered class belonging
MDS

Metric multidimensional scaling

NHK

Japan Broadcasting corporation

NIER

National Institute for Education

NINJAL

National Institute for Japanese Language and Linguistics

nMDS

non-metric MDS

QOLQuality of life

SSA

Smallest space analysis

TAP

Travel Award Program

UPS

Universe, population, sample

168

A
Akaike, Hirotsugu 161
Akuto, Hiroshi 57, 76, 85
aliens 82-84
Aoyama Gakuin University 46,
64, 90Asahi, Shimbun 59, 68, 86, 135,
156
Atago 22, 160, 163
Atsugi 26
AOR 157
B
Baba, Yasuo 126
Bayesian probability 63
Bayesian inference 148
Behaviormetrika 61-62, 98
Behaviormetric Society 62, 92-99,
101, 127, 129, 141, 159, 163
Benzécri, Jean-Paul 112-117, 160
Big data 65, 100, 118, 135
Biometric Society 160
blog 120bogey 76, 82, 84-86, 160
bootstrap 99
Bourbaki 112
Brain, Dennis 161
bubble 131
Bunkyo 16, 154
C
CA 112, 149
central limit theorem 38, 50
CIE 37

CLA 107D
data analysis 10-11, 28, 39-40, 58,
60, 62, 68-69, 71, 73, 99, 112114, 116-120, 124, 126, 130,
134, 136, 146-147, 149-150
data mining 118-120, 129, 134
data reduction 78
data science 11-12, 26, 29, 40, 54,
87, 118, 121, 126, 128-129, 133,
135-136, 140, 146, 148, 152,
162-163
Debussy 21
Dentsu 71-73
Diday, Edwin 117
dragon 82-83, 85
Dugué, Daniel 113
E
ECG 64, 157
eij-type quantification 56
eijk-type quantification 56
F
Facebook 120
Finney, D.J. 160
Fisher 148
Fujitsu 58-59
G
game theory 30, 50
Gaussian distribution
58, 91

169

38, 48-50,

Indexghost 49, 76, 82-84, 86
Ginza 72
Guttman, Louis 52-54, 77, 80-81,
84, 157, 159, 161
Guttman scale 52, 54, 156
H
Hachijo 39
Hawaii 22, 106-109, 155, 159
Hayakawa, Hirokazu 130
Hayakawa, Takeshi 113
Hebrew University 157
Hibiya 30
Hidano, Tadashi 127
Hill, M. 114-115
Hinode 137
Hokkaido University 57
Hongo 16, 98, 154
Honolulu 107
Hume, David 55
I
ICU 163
IFCS 125-127, 162
Ikeda, Toshio 59
Ikeuchi, Hajime 52, 54-57, 156
Imperial Japanese Army / Imperial Army
25-26, 71, 155
Inagaki, Nobuo 113
Indo, Taro 56-57, 156
information 11, 12, 26-27, 37, 71,
74, 97, 113, 118, 125, 127, 153,
156, 160
INRIA 113

170

Institute of Nuclear Safety System
162
Institute of Social Research 162
International Biometric Society
160
Ishida, Masatsugu 58
Ishiguro, Osamu 36
ISM 32-33, 36-37, 42, 48, 51, 54,
56, 58-59, 64, 68-69, 72, 76-77,
90, 92-95, 97, 99, 106, 113, 117,
124-129, 136, 140, 156-159,
161-163
Istanbul University 157
Iwatsubo, Shuichi 94-95, 124,
129
J
Jambu, Michel 117
Japan Association for Public Opinion Research
72, 161
Japan Association for Philosophy of Science
91-92, 159
Japan Association for Forestry Statistics
158
Japan Color Research Institute93, 159
Japanese Language Council 160161
Japanese Society for Hares 158,
160
Japan Statistical Society 92, 161162
Japanese Wildlife Research Society
160

Japan Women’s University 57
JCS 125, 161
Jennings, Helen Hall 56
Japan Marketing Association
159-160
Japan Marketing Research Association
161
JSPS 117K
Kaisei middle school 16, 21-22,
154
Kakeya, Soichi 23, 29, 31-32, 90,
155-156
Kakutani, Shizuo 32
kamikaze 28
kappa 82-83, 85, 103
Kawada, Yoshitaka 159
Keifukuji 137
Keio University 56
Kimura, Eiichi 64, 130, 157
Kitagawa, Genshiro 136, 163
Kitagawa, Toshio 156
Kobe 125, 162
Kodaira, Kunihiko 30
Kolmogorov, Andrei 30
Komagome 16, 154
Komiya, Jun’ichi 70
Komuro, Naoki 98-99
Koshien University 129, 141, 163
Kruskal, Joseph Bernard 79-80
Kubota, Tadahiko 156
Kurabo industries 157
Kurashiki spinning works 157

Kurochaya 137
Kuroda, Yasumasa 107
Kyushu University 51
L
Lebart, Ludovic 117
Likert scale 48, 50-52
Loch Ness Monster 82-84, 86
Locke, John 55
Mmarket survey 68-69, 73, 158
marketing research 68, 70-72, 92,
120, 161
Masuyama, Motosaburo 62
Matsushita, Kameo 113
McGill University 127
MDA 77, 80-81, 84
MDA-OR 84
MDA-UO 84
MDS 79, 81
Meidaimae 155
minimax, theorem 50
Mises, Richard von 22-24, 30,
100-101, 157
Misumi, Juji 51
Mitakadai 60, 157
Mizuhara, Taisuke 56
Mizuno, Hiroshi 39-40, 54-55, 58,
162
Mizuno, Kinji 92-97, 127
Moreno, Jacob L. 56
Morinaga 70, 158
multivariate analysis 77, 97, 127

171

Index
Mito Flying school 25, 155
Murakami, Masakatsu 124
Murayama, Takayoshi 68
Musashino Art University 60-61
Mutai, Risaku 36
N
Nagoya University 94
Nakajima, Kenzo 20, 154
Nakano 154-155, 157
Neumann, John von 30, 50, 100
Neyman 148
Neyman-Pearson 38
NHK 22, 37, 69, 70, 113, 158-159
NIER 36
Nikkan Kogyo Shimbun 68
NINJAL 36
Ninomija, Satoki 64
Nippon Medical School 64, 130,
157, 162
Nishihira, Shigeki 93, 113
Nishimura, Katsuhiko 46, 48
Nishi-Nippori 16
Nishitama district 137
nMDS 79
Nomoto, Kikuo 36
normal distribution 38
Oobake 76
Obonai, Torao 93
Occam’s razor (parsimony) 77
Ochanomizu University 56
Ogawa, Takashi 56

172

Ohashi, Reiko 157
Ohira, Masayoshi 160
Ohsumi, Noboru 113, 117, 120,
126
Okemoto, Masao 68
oni 82-83, 85
onryo 82-83, 85
Open University of Japan 124,
161-162
operations research/OR 39, 68,
73, 157
Oshio, Shunsuke 56
Ouchi prize 157
P
Pacific War 155
Pelzel, John C. 37
phantoms 82-83, 85
probit analysis 92
Psychometrika 98
Public Opinion Research Center
162
Q
QOL 64
quantification method KL-type
81, 84
Quantification Method Type I 57,
68, 156
Quantification Method Type II
57, 68, 91, 156
Quantification Method Type III60, 69, 107, 112, 115, 156-157,
159

Quantification Method Type IV
56-57
quantification theory 12, 28, 48,
51-52, 54, 56-57, 59, 64, 68-71,
81, 117-119, 128, 136, 156-158
Quetelet, A. 38-39, 49
Rrating 69-70, 72-73
readership 68
Reichenbach, Hans 157
Remington Rand 26
Roux, Maurice 117
RWTH Aachen University 125
S
Sakuragaoka 124, 161
Sakuraba, Masafumi 132, 135,
141, 163
sampling theory 10, 54, 133
Saito, Ichiro 60
Sakasegawa, Hirotaka 113
Sano, Katsuo 56
Sasaki, Tatsujiro 58, 156-157
Sato, Keinosuke 60-62, 157
Sato, Masaaki 20, 154
Science Council of Japan 160-161
Seijo high school 22, 154
September 11 128-129, 141, 162
Seya, Masatoshi 90
Shepard, Roger Newland 79-80
Shibata, Takeshi 36
Shibuya 124, 127, 161
Shimazu, Kazuo 36

Shimizu, Ryoichi 162
Shiodome 72
Sino-Japanese War 154
snowman, the abominable 76,
82-84, 86
spirit, vengeful 82-83, 85
spirit, wrathful 82-83, 85
SSA 77, 80-81, 84
Suetsuna, Joichi 90, 135, 156,
158-159, 162
Sugiyama, Meiko 69, 113, 127
Suzuki, Yukio 113
supernatural power 82-83, 85
Surikagaku 86
survey 10, 21, 22, 36-43, 46-50,
56-57, 63, 65, 68-69, 71-73, 76,
81, 84-86, 95-97, 99, 106-109,
111, 119-120, 124, 130-133,
140, 148, 156-163
T
TAP 126
Takagi, Sadaji 56-57, 157
Takane, Yoshio 127
telekinesis 86
Tenmatsu 125
Terada, Torahiko 17, 19-20, 86
time machine / time traveler 8283, 85-86
Tokyo Imperial University 17, 23,
25, 36, 58, 155
Tokyo Metropolitan University 56
Tokyo University of Education 36,
93173

Torgerson, Warren S. 79
Tsukiji 72
Tucker, Ledyard R. 81, 127
Tukey, John 113, 116
Twitter 119-120

Yokai 83, 85
Yokohama 47
Yonezawa 84
Yoshino, Ryozo 124, 162
yukio 84

U
UFO 76, 82-84, 86
Umeoka, Yoshitaka 56-57, 156
University of Hawaii 107
University of Illinois 81, 127
University of London 157
University of Paris VI 112-113
University of Tokyo 29, 52, 56-57,
90, 93-94, 99, 113, 156-159
UPS 133

Z
zaibatsu 157
Zen 137

V
Valéry, Paul 17-20, 25, 60, 154viewership 69
W
Wald, Abraham 50,100
water sprite 82-84
Weber, Max 110
Weber-Fechner Law 91
World War I 154
World War II 9, 131, 155
Y
Yale University 32
Yamagata 84
Yanagihara, Ryozo 96-97
Yanai, Haruo 94, 97-98

174

About the Author
Kumiko Maruyama graduated with a Master's Degree in
psychology from Aoyama Gakuin University. She received special
training in data science under the guidance of Professor Chikio
Hayashi of the Institute of Statistical Mathematics. K. Maruyama
became a research assistant at Aoyama Gakuin University, then aspecial research student at the Division of Educational Psychology,
Graduate School of Education, the University of Tokyo. She
studied psychometrics at the University of Illinois. Subsequently,
she was a professor at Morioka University, then a professor
at Seigakuin University, a visiting professor at the Universityof Cologne (Germany), a visiting researcher at the National
University of Ireland and at the University College Dublin, anda professor at Hokuriku Gakuin University. She is currently an
emeritus professor at Seigakuin University. K. Maruyama was arecipient of the Chikio Hayashi Award.
Her scientific publications include several articles co-authoredwith Professor C. Hayashi. K. Maruyama has written several
biographies, including "Hayashi Chikio no Shogai" (The Life of
Chikio Hayashi), Shin-yo-sha, 2015; "Kitamori Kazoh Den" (The
Story of Kazoh Kitamori, Theologian), Kyobun-sha, 2016; "Soutou
no Washi" (The Life of Tokiyuki Hojo, Educator, Mathematician),
Kosaku-sha, 2018; "Sakuta Akira no Shogai" (The Life of Akira
Sakuta, Criminal psychiatrist), Seigakuin University Press, 2020.

175

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