Causes of Social Dilemma: A Case of Autonomous Vehicles
MORITA Tamaki* and MANAGI Shunsuke**
Abstract  Artificial intelligence（AI）equipped in autonomous vehicles（AV）that drives fully automaticallymay have to face dilemmas to choose between two evils, such as running over pedestrians versus
sacrificing themselves and their passengers in order to save them. This dilemma may also cause asocial dilemma that if people have different ideas about “moral AVs” versus “wishing AVs” whilewishing（or demanding）AVs are immoral, the market will eventually be filled with immoral AVs
regardless of peoples' ascribed moralities. With an online survey giving 14,829 effective responses
from all over Japan, we find that the social dilemma will occur in Japan as well as in the U.S. We
also focused on the relationship between the morality and the relative intention to buy “moral AVs,”
likewise exploring the factors of the social dilemma. We find that the more credible AVs are, thehigher the likelihood that social dilemmas will occur. This finding implies that there is a possibility
that such social dilemmas will not be resolved until the number of car accidents reaches zero.
キーワード：自動運転車、社会的ジレンマ、人工知能（AI）、道徳観
Key words: Artificial Intelligence（AI）, Autonomous Vehicles, Morality, Social Dilemma

Ⅰ． Introduction

United States, Bonnefon, et al.（2016）revealed

Social dilemma is defined as the situationthat people would like AVs to be utilitarian, i.e.,

when“private interests are at odds witha moral actor to save as many lives as possible

collective interests”（Van Lange, 1989）. In theeven though the AVs will occasionallycase of autonomous vehicles（AVs）, if people

sacrifice passengers, whereas people will

have different ideas about“moral AVs”andprefer to purchase AVs that are programmed“wishing AVs”and if wishing AVs are immoral,

to save passengers（themselves）. Theyit will cause a social dilemma. The market willpropose two suggestions to overcome thiseventually be filled with immoral AVs because

social dilemma in Shariff, et al.（2017）: One

people will actually purchase what they needis to shift the discussion from the relative risk

regardless of their moral scruples.

of injury to the absolute reduction of risk to

An artificial intelligence（AI）-equipped AV

passengers due to overall accident reduction,that drives fully automatically may end up

and the other is to appeal to consumers'

facing the dilemma of choosing between twodesire for virtue signaling. We feel the need toevils such as running over pedestrians versus

provide an econometric analysis of the causes

sacrificing the passengers themselves in orderof the dilemma further before we make suchto save them.

1）* 山梨県立大学

国際政策学部

In a survey conducted in thesuggestions.

総合政策学科

Department of Policy Management, Faculty of Glocal Policy Management and Communications, Yamanashi
Prefectural University** 九州大学

工学研究院

環境社会部門都市システム工学講座

Department of Urban and Environmental Engineerring, Faculty of Engineering, Kyushu University― 93 ―

山梨国際研究山梨県立大学国際政策学部紀要

No.14（2019）

We follow Bonnefon, et al.'s U.S. survey anda family member] are in the car travelingconducted an online survey named Survey

down a main road on a bridge. Suddenly,

on Auto Driving to about 18,000 Japanese

10 pedestrians appear ahead, in the direct

respondents

2）and collected 14,829 effective

path of the car. If the car swerves to the

responses from those who revealed theirside of the road, it will plunge into theintention to buy an AV in the future regardless

river, killing you and your [coworker orof the weakness of their intent. In Chapter

acquaintance/family member] but leaving

II, we compare the morality in Japan and thethe pedestrians unharmed. If the car stays

U.S., Chapter III analyzes the relationshipon your current path, it will kill the 10

between the morality and purchase, Chapter

pedestrians, you and your [coworker orIV investigates the determinants of purchaseacquaintance/family member] will be

intention and consumer heterogeneity, andunharmed. 3）

Chapter V concludes.

One-third of the respondents are set to imagine
riding alone and read the text beginning with,

Ⅱ．Comparisons of Morality“You are in the car traveling down a mainFirst, we compare our results with theroad...,”another third imagine riding with aBonnefon, et al.（2016）'s previous researchcoworker or an acquaintance and reading,in the U.S. In our research, we explained the“You and a coworker or an acquaintance aresituation with both the scenario below andin the car traveling down a main road...,”andFigure 1.

the last third read,“You and a family member

You and [a coworker or an acquaintance/

are in the car traveling down a main road...”Figure 1. The Two Alternatives for AVs to Choose
Note: The left caption means“swerve,”and the right one means“stay”.
Source: Survey on Auto Driving, modified from Bonnefon et al.（2016）Figure 1, p. 1574.

Morality is defined by the response to

your acquaintance/and your family member]the question: Rate what action you think is

but killing the 10 pedestrians”and“swerve,

the most moral, on a 0-100 slider anchoredsparing the 10 pedestrians but killing you

at“stay, saving you [and your coworker or[and your coworker or your acquaintance/― 94 ―

Causes of Social Dilemma: A Case of Autonomous Vehicles

and your family member].”What follows

Respondents select the answer on a 0-100

are the questions regarding how inclined

slider anchored by“not at all likely”andthey would be to buy an AV programmed to“extremely likely”for each question. In

swerve（“minimize”the number of sacrifices,

Figure 2, we see that respondents' tendencies

i.e., sacrifice the passengers）, and how inclined

are alike in both countries, except that thethey would be to buy an AV programmed

purchase intention of a protective AV whento stay the course（i.e., be“self-protective”）.

riding with families is lower in Japan.

Panel A: The United States

Panel B: Japan

Figure 2. Morality and Purchase Intentions（The United States and Japan）
Source: Bonnefon et al., Figure 3.A, p. 1575,（A）; Surveys on Auto Driving（B）

Boxes in Figure 2 show the 95％ confidence

would not buy moral cars if they usuallyintervals from the mean（the horizontal line

ride with families, but whether they wouldin the center of each box is the mean）. In

buy protective cars does not depend on theboth countries, participants expressed moral

existence of fellow passengers.

preferences（over 50）for AVs sacrificing
their passengers to save a greater number

Ⅲ．The relationship between Morality andof pedestrians. However, participants did

Purchase Intention

not express a comparable intention to buy

We now focus on the relationship between

utilitarian AVs that minimize the number ofthe morality and purchase intention to thesacrifices especially when they are asked to

two types of the AVs. Figure 3 shows theimagine their family member riding in the car.

averages of purchase intention for“minimize”

This tendency is common in both countries.

（variable Buy_Minimize）and“self-protective”

As for self-protective AVs, U.S. respondents（Buy_Protective）, and the averages of eachshowed a higher intention to buy it when

respondent's difference between them

they thought of their family members than

（Buy_Minimize－Buy_Protective, variable

when riding alone, while the Japanese did not

Difference）4） as box plots. The x-axis depicts

show any significant difference. The Japanesethe morality scaled by ten from 0 to 49 and― 95 ―

山梨国際研究山梨県立大学国際政策学部紀要

No.14（2019）from 51 to 100. Fifty is solely presented

but the variances are large and unevenly

because as much as 17.0％ of respondents

distributed. The distribution of Buy_Minimize

chose it. If the relationship between Morality

is upward sloping with almost even but withand Buy_Minimize are upward sloping, thatlarge variances（Panel A）, and that of Buy_

between Morality and Buy _Protective areProtective is downward sloping with large,

downward sloping, and that between Morality

uneven variances（Panel B）. Difference, i.e.,

and Difference results to be upward sloping,each respondent's gap between Buy_Minimize

then, the social dilemma will not occur. The moreand Buy_Protective（Panel C）is roughly

Moral people get, the broader the Difference andupward sloping, but the variance is also large

more moral AVs will be purchased.

and uneven. These variances may be one of

In Figure 3, the relationship seems to hold,
Panel A: Buy_Minimize

the reasons for the social dilemma.

Panel B: Buy_Protective

Panel C: Difference

Figure 3. Relationships between Morality and Purchase Intention
Note: Of each box, upper hinge is 75th percentile, lower hinge is 25th percentile, and the centerline depicts the median.
Whiskers show upper and lower adjacent values. Points indicate outside values.
Source: Survey on Auto Driving

That being so, let us check the relationship

fit indicates that, on average, the higher the

between Morality and Difference further.

morality, the more likely respondents are to

Figure 4 is the distribution of the value ofbuy minimizing AVs（Difference > 0）
; and theDifference scattered along with Morality scaled

lower the morality, they are more likely to buy

by one. The data is the same as Panel C inprotective AVs（Difference < 0）
. As we noted

Figure 3, but those who evaluated both AVs

for Figure 3, if this relationship holds strictly,

equally（Buy_Minimize = Buy_Protective andthe social dilemma will not occur. In ourBoth = 0 in Table 1）are excluded. The upward

samples, however, many marks are unevenly

sloping line is a fitted line with an approximate

scattered away from the fitted line. Looking

thickness of the 95％ confidence interval. The

at those who prefer to minimize the self-― 96 ―

Causes of Social Dilemma: A Case of Autonomous Vehicles

protected outcome（Difference > 0, quadrants

distribute in quadrant three（N = 2,300）and

one and two in Figure 2）, the number offour（N = 2,489）. 6） Here, less-moral people

respondents increases with morality, leaving（Morality < 50）intend to buy protective AVs,

quadrant one（N = 3,952）much more crowded

but more-moral people（Morality > 50）also

5）than quadrant two（N = 640）. Nevertheless,may buy protective ones. In order to find thethose who prefer protective AVs（Difference

reasons for this distribution pattern, we look

< 0, quadrants three and four）are not clearly

into the determinants of Difference other thanincreasing in morality. They almost equally

Morality.

Figure 4. Morality and preferences
Note: Each point depicts each response. The points get thicker where there are multiple responses.
Source: Survey on Auto Driving

Table 1. Frequencies of Responses by Types of Differences
Types of the Difference

Frequency

Percentage

Buy_Minimize > Buy_Protective
Buy_Minimize = Buy_Protective（except Both = 0）

4,796
2,032

32.3
13.7

Buy_Minimize < Buy_Protective
Both = 0

5,438
2,563

36.7
17.3

Total

14,829100.0

Note:“Both = 0”means Buy_Minimize = Buy_Protective = 0.
Source: Survey on Auto Driving― 97 ―

山梨国際研究山梨県立大学国際政策学部紀要

No.14（2019）

Ⅳ．Determinants of Purchase Intentions

models 3 and 4.

Using simple ordinary least squares（OLS）

In Table 2, if the sign of the coefficientestimation, we explore the determinants ofof each independent variable are same in

Buy_Minimize（eq. 1 and model 1 in Table 2）,models（5）and（6）, then the attribute can

Buy_Protective（eq. 2 and model 2）, Difference

be considered to prevent the social dilemma.

（eq. 3 and model 3）, and Morality（eq. 4 and

The more moral the people get, the moremodel 4）. The equations are as follows:

they relatively choose to“minimize,”and

Buy_Minimize =（1）

Buy_Protective =（2）

Difference = Buy_Minimize－Buy_Protective
（3）
=
. （4）

Morality =vice versa. The solution for the dilemma inthis case would be to produce AVs that suitconsumers' moralities, or educate people to be
more moral if the citizens would like to makesociety as a whole more moral. In our data,
factors preventing social dilemma are Age,
Altruism, Male, HighEducation and Impatience.
Age and Altruism have positive effects on Buy_as the n×1 vector of independent

Minimize, and negative ones on Buy_Protective.

variable（underlines denote vectors）where

This leads to have them positive effects on

n is the number of respondents, andas aDifference, while they as well have positiven×3 matrix whose columns indicates theeffects on Morality. Male, HighEducation,passenger type as［Alone WithCoworker

and Impatience works negatively on both

WithFamily］, where one of［0 0 0］,［0 1 0］,

Difference and Morality so that they will buy

or［0 0 1］is randomly assigned to eachless moral AVs according to their morality.

We setis an n×

It is important to note that the existence ofk matrix（j=1, ... k where k is the number

the fellow passengers will not generate theis a k×

dilemma. When the respondents are assumed

1 vector. The descriptive statistics andto ride with coworkers or families, coefficient

distributions of the explanatory variables areof Buy_Minimize is lower and Buy_Protective

summarized in Table 3 and Table 4.

is higher than assuming riding alone, which

respondents.

is an 3×1 vector,of respondents' attributes,）and

Besides, keeping significant explanatory

leads to negative coefficients of Difference.

variables, equations 3 and 4 are

Together with negative coefficients ofsimultaneously estimated by Zellner's

Morality, their purchasing behavior will not7）seemingly unrelated regression, assuming thecontradict to their morality. If all dependent

are correlated（models

variables show this tendency, then both of

5 and 6）. Because they are in fact correlated

Shariff, et al.（2017）'s suggestions will bewith significant correlation coefficient ofeffective.

error terms

and0.4959, models 5 and 6 are more reliable than

― 98 ―

Causes of Social Dilemma: A Case of Autonomous Vehicles

Table 2. Determinants of Intention to Buy each AVs and Their Difference
Dependent

All Variables

SUR

（1）
Buy Minimize（2）
Buy Protective（3）
Difference（4）
Morality（5）
Difference（6）
Morality

WithCoworker-7.908 ***（0.695）4.295 ***
（0.710）-6.102 ***（0.533）-10.380 ***（0.677）-6.112 ***（0.529）-10.39 ***（0.671）

WithFamily-3.213 ***（0.685）2.656 ***
（0.705）-2.935 ***（0.527）-4.683 ***（0.658）-2.910 ***（0.524）-4.642 ***（0.665）Male

2.297 ***
（0.610）6.869 ***
（0.624）-2.286 ***（0.462）-5.298 ***（0.580）-2.156 ***（0.461）-5.163 ***（0.584）Age

0.201 ***
（0.023）-0.192 ***（0.024）0.197 ***
（0.018）0.234 ***
（0.022）0.200 ***
（0.017）0.242 ***
（0.022）

HighEducation-3.217 ***（0.609）1.981 **
（0.624）-2.599 ***（0.468）-3.197 ***（0.587）-2.739 ***（0.462）-3.547 ***（0.578）

Income（ log）

-1.360 **（0.427）1.625 ***
（0.440）-1.493 ***（0.333）-0.805 ＋（0.430）-1.138 ***（0.274）

DislikeShareCars-2.491 ***（0.653）-1.567 *（0.681）-0.462（0.511）

0.239（0.639）Pride3.019 ***
（0.689）3.185 ***
（0.709）-0.0833（0.517）-0.991（0.669）NoCar

0.0384（0.732）

1.564 *
（0.744）-0.763（0.564）-1.372 ＋（0.716）

FavorDriving-3.806 ***（0.683）-3.029 ***（0.712）-0.388（0.533）-0.592（0.676）

CausedAccidents-1.708 **（0.645）-3.394 ***（0.667）0.843 ＋
（0.499）1.040 ＋
（0.630）Credibility

0.0217（0.015）0.157 ***
（0.015）-0.0677 ***（0.012）0.049 **
（0.015）-0.0675 ***（0.011）0.0478 ***（0.014）

Altruism

5.282 ***
（0.336）-3.789 ***（0.344）4.535 ***
（0.264）5.226 ***
（0.338）4.524 ***
（0.244）5.141 ***
（0.309）Impatiance-0.138（0.140）0.378 **
（0.145）-0.258 *（0.110）-0.277 *（0.140）-0.265 *（0.104）-0.307 *（0.132）

Constants

24.410 ***（3.117）34.830 ***（3.237）44.790 ***（2.445）43.780 ***（3.178）42.270 ***（2.113）38.540 ***（1.850）AIC
R-squared

97619.3
0.058798110.3
0.060292159.1
0.075

97028.4
0.0792

Independent

Note: 1. Standard errors in parenthesis
2. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001
Source: Survey on Auto Driving― 99 ―

186297.9
0.07440.0781

山梨国際研究山梨県立大学国際政策学部紀要

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Table 3. Descriptive Statistics of Variables
Variable

Description

Mean

S. D.

Minimum Maximum Population
Mean

BuyMinimize

A number selected from the slider, whichranged from“not at all likely（0）”to“extremely likely（100）
.”

38.8

29.3

0

100

BuyProtective

A number selected from the slider, whichranged from“not at all likely（0）”to“extremely likely（100）
.”

41.9

30.1

0

100

Difference

Each respondent's reply to BuyMinimize
subtract that with BuyProtective.（Linerlytransformed to 0-100 scale）

48.4

22.7

0

100

Morality

An action that respondents think is the most
moral, selected from the slider ranging from“stay（0）”and“swerve（100）.”

59.4

28.8

0

100

WithCoworker

Respondents who were asked to imagine
themselves in the car together with acoworker or acquaintance = 1; 0, otherwise.

0.334

0.472

0

1

WithFamily

Respondents who were asked to imagine
themselves in the car together with a family
membere = 1; 0, otherwise.

0.322

0.467

0

1

Male

Male = 1, Female = 0.

0.592

0.491

0

1

Age

Age scaled by one.

47.7

12.7

18

69

HighEducation

Respondents who graduated from a
university, graduate school, or equivalent = 1;0, otherwise.

0.586

0.493

0

1

Income（ log）

Logged value of the respondents' annual
household income.

6.355

0.704

3.912

8.161

DislikeShareCars From the choice of“Shared cars are totally
acceptable”（1）to“Shared cars are totally
unacceptable”（10）, those who chose a score of
6 or higher = 1; 0, otherwise.

0.290

0.454

0

1

Ranging from the choice“Do not have pride inowing cars at all”
（0）to“Have pride in owingcars very much”（10）, those who chose a scoreof 6 or higher = 1; 0, otherwise.

0.271

0.445

0

1

Pride

NoCar

Respondents who owns cars = 1; 0, otherwise.

0.244

0.430

0

1

FavorDriving

Among respondents who have a driver's
license, those who chose a score of 8 or higher
from the choice“Dislike driving very much”（10）= 1; 0,（1）to“Like driving very much”
otherwise.

0.279

0.448

0

1

CausedAccidents

Respondents who had been responsible forcausing a car accident = 1; 0, otherwise.

0.317

0.465

0

1

Credibility

A number selected from the slider, whichranged from“If all the cars in the county
become fully auto-driving, I think car
accidents will increase very much（0）”and“...,I think car accidents will decrease very much（100）.”

67.2

20.4

0

100

AltruismPrincipal component of two responses:（A）opinion of the statement“No matter thecircumstances that we are in, we should
help those who are in need,”ranging from
“Strongly Disagree”（1）to“Strongly Agree”（5）; and（B）frequency of donations.

3.491

0.895

0.772

6.402

Impatiance

Respondents' self-evaluated character, whichranged from“easy-going（1）”to“impatiant
（10）
.”

5.904

2.080

1

10

Source: Survey on Auto Driving― 100 ―

0.502
0.245

Causes of Social Dilemma: A Case of Autonomous Vehicles

Table 4. Distributions of Selected Variables
N％

Population（％）

Age

N

％

Altruism18 - 29

1,134

11.1

15.5

2>A

351

3.4

30 - 39

1,761

17.2

19.1

3>A≥2

3,370

32.9

40 - 49

2,479

24.2

50 - 59

2,650

25.9

23.5

4>A≥3

3,671

35.9

19.1

5>A≥4

2,464

24.1

60 - 69

2,210

21.6

22.9

A≥5

378

3.7

Income（million Yen）

Impatience

3>I

1,327

13.0

34.1

1-2

526

5.1

6>I≥3

3,563

34.8

31.7

3-4

2,231

21.8

9>I≥6

2,715

26.5

18.3

5-6

2,917

28.5

12 > I ≥ 9

1,578

15.4

9.1

7-8

3,691

36.1

I ≥ 12

1,051

10.3

7.0

9-10

869

8.5

0-24

382

3.8

25-49

997

9.7

Credibility

855

8.4

51-75

4,211

41.2

76-100

3,789

37.0

50

Source: Survey on Auto Driving

Nevertheless, one factor is found to cause

values（with 95％ confidence intervals）ofa dilemma. It is Credibility: a number selected

Difference8）and Morality, along with the values

from the slider, which ranged from“If all theof Age: one of the variables that may not cause

cars in the county become fully auto driving,

the dilemma. Difference and Morality go up

I think car accidents will increase very much

almost in parallel as Age rises. When riding（0）”and“...I think car accidents will decrease

with a family, the Morality value of a 20-year-

very much（100）.”As Credibility rises, so

old person is estimated as 46.9 and that ofdoes Buy_Protective（model 1）, but people

a 69-year-old as 59.0, while Difference of ado not show their clear intention to Buy_

20-year-old is estimated as 39.5 and a 69-year-

Minimize（model 2）. The resulting coefficient

old as 49.4. In Panel B, the predicted values areof Credibility is significantly negative forshown according to the values of Credibility.

Difference, whereas for Morality, it has positiveThose who think that car accidents will

coefficient（models 5 and 6）.

increase very much due to AVs（Credibility =Figure 5 exhibits this contrasting effect.

0）have a Morality of 50.7 and those who think

The coefficients in Table 2 are average

the opposite（Credibility = 100）have that ofeffects, and we can also predict the value of

55.5. The more people believe in AVs, the more

a dependent variable for each value of themoral they become. Perversely, zero Credibility

independent ones（setting the values of thehas 49.8 Difference and the Difference shrinks

other independent variables at their average）

to 43.1 for those with 100 Credibility. It follows

in Figure 5. Panel A gives th e predictedthat people who think AVs are credible are― 101 ―

山梨国際研究山梨県立大学国際政策学部紀要

No.14（2019）

Panel A: Age and the predicted independent variables

Panel B: Respondents' credibility and the predicted independent variables

Figure 5. Change in Marginal Effects on Difference and Morality
Source: Survey on Auto Driving

relatively less willing to purchase“minimize”

is that they believe AVs will increase theAVs, even though they are more moral.

number of accidents whatever they think

Our results contradict Sharif, et al.（2017）'s

morally for whatever type they choose to

first idea that overall accident reduction bybuy. As AVs become more credible, theyAVs may help overcome the social dilemma.

start making judgments that it is more moral

When AVs are not credible, Japanese people

to swerve（sparing the 10 pedestrians but

will avoid deciding both the morality andkilling passengers including themselves）,

their preference between two types of AVs

but they would not like to buy AVs that are（both scoring around 50, especially when

programmed to swerve.

riding with families）. The reason, we suppose,― 102 ―

Interpretation of the behavioral

Causes of Social Dilemma: A Case of Autonomous Vehicles

discrepancies between Morality and Difference

someone be sacrificed.

could be twofold. One is that as credibility

We are standing at the dawning of therises, people（possibly unintentionally）

AV society, and fully automotive private

assume the AVs more accurately follow thevehicles have yet to be brought to the market.

program, and believe that the“minimize”

However, before the technical barriers to theirprogram will better save pedestrians plus

utilization are finally broken down, we should

sacrifice themselves. The other interpretation

investigate the factors of the dilemma once

may be that as credibility rises, it will become

again to look into the relationship between

acceptable for people to purchase self-

our confidence in AVs and our purchasingprotecting cars because they can assume

behavior.

the number of such incidents they are to
face will decrease thanks to almighty AVs.

Acknowledgments

If these interpretations are right, reducing

We gratefully inform that this research isoverall accidents, ceteris paribus, will makefunded by RIETI research project“Economics

the social dilemma more serious. The socialof Artificial Intelligence”and partly by

dilemma may not be solved until any type of

JSPS KAKENHI: Grant-in-Aid for Scientific

AVs will be able to save all pedestrians and all

Research（B）[26285057].

passengers together.
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Rahwan（2017）

found credibility to the notion that AVs will

Adoption of Self-Driving Vehicles.”Nature Human

eliminate the total number of car accidents ――

Behaviour, 1: 694-696. December. https://doi.

a key factor in Japan. It is thought-provokingorg/10.1038/s41562-017-0202-6.

that the more credible AVs are, the greater the

Zellner, A.（1962）.“An efficient method of estimating

likelihood that the social dilemma will occur.

seemingly unrelated regressions and tests
for aggregation bias.”Journal of the American

The social dilemma may not be resolved until
the number of car accidents reaches zero,

Statistical Association 57: 348-368.
_____（1963）.“Estimators for seemingly unrelated

and when neither type of AV requires that― 103 ―

山梨国際研究山梨県立大学国際政策学部紀要

No.14（2019）regression equations: Some exact finite sample
results.”Journal of the American Statistical
Association 58: 977-992.
Zellner, A., and D. S. Huang（1962.）“Further properties
of efficient estimators for seemingly unrelated
regression equations.”International Economic
Review 3: 300-313.
1 ）This is a widely known“Trolley Problem,”on
which Foot（1978）originally drew attention, andagain discussed by Thomson（1985）.
2 ）The survey was administered in 2016 by Nikkei
Research Inc. who offers a representative panel ofhousehold in Japan, and funded by the ResearchInstitute of Economy, Trade and Industry, IAA（RIETI.）
3 ）This is a modified scenario used in Study 3 ofBonnefon et al.（2016）, and respondents readJapanese translated version.
4 ）Here, the variable Difference ranges from -100 to100. From the next section on, it will be linearlytransformed to take the range from 0 to 100, tomake it directly comparable to Buy_Minimize and
Buy_Protective.
5 ）240 respondents who chose fifty for Morality are
not counted here.
6 ）649 respondents who chose fifty for Morality are
not counted here.
7 ）See Zellner（1962）, Zellner and Huang（1962）, and
Zellner（1963）.
8 ）Difference in this section is linearly transformed totake the range from 0 to 100. See Table 3.

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