Is All Economics Local?
Speech given by
Andrew G Haldane
Chief Economist
Bank of England
Sheffield Political Economy Research Institute (SPERI) Annual Lecture
University of Sheffield
7 May 2019
The views expressed here are not necessarily those of the Bank of England or the Monetary Policy
Committee. I would like to thank Shiv Chowla, Fergus Cumming, Alastair Firrell, Ryan Lovelock,
Rebecca Mari, Georgina Parker and Doug Rendle for their help in preparing the text. I would like to
thank Arjun Ahluwalia, Mike Anson, Kate Barker, Paul Clarke, Diane Coyle, Tom Forth,
Clare Macallan and Rachael Muir for their comments and contributions.
It is wonderful to be here at the University of Sheffield, my alma mater, to give the SPERI Annual Lecture. I
have been a great admirer of SPERI's work over the years. It has played an important role in shaping the
conversation about public policy, economically and socially, locally and nationally. My topic today echoes
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many of the themes SPERI has championed the role of local perspectives and policies in better
understanding the economy and in shaping a better society.
Tip O'Neill, the former Speaker of the United said "all politics is
States House of Representatives, famously
local". Those words probably apply to politics everywhere. They have also probably been true at every point
in history. But at this particular point in history, with local or national identities becoming an increasingly
potent force shaping our economies and societies, those words have a particular resonance.
the O'Neill
That set me thinking. Does hypothesis hold for our economies too? Is all economics local? Are
our economies better understood as a nation-state solid or as a set of loosely-linked local clusters? Might
this have implications for how we map, model and manage the economy? And might it even have
implications for central banks, despite their policies operating nationally rather than locally?
I want to explore these questions, tentatively and provisionally, first by drawing some simple maps of the
economy at different spatial resolutions. These maps provide some insight into how "local" economies really
are. How large are differences between-region? How do they compare with differences within-region? And
what do we know about flows of factors, including people, between and within regions?
This local lens poses some interesting questions about how the economy should be modelled. Existing
models of the macro-economy operate at a high level of aggregation. When is this telescopic lens on the
economy the most useful one? When might a microscopic, or local, lens offer additional insights? And how,
practically, can those microscopic perspectives be pieced together to give a macroscopic overview?
When it comes to managing the economy, this has been fertile ground for policy debate recently. Several
including Michael Jacobs' work
reports have made the case for rethinking local economic policies, as part of
The Government has put "place"
the IPPR Commission on Economic Justice.1 centre-stage in its own policy
agenda.2 I discuss ways in which analytical techniques and data might help in shaping those policy choices.
Some areas of public policy can only operate at the national rather than local level, such as monetary policy.
Even then, however, a local perspective can be important for better understanding the economic issues
people face and for building understanding and trust among those people. That is why the Bank of England
has recently augmented its own local initiatives, as I will discuss. Other central banks are following suit.
1
IPPR Commission on Economic Justice (2018). See also Collier (2018).
2
Department for Business, Energy and Industrial Strategy (2017).
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In his recent book, Raghu Rajan blames neglect of the Third Pillar of society community relative to the
market and the state (the first two pillars) for rising societal disconnection and mistrust.3 Rajan is right.
Economic policymakers, including central banks, have a pivotal role to play in resurrecting that Third Pillar, in
making economic policy local, to better support our economies and societies. Here is how.
Mapping the Economy
Let me begin with some simple mapping of the economy. The most widely-used metric of economic success
is Gross Domestic Product (GDP).4 In simple terms, this measures how much the average person has
available to spend on the good things in life. If asked, most people would say this is a decent, if partial,
proxy for their overall economic health, even if they express bafflement at the concept of GDP.
Aggregate GDP is far from being the only possible proxy for economic health. The lived experience of most
people depends on more than their income. A rich strand of literature has developed alternative measures of
people's
well-being. As well as economic and financial health, these measures include physical and mental
health, their families, friends and communities.5 This work tells us that people's sense of well-being, if not
their GDP, is shaped by local factors, underpinned by the Third Pillar.
GDP may not chime with most people's
Another reason why aggregate lived experience is because, by
definition, most people are not "average". Economists use the idea of the average or representative agent
People's lived experiences often differ
as a convenient shortcut. But that agent is a fiction. very materially,
even within a single country or region - spatially, socially and financially. For most people's everyday lives
and everyday decisions, all economics is local.
These imperfections in GDP are well-understood. They certainly do not fatally undermine its usefulness as a
means of keeping economic score. They do, however, suggest it can usefully be complemented by looking
at the economy through different lenses. One of those different lenses comes from mapping the economy
bottom-up, rather than top-down, aggregating microscopic experiences into a macroscopic view.
In medicine, we use a variety of different tools, at different resolutions, to diagnose problems and when
prescribing solutions: thermometers, blood pressure monitors, X-rays, CT scans, ultrasound and blood tests.
Rarely does one of these measures provide all of the diagnostic answers. Using them in combination can,
however, help reach robust clinical conclusions. And that "micro-to-macro" approach is commonplace when
understanding other complex adaptive systems like the body, natural, physical and social.
3
Rajan (2019).
4
Coyle (2014).
5
For example, Helliwell, Layard and Sachs (2019), Ngamaba (2017) and Commission on the Measurement of Economic Performance
and Social Progress.
Like our bodies, the economy is also a complex, adaptive system. As in medicine, it can be measured using
different tools at different resolutions. There are various metrics of societal health, in addition to GDP. For
example, there has been increasing interest recently in measures of physical and mental health.6 A number
people's
of statistical agencies, including in the UK, now gather direct and indirect metrics of well-being.
Here we map a selection of those alternative metrics, based around health, wealth and happiness.
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A different sort of lens comes from viewing these metrics at different spatial resolutions regional, local
authority, postcode even. This allows a "micro-to-macro" jigsaw of the economy to be pieced together, as
with other complex systems. Patterns in these systems often self-replicate at different resolutions; they are
7
"fractal". One interesting question is whether economic systems also exhibit self-replicating patterns. If so,
this has implications for how we understand and model them.
We consider these questions using a sequence of maps. Many of these maps are not new. For example,
Philip McCann from the University's Productivity Insights Network (PIN) has published an excellent book
setting out a range of fascinating regional facts and maps. So too have others.8 These maps provide useful
context when testing the Tip O'Neill hypothesis
and when modelling and managing the economy.
To provide the clearest visual guide, these maps are scaled by economic rather than geographic size
- so-called cartograms.9 Each region is initially scaled to have the same geographic area and then rescaled
in line with economic differences. This means some areas shrink (such as Scotland) while others expand
(such as London) even without differences between them. This helps when visualising local differences.
(a) Differences between regions
To fix ideas, Charts 1a and 1b plot a map and an accompanying cartogram of population by region in the UK
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and, by way of comparison, Germany. It uses a resolution one-level below the national administrative
"regions" in England and each of Scotland, Wales and Northern
Ireland. Comparing the map and cartogram
suggests the UK has a significant demographic skew towards London and the South-East. Germany has
nothing like the same skew, with the cartogram and map little different.10
-
A key focus of work among economic geographers is the importance of agglomeration effects that is,
increasing returns to economic scale at the spatial level.11 If those effects are powerful, and if workers are
one of their most important sources, we would expect cartograms to have similar properties when looked at
by population, income (per head) and wealth (per household). More should not only be merrier but wealthier.
6
For example, Case and Deaton (2017).
7
For example, Viscek, Shlesinger and Matsushita (1994).
8
McCann (2016), Centre for Cities (2019), What Works Centre for Local Economic Growth (2018).
9
See Dougenik, Chrisman and Niemeyer (1985).
10
Put differently, the correlation between geographic area size and population in the UK is in fact slightly negative (-0.05) while it is
relatively high and positive (0.7) in Germany.
11
Viladecans-Marsal (2004) and Rosenthal and Strange (2003), example.
Chart 1a: Map (LHS) and cartogram (RHS) of population in the UK
Chart 1b: Map (LHS) and cartogram (RHS) of population in Germany
Sources: Eurostat and Bank calculations.
Notes: Charts show population in 2018 for NUTS 1 regions. Cartograms resized by population.
As Charts 2 and 3 show, broadly-speaking that is the case. The income per head cartogram suggests a
shrunken North and a swollen South. The gap between richest and poorest region is around 150%. For
wealth per household, the North-South divide is also large, with a gap between richest and poorest of 130%.
These are striking differences. But are they unusual compared either with the past or other countries?
Chart 2: Cartogram of UK income per head
Sources: Eurostat and Bank calculations.
Notes: Purchasing power standard (PPS) per inhabitant for NUTS 1 regions.
Chart 3: Cartogram of Great Britain wealth per household
Sources: ONS Wealth and Assets Survey and Bank calculations.
Notes: Data refer to 2014-16 for NUTS 1 regions, re-sized and coloured by median household wealth. No equivalent
data available for Northern Ireland.
On the first of those, Chart 4 plots the distribution of income per head across UK regions on three dates:
1997, 2007 and 2016. All of the distributions are centred on mean levels of income in 2016. This suggests a
modest increase in the degree of regional income dispersion over time. The 2016 distribution has somewhat
fatter tails, lower and upper, than in earlier periods.12
Chart 4: Distribution of UK income per head over time (mean-aligned)
Sources: ONS and Bank calculations.
Notes: Chart shows regional gross disposable income for different time periods at current basic prices, centred around
the 2016 mean. Observations for each region are weighted by the population of each region.
Chart 5 compares the regional distribution of income per head in the UK, France and Germany in 2016,
again centred on mean income levels in the UK in 2016. It suggests a materially wider regional dispersion in
incomes in the UK than in France and Germany. The gap between richest and poorest regions in the UK is
almost twice as large as in France and three-quarters larger than in Germany.13
12
Comparing NUTS 3 regions.
13
Figures refer to income differences at the NUTS1 regional level, using Purchasing Power Standard (PPS).
Chart 5: Distribution of UK, France and Germany income per head in 2016
Île de
France
Oberbayern Inner London -
West
Sources: Eurostat and Bank calculations.
Notes: Purchasing power standard (PPS) per household in NUTS2 regions for 2016 centred around the UK mean.
Excludes NUTS region 'FRY'
('Départements d'outre mer').
These regional differences in GDP are found in alternative metrics of economic health, such as pay
(Chart 6).14 They are also mirrored in medical health. Chart 7 plots one measure of medical health by region
-
average life expectancy. This mirrors regional income differences. Richer people tend to live longer. The
gap between highest and lowest life expectancies regionally is around 3.4 years, or 4.2% of the UK average.
14
Haldane (2019).
Chart 6: Cartogram of UK pay
Sources: ONS Annual Survey of Hours and Earnings (ASHE).
Notes: UK NUTS 1 regions re-sized and coloured by median gross weekly earnings (£) in 2018.
Chart 7: Cartogram of UK life expectancy
Sources: ONS and Bank calculations.
Notes: Life expectancy at birth by local area in the UK (years) for UK NUTS 1 regions, re-sized and coloured by mean
life expectancy in 2010-12.
- -
If we turn to an alternative measure of health psychological health the picture changes dramatically.
Chart 8 plots a subjective measure of well-being on a regional basis.15 This is the mirror image of the
income and wealth maps. Well-being is lowest in the richest region (London) and highest in a region where
incomes are low (Northern Ireland). Income may buy you many things, but happiness is not one of them.16
15
This is based on answers to the question: "Overall, how satisfied are you with your life nowadays?" Other questions regarding
wellbeing yield very similar conclusions.
16
Easterlin (1974) and Easterlin (2013), for example, point out that while wealthier people tend on average to be happier, happiness
does not increase in line with GDP per head. Unemployment and debt arrears also have a large adverse psychological impact on
individuals, above-and-beyond their direct impact on income and wealth (Wildman (2003) and Taylor et al (2007)).
While simple, these maps suggest there is important information on economies and societies to be found by
peering through different lenses: lenses that operate at different resolutions, as the striking UK regional
differences in income, wealth and health demonstrate; and lenses that focus on different economic
the UK's
attributes, as the striking differences between regional income and well-being maps demonstrate.
Chart 8: Cartogram of UK well-being
Sources: ONS Annual Population Survey (APS) and Bank calculations.
question, "overall, how satisfied are you with your life nowadays?" where 0 is 'not at all
Notes: Responses to the
satisfied' and 10 is 'completely satisfied' for 2016/17. UK NUTS 1 regions
re-sized and coloured by mean response.
(b) Differences within regions
Given these striking regional differences in health, wealth and happiness across the UK, an interesting
supplementary question is how those differences compare within-regions. As we alter the resolution at
which we view the economy, do these differences narrow? Do economic patterns look different when
comparing them within and between regions? Or are these patterns self-similar at different resolutions, as
when we use a microscope to study the shoreline?
These "violin" plots compare the
Chart 9 provides one visualisation of these patterns at different resolutions.
distribution of income per head at the regional (NUTS 1) level with the distribution at NUTS 2 and NUTS 3
level. If between-region income differences were much larger than within-region differences, we would
expect the violin to be much longer on its left than on its right hand side.
This is clearly not the case. The distributions of income within and between regions are in fact quite similar,
at both the NUTS 2 and 3 levels. If anything, the distribution of outcomes within each region appears to be a
little larger than the distribution between regions. If anything, income inequalities within a region appear to
be larger than income inequalities between regions.
'Violin' plot of
Chart 9: GVA per head
Sources: ONS and Bank calculations.
Notes: Regional gross value-added (GVA, income approach) per head of population at current basic prices for 2017 for
NUTS 1, 2 and 3, log-scale. Regional figures weighted by population. Dark bar shows interquartile range, dots show
median and circle shows mean.
Charts 10, 11 and 12 are violin plots for wages, health and well-being at higher resolutions. For health and
well-being, differences within a region or local authority are materially larger than differences between
regions. The standard deviation of health outcomes at the local authority level is almost 3 times larger than
at the regional level. The difference between the highest and lowest life expectancy among UK local
authorities is 12 years, or 15% of the average expected lifespan.
'Violin' plot of wages
Chart 10:
Sources: ONS Annual Survey of Hours and Earnings (ASHE) and Bank calculations.
Notes: Median gross wages in 2018. Regional figures weighted by number of jobs per region. Dark bar shows
interquartile range, dots show median and circle shows mean. No Northern Ireland wage data available at NUTS 3 level.
'Violin' plot of health
Chart 11: (life expectancy)
Sources: ONS and Bank calculations.
Notes: Life expectancy at birth for NUTS 1, 2 and 3 regions in 2010-12. Regional figures weighted by population in each
region. Dark bar shows interquartile range, dots show median and circle shows mean.
'Violin' plot of
Chart 12: well-being (life satisfaction)
Sources: ONS Annual Population Survey (APS) and Bank calculations.
to the question, "overall, how satisfied are you with your life nowadays?" for NUTS 1, 2 and 3 where
Notes: Responses
0 is 'not at all satisfied' and 10 is 'completely satisfied' for 2016/17. Regional figures weighted by population in each
region.
Chart 13 plots health perceptions by electoral ward in four big UK cities: Belfast, Cardiff, Edinburgh and
(inner) London.17 The differences are big, with nearly twice as many people perceived to be in bad or very
same
bad health in Belfast than Edinburgh. Even more striking are the differences in the city, however, with
adjacent districts reporting bad health differences of between 3% and 25%.
17
Data refer to percentage of people in the census rating themselves in "bad" or "very bad" health.
Chart 13: Heat maps of health in Belfast, Cardiff, Edinburgh and Inner London
Sources: 2011 Census.
Notes: Data show percentage of respondents declaring themselves to be in bad or very bad health at electoral ward
level.
I draw two conclusions from these micro-level visualisations. First, however striking the regional differences
in economic and societal health across the UK relative to historical and international standards, these
conceal even more striking differences in levels of health, wealth and happiness within regions. To the
extent there is an inequality issue in the UK, it is as much or more a local rather than regional one.
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A second, nerdier, point is what this means for our understanding the economy. Many systems social,
natural and physical - exhibit a pattern in which the distribution of outcomes does not converge at higher
resolutions; it remains constant or even expands. These systems have a fractal property with patterns that
are self-similar at different levels of resolution. Snowflakes and shorelines are the best-known examples.
The violins charts suggest economic systems may also exhibit some of these properties, with distributions
which do not converge at higher resolutions and may even expand. Outcomes in these systems are not
distributed like a bell-shaped, Normal distribution. Instead, outcomes are drawn from a Power Law
distribution, which has fatter tails and a much greater probability of extreme outcomes.18
Chart 14 plots the distribution of health, wealth and happiness across the UK. The straight line shows where
outcomes would lie if they were Normally-distributed. Deviations from that line, at its extremities, signify
fat-tails. For health and income there is strong evidence of fat tails and Power law distributions.19
Chart 14: Power Law tests
GVA per head Well-being
18
Haldane (2012) discusses Power Law distributions as they apply to economic and financial variables.
19
Haldane (2012).
Wages Health (life expectancy)
Sources and notes: as per violin plots in Charts 9-12.
Fat-tails and Power Law distributions are associated with systems that are complex and adaptive. The best
"one made up of a large
definition of complexity is, for me, the one provided by Herbert Simon back in 1962:
way".20
number of parts that interact in a non-simple That description fits our maps rather well. They are
certainly made up of a large number of distinct parts, even within geographically-concentrated areas.
If the economic system is complex, in the sense of Simon, this carries important implications for
understanding and modelling these systems. As with the body, to understand the dynamics of a complex
system it is crucial you study the micro-level moving parts. It is equally important to understand interactions
among these parts. What do we know about interactions among the economy's moving parts?
(c) Movements between regions
In economic systems, interactions occur among all of the factors involved in production and consumption
-
decisions people, money, goods and services. Capturing flows of these factors, and their interactions, is
crucial for understanding how a complex economic system will function. Below I consider flows of money,
goods and services. Let me start by considering flows of people.
Chart 15 plots migration in and out of various regions in 2016-17, expressed as a proportion of the regional
population. Even gross inflows and outflows are relatively modest, varying between 4% (London) and 1⁄2%
(Northern Ireland). Net flows of people are even more modest, in all cases bar London averaging less than
0.6%. One of the reasons regions are distinct may be because of their ring-fenced populations.
20
Simon (1962).
Chart 15: Migration inflows and outflows between different UK regions
Sources: ONS Local Area Migration Indicators and Bank calculations.
Notes: Data refer to mid-2016 to mid-2017, as a percentage of 2017 population.
Of course, people living in one region can still commute into another for work purposes. Table 1 looks at
commuting destinations across UK regions, as a percentage of each region's population. With the exception
of London and the South-East, worker flows from outside of the home region are modest, averaging only 5%
of the working population. Even when looking at flows of workers, most regions are self-contained and local.
Table 1: Commuting flows between UK regions (percentage of workers resident in each region)
Sources: Nomis and Bank calculations.
Notes: Percentage of each region's workers commuting from each residential region.
These flow data provide a useful lens on local economic eco-systems. As an example, Chart 16 plots the
the UK's regions.
time to get to work (in minutes) against the distance to work (in km) across Most regions
outside London and the South-East are tightly clustered, with an average commute time of 27 minutes and
distance of 16 km. Consistent with other evidence, this suggests most regions are local economically.
Chart 16: Distance vs. time to work
Sources: ONS Labour Force Survey, 2011 Census and Bank calculations.
Notes: Average distance travelled (km) and usual time (minutes) from home to work by NUTS 1 region in England and
Wales.
The situation is somewhat different in London. The average commute distance is materially shorter (11km)
but the average travel time almost doubles at (40 minutes). A cartogram of commute times makes this point
strikingly (Chart 17). Contrary to popular belief, London is life in the slow lane. As we know commuting often
people's
detracts from well-being, this cartogram perhaps explains why Londoners are richer but more
miserable.21
21
For example, Office for National Statistics (2014) and Novaco and Gonzalez (2009).
Chart 17: Cartogram of UK travel time to work
Sources: ONS Labour Force Survey, 2011 Census and Bank calculations.
Notes: UK local authorities re-sized and coloured by mean travel to work time (minutes).
Modelling the Economy
How can these micro-level data help when modelling the economy? Economists and policymakers typically
use highly-aggregated models which operate at an economy-wide level, often comprising a small number of
"representative"
agents. In their most aggregated versions, there is a single representative household and
company. The most widely-used is the Representative Agent New Keynesian (RANK) model.22
RANK-type models have real virtues. They are underpinned by an optimising view of individual human
to be They are
behaviour and, in this sense, can be said "micro-founded". explicitly identified, in the
statistical sense, and parsimonious. These characteristics mean these models can often offer deep insight
into comparative static questions about the impact of changes in the deep parameters of an economy, such
as shifts in risk-aversion among households or in levels of technology among firms.23
There are also, of course, costs of simplicity. The most serious is the inability of these models often to fit the
macro-economic facts at business cycle frequencies, particularly at times of economic stress.24 RANK
models fared poorly in capturing the dynamics of the economy either side of the global financial crisis. At the
very time it was most interesting and important, these models were found wanting as a guide to the fortunes
of the economy and economic policy.
One response has been to develop models that relax the representative agent assumption. So-called
Heterogeneous Agent New Keynesian (HANK) models comprise distinct agents with distinct behaviours. As
25
importantly, these models capture interactions among agents. While it is heterogeneity that is often
emphasized, it is interactions between agents that is most important for replicating real-world dynamics.
Consider the dynamics of two very simple systems: a single and double pendulum. The single pendulum is
a representative agent model. Once disturbed, its motion is regular and its distribution normal
(Figure 1, LHS). The double pendulum involves two identical representative agents which interact. Its
dynamics are complex and highly non-linear and it is Power Law distributed (Figure 1, RHS). Even simple
interactions among representative agents generate complex system dynamics.
22
For example, Galí (2015).
23
For example, Kydland and Prescott (1982).
24
Haldane (2016), Haldane and Turrell (2018).
25
For example, Kaplan, Moll and Violante (2018).
Figure 1 : Single and double pendulum
Single pendulum Double pendulum
Sources: https://codepen.io/anon/pen/XQLNaZ
HANK-type models do a better job of capturing heterogeneity across agents than their RANK cousins. The
number of interactions among their internal moving parts is nonetheless heavily constrained. Certainly, they
do not come close to capturing the granularity, spatially or sectorally, that exists in real-world economic
systems. Are there alternative modelling approaches that capture more of these moving, interacting parts?
Agent-Based Models (ABMs) use a larger array of distinct agents and permit a far-larger scale of interaction
-
among them.26 They have been used extensively to model complex systems natural systems (rainforests
and oceans), physical systems (electricity grids and war zones) and social systems (swarms of birds and
bees).27 These are systems where it is recognised that an understanding of interactions among the internal
behaviour of the system as a whole, in line with Simon's dictum.
moving parts is crucial for capturing
For economists used to dealing with highly-aggregated models, the very dimensionality of ABMs is striking.
The largest in physics can now simulate interactions of up to 400 billion distinct particles at any one time.28
That is around 50 times as many agents as there are humans on the planet. By way of comparison, the
largest-scale macro-models would typically have fewer than 10 distinct agent types.
ABMs have been used to study the economy, albeit not very extensively.29 The Bank has recently used
them to model the housing and corporate bond markets, both of which exhibit non-linear dynamics due to
30
complex interactions among agents. These models have also been used to run hypothetical policy
simulations, in particular for macro-prudential policies.31 These models are no free lunch. They pose difficult
choices, such as where to strike the balance between realism (complexity) and parsimony (simplicity).
26
Farmer (2019) discusses agent-based models in the context of complexity economics.
27
For example, Ge et al (2018), Ge and Polhill (2016) and Heppenstall et al (eds.) (2012).
28
https://arxiv.org/abs/1112.1754
29
For example, Cristelli, Pietronero and Zaccaria (2011) and Napoletano, Gaffard and Babutsidze (2012).
30
Baptista et al (2016) and Braun-Munzinger, Liu and Turrell (2016).
31
Baptista et al (2016).
We typically define models as simplified abstractions of reality, partly for reasons of practicality given
humans' limited capacity to track too many moving parts. But computers these days can keep track far
better than any human. If the largest ABMs have a dimensionality greater than our population, why not
model the economy at its highest possible resolution? Why be abstract if you can be exact? That would be
a very different conception of the term "micro-founded".
This may sound like science fiction. Yet we already have practical examples of just that, starting at the
-
highest level of resolution the firm. In 1956, Jay Wright Forrester, an electrical engineer, switched from
devising systems for detecting incoming Soviet nuclear bombs to designing systems for understanding
production problems in companies. He developed the first computer models of how companies operated. In
the discipline that would become "system dynamics".32
so doing, he founded
In line with Forrester's
initial insight, one of the most interesting developments recently when understanding
the dynamics of companies is the creation of so-called "digital twins".33 These are a DNA-level digital clone
of a company, tracing the interactions among its moving parts. A digital twin is a high-resolution, dynamic
model of a firm-level micro-economy that can be used both to understand and to simulate a company's
operations, however complex. Their use among frontier companies is increasingly commonplace.
The data necessary to calibrate these digital twins is considerable, but also increasingly commonplace.34
Twenty years ago, the large American retailer Walmart created a system allowing them, and their suppliers,
to monitor their complex supply-chain in close to real time.35 This transformed inventory management at
Walmart and its suppliers. Twenty years on, these real-time data systems are rapidly becoming the norm.
At the next level of resolution, could a digital twin be created at a town or city level? Having started with the
firm, Jay Wright Forrester's next project was to simulate a
city, in an attempt to understand and help solve
the US's "urban crisis" of the 1960s. Forrester developed DYNAMO, a computer-simulated model of a city
comprising 150 equations and 200 parameters. His book Urban Dynamics, published in 1969, set out how
these computer models could be used to understand the evolution of cities.36
Reading Forrester's book in the 1980s, video-game developer Will Wright had an idea for creating a game
involving building a dynamic model of a city. The result was SimCity (Figure 2). This quickly became one of
the most popular video games of its era. Computer-simulated video games quickly came to be used to solve
real-world city planning problems. Subsequently, ABM-like models of simulated cities have found
widespread use for urban planning and development.37
32
For example, Karnopp, Margolis and Rosenberg (1990).
33
Shaw and Fruhlinger (2019).
34
Bolton et al (2018) have discussed the possible guiding values for a national digital twin framework as part of work on behalf of the
Centre for Digital Built Britain.
35
Hardgrave et al (2006).
36
Forrester (1969).
37
For example, Motieyan and Mesgari (2018).
Figure 2: