Economische Wetenschap na Tinbergen in een complexe wereld · RelatiemetTinbergen...

Economische Wetenschap na Tinbergenin een complexe wereld

Cars Hommes

Universiteit van Amsterdam

Alumni LezingUvA Kringen Amsterdams Economen & Andragologie

UvA, 17 Mei, 2018

Cars Hommes (UvA) UvA Alumni Lezing 17 Mei, 2018 1 / 40

Relatie met TinbergenJ. Tinbergen, Zeitschrift für Nationalökonomie, April 30, 1930


Relatie met Tinbergen

Common featuresmathematical modelsempirical relevancepolicy analysis

New features:behavioural modelingcomplex systems approach


Main Themes of the Talk

Economy as a Complex Evolving Systemsimple behavioral agent-based models with heterogeneousagentsEmpirical Validation

stock markethousing market

Laboratory Macro ExperimentsPolicy implications: how to manage complex socio-economicsystems?


Introduction

Literature

Hommes, C.H. (2013), Reflexivity, expectations feedback and almos self-fulfillingequilibria: economic theory, empirical evidence and laboratory experiments, Journalof Economic Methodology special issue on Reflexivity and Economics: George Soros’Theory of Reflexivity and the Methodology of Economic Science, 406-419.Battiston, S., Farmer, J.D., Flache, A., Garlaschelli, D., Haldane, A.G., Heesterbeek,H., Hommes, C.H., Jaeger, C., May, R. and Scheffer, M. (2016), Complexity theoryand financial regulation. Economic policy needs interdisciplinary network analysisand behavioral modeling, Science Vol. 351, 6275, 818-819.Hommes, C.H., (2013), Behavioral Rationality and Heterogeneous Expectations inComplex Economic Systems, Cambridge.

Behavioral Rationality and Heterogeneous Expectations

in Complex Economic Systems

Cars Hommes

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Cover designed by Hart McLeod Ltd

Behavioral Rationality and Heterogeneous Expectations

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Introduction

Traditional Rational View in Economics

representative, average agent, who is perfectly rationalexpectations are model consistentFriedman hypothesis: “irrational agents will lose money andwill be driven out the market by rational agents”simple (linear), stable model, driven by exogenous randomnews about fundamentals (crisis ≡ large shock)prices reflect economic fundamentals (market efficiency)Lucas: macroeconomic policy should be based on rationalexpectations


Introduction

Alternative Complexity ApproachBehavioural Agent-Based Models

heterogeneous agents, heterogeneous beliefsmarket psychology, herding behavior (Keynes (1936))bounded rationality (Simon (1957))markets as complex adaptive, nonlinear evolutionary systemsinteractions of agents’ individual (micro) decision rules createemergent aggregate (macro) structure explaining observedstylized factsnonlinear and critical transitions:small changes at micro-level may lead to large and irreversiblechanges at macro levelpolicy: avoid “dark corners” and undesirable critical transitions


Introduction

Examples of Complex Systems


Introduction

Challenges for Complexity Agent-based Approach

how to model non-rational agents?in economics the “particles can think”need a theory of adaptive behaviour and learning‘wilderness’ of bounded rationalitymany degrees of freedom for heterogeneitywhat exactly causes the outcome in a (large) computational HAM


Introduction

How to Discipline Bounded Rationality?

stylized agent-based modelsbehavioral rationality –behavioral consistency:simple heuristics that work reasonably wellevolutionary selection (‘survival of the fittest’) andreinforcement learninglaboratory experiments to study group behaviour:simultaneous test of individual decision rules and aggregate macrobehavior


simple ABM

Simple 2-type Behavioural Switching ModelBrock-Hommes, 1997,1998

two types financial market model:fundamentalists believe price will return to RE benchmarkchartists believe price will follow past trends

reinforcement learning / evolutionary selection:investors gradually switch to better performing strategy


simple ABM

Rational Fundamental Benchmark

standard asset pricing equilibrium:(common beliefs on future dividends Et[yt+1])

pt =1

REt[pt+1 + yt+1], R = 1 + r

strong positive feedback: 1/R ≈ 1

unique rational fundamental solution p∗t :(discounted sum expected future cash flow)

p∗t =Et[yt+1]

R+Et[yt+2]

R2+ · · ·

For special case of IID dividends, with Et[yt+1] = y:

p∗ =y

R− 1=y

r


simple ABM

Model in deviations from fundamental

deviation from fundamental

xt = pt − p∗

homogenous benchmark model in deviations:

xt =1

REtxt+1

Behavioural model with heterogeneous beliefs:(in deviations from RE-fundamental)

xt =1

R

H∑h=1

nhtEhtxt+1

strong positive feedback: weak mean reversion


simple ABM

Evolutionary selection of strategiesBrock and Hommes, Econometrica 1997

evolutionary selection or reinforcement learning:more successful strategies attract more followers

fractions of belief types are gradually updated in each period:(discrete choice model with asynchronous updating)

nht = δnh,t−1 + (1 − δ)eβUh,t−1

Zt−1

where Zt−1 is normalization factor.Uht fitness measure (e.g. realized profits)β is intensity of choice.δ asynchronous updating


simple ABM

Bubble and Crash Dynamics due to switchingbetween Fundamentalists versus ChartistsBrock and Hommes, JEDC 1998

Fundamentalists can not drive out chartistsdriven by short run profitability


simple ABM

Empirical Validation Two type model

Two trader types, with forecasting rules

f1t = φ1xt−1, 0 ≤ φ1 < 1 fundamentalistsf2t = φ2xt−1, φ2 > 1, trend extrapolators

xt =1

R[n1tφ1xt−1 + (1 − n1t)φ2xt−1] + εt

φt =ntφ1 + (1 − nt)φ2

Rmarket sentiment

φt < 1: mean reversion;φt > 1: explosive, trend following


simple ABM

2-type Behavioural Switching ModelHommes and in’t Veld, 2017

excess volatility: the stock market fluctuates much more than therational fundamental price p∗ based on dividends


simple ABM

Bubbles and Crashesdue to time-varying Market Sentiment

fractionfundamentalists

market sentiment

temporary bubbles and crashes: triggered by fundamentals andstrongly amplified by trend-following behaviour


simple ABM

Bubbles and Crashes in Housing Marketsjoint with DNB in NWO Comlexity program

Time

pric

e in

dex

1970 1980 1990 2000 2010

100

120

140

160

real house pricefundamental real house price

US

Time

pric

e in

dex

1970 1980 1990 2000 2010

100

150

200

300


NL

Time

pric

e in

dex

1970 1980 1990 2000 2010

100

200

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500


JP

1

Outline of the NWO strategic theme

Dynamics of complex systems

Netherlands Organisation for Scientific Research

Complexity

Time

X

1970 1980 1990 2000 2010

−0.

50.

00.

51.

0

US

Time

X

1970 1980 1990 2000 2010

−0.

50.

00.

51.

0

NL

Time

X

1970 1980 1990 2000 2010−

0.5

0.0

0.5

1.0

JP


simple ABM

Persistent Bubbles and Crashes in Housing MarketsBolt, Demertzis, Diks, Hommes and van der Leij, 2017

−0.

10.

00.

10.

20.

3

X

0.0

0.2

0.4

0.6

0.8

1.0

n 1

1970 1980 1990 2000 2010

0.90

0.95

1.00

1.05

Time

AR

(1)

US

−0.

20.

00.

20.

40.

6

X

0.0

0.2

0.4

0.6

0.8

1.0

n 1

1970 1980 1990 2000 2010

0.94

0.98

1.02

1.06

Time

AR

(1)

NL

−0.

20.

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20.

4

X

0.0

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n 1

1970 1980 1990 2000 2010

0.95

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Time

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(1)

JP

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Complexity


Experiments

Laboratory Experiments in Macro and Finance

laboratory test for (simple) complex systems;study individual (micro) as well as aggregate (macro) behaviorin controlled laboratory environmentempirical foundation for individual decision rules foragent-based models ABMs to discipline wilderness of boundedrationalitylaboratory test for policy analysis;test policies in more realistic controlled lab environment


Experiments

Learning to Forecasts Laboratory ExperimentsRepeated Keynesian Beauty Contest Game

individuals only have to forecast prices, ceteris paribus,with all other behavior computerized by theory

price depends on average forecast: pt = f(pet+1)

Round Prediction Real value

1 33,70 50,232 33,70 56,633 37,00 65,324 40,10 65,005 43,50 66,126 50,00 64,537 48,35 58,358 38,70 42,359 30,10 40,01

10 28,25

Total Earnings Remainingearnings: this period: time:

10357 1298 00

What is your prediction Prediction:this period?

Your prediction mustbe between 0 and 100

0102030405060708090

100

1 6 11 16 21 26 31 36 41 46

prediction

real number

Round

Number


Experiments

Financial Market Experimental Setting

asset pricing experiment (with/without robot trader)two-period ahead

positive feedback

mean dividend y = 3 and interest rate r = 0.05 are knownrational fundamental price pf = y/r = 60 not known(but can be computed)

Repeated Keynes’ Beauty Contest

pt =1

1 + r

((1 − nt)

pet+1,1 + · · · + pet+1,6

6+ nt p

f + y + εt

)


Experiments

Examples Simulation Benchmark Models

anchor and adjustment trend-following rule

pet+1 =60 + pt−1

2+ (pt−1 − pt−2)


Experiments

Financial Market Experiment (with Robot Trader)6 different markets

40

45

50

55

60

65

70

Pric

e

Group 2

fundamental price experimental price

40

45

50

55

60

65

70

Group 5


40

45

50

55

60

65

70

Pric

e

Group 1


40

45

50

55

60

65

70

Group 6


10 20 30 40 50 60 70 80 90

0 10 20 30 40 50

Pric

e

Group 4


40

45

50

55

60

65

70

0 10 20 30 40 50

Group 7



Experiments

Financial Market ExperimentStrong coordination of individual forecasts and errors

35

45

55

65

0 10 20 30 40 50

Pred

ictio

ns

45

55

65

Pric

e

Group 2

-2 0 2

35

45

55

65

0 10 20 30 40 50

Pred

ictio

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45

55

65

Pric

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Group 1

-5 0 5

10 30 50 70 90

0 10 20 30 40 50

Pred

ictio

ns

10

30

50

70

90

Pric

e

Group 4

-30 0

30


Experiments

Summary Results Asset Pricing Experiment

Results are inconsistent with rational, fundamental forecasting

One would like to explain:three qualitatively different patters

(almost) monotonic convergence

constant oscillations

damping oscillations

coordination of agents in their predictions

no homogeneous expectations model fits theseexperimentsneed heterogeneous expectations model


Experiments

Examples of Individual Predictions and Switching

48

50

52

54

56

58

60

0 10 20 30 40 50

Pric

e, P

red

icti

on

s

Group 2, participants 1 and 5

prediction 5prediction 1

price

52

54

56

58

60

62

64

66

0 10 20 30 40 50

Group 6, participant 1

prediction price

48

50

52

54

56

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64

66

0 10 20 30 40 50

Pric

e, P

red

icti

on

Time


prediction price

40

45

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60

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75

0 10 20 30 40 50

Time


prediction price


Experiments

Heterogeneous ExpectationsHeuristics Switching ModelAnufriev and Hommes, AEJ:Micro 2012

agents choose from a number of simple forecasting heuristics

performance based reinforcement learning:(extension of Brock and Hommes, Econometrica 1997)agents evaluate the performances of all heuristics, and tend toswitch to more successful rules; impacts are evolving over time


Experiments

Heuristic Switching Model: four forecasting heuristicsAnufriev and Hommes, AEJ:Micro 2012

adaptive expectations rule, [w = 0.65]

ADA pe1,t+1 = 0.65 pt−1 + 0.35 pe1,t

weak trend-following rule, [γ = 0.4]

WTR pe2,t+1 = pt−1 + 0.4 (pt−1 − pt−2)

strong trend-following rule, [γ = 1.3]

STR pe3,t+1 = pt−1 + 1.3 (pt−1 − pt−2)

anchoring and adjustment heuristic with learnable anchor

LAA pe4,t+1 = 12

(pavt−1 + pt−1

)+ (pt−1 − pt−2)


Experiments

Switching Model fitted to Experimental Data

35

45

55

65

0 10 20 30 40 50

Pred

ictio

ns

ADA WTR STR LAA 45

55

65

Pric

e

Group 5

simulation experiment

-2

0

2

35

45

55

65

0 10 20 30 40 50

Pred

ictio

ns

ADA WTR STR LAA 45

55

65

Pric

e

Group 6


-5

0

5 45

55

65

75

0 10 20 30 40 50

Pred

ictio

ns

ADA WTR STR LAA

45

55

65

75

Pric

e

Group 7


-10

0

10

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Fractions of 4 rules in the simulation for Group 5

ADA WTR STR LAA

0

0.2

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0.6

0.8

1

0 10 20 30 40 50


ADA WTR STR LAA

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50


ADA WTR STR LAA


Experiments

More Financial Market Experiments

without RE robot trader large group size (n = 26)

0

200

400

600

800

1000

0 10 20 30 40 50

gr 1gr 2gr 3gr 4gr 5gr 6

Does positive feedback cause instability?


Experiments

Positive versus Negative Feedback ExperimentsRepeated Beauty Contest Games

negative feedback (strategic substitute environment)

pt = 60 − 20

21[

6∑h=1

1

6peht] − 60] + εt

positive feedback (strategic complementarity environment)

pt = 60 +20

21[

6∑h=1

1

6peht − 60] + εt

common feature: same RE equilibrium 60

only difference: sign in the slope of linear map +0.95 vs −0.95


Experiments

Feedback Mappings in LtFE

negative feedback positive feedback

20 40 60 80 100 120Prediction

20406080100120Price

20 40 60 80 100 120Prediction

20406080100120Price

pt = 60 − 2021

(pet − 60

)+ εt pt = 60 + 20

21

(pet − 60

)+ εt

My main concern with macroeconomics:rational expectations ignores almost self-fulfilling equilibria inpositive feedback systems


Experiments

Negative vs. Positive Feedback ExperimentsPrices, Individual Predictions and Errors; (Heemeijer et al., JEDC 2009)

0

20

40

60

80

100

0 10 20 30 40 50

Pred

ictio

ns

20

40

60

80

Pric

e

NEGATIVE

-3 0 3

0

20

40

60

80

100

0 10 20 30 40 50

Pred

ictio

ns

20

40

60

80

Pric

e

POSITIVE

-3 0 3

Positive Feedback: coordination on “wrong” non-RE price;coordination on almost self-fulfilling equilibria


Experiments

Prices in Experiments with Positive/Negative Feedback(7/6 groups)

negative feedback positive feedback

20

40

60

80

0 10 20 30 40 50

Pri

ce

Time

20

40

60

80

0 10 20 30 40 50Pr

ice

Time

convergence to REalmost self-fulfilling

equilibria


Experiments

Positive vs Negative Feedback; Small ShocksHeuristics Switching Model Simulations

prices strategy frequencies

0

20

40

60

80

100

0 10 20 30 40 50

Price


0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Impacts of Heuristics

ADA WTR STR LAA

0

20

40

60

80

100

0 10 20 30 40 50

Price


0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Impacts of Heuristics

ADA WTR STR LAA

positive feedback: trend-followers amplify fluctuationsCars Hommes (UvA) UvA Alumni Lezing 17 Mei, 2018 37 / 40

Experiments

Managing Positive Feedback through Negative FB PolicyHousing Market Experiments, Bao and Hommes, 2017

no FB policylarge bubble

(λ = 0.95; r = 5%)

weak negativeFB policy

oscillations(λ = 0.85; r = 18%)

strong negativeFB policystable

(λ = 0.71; r = 40%)

0 10 20 30 40 500

100

200

300

400

500

600

700

800

900

1000

Period

Market N2

Market PriceSimulation

0 10 20 30 40 500

20

40

60

80

100

120

Period

Market L1


0 10 20 30 40 500

20

40

60

80

100

120

Period

Market H1


adding negative FB stabilizes complex positive FB system

Note: policy under RE: do not interfereCars Hommes (UvA) UvA Alumni Lezing 17 Mei, 2018 38 / 40

Science, February 19, 2016Policy Dashboard based on Complexity Theory

INSIGHTS | PERSPECTIVES

sciencemag.org SCIENCE818 19 FEBRUARY 2016 • VOL 351 ISSUE 6275

By Stefano Battiston,1* J. Doyne Farmer,2,3

Andreas Flache,4 Diego Garlaschelli,5

Andrew G. Haldane,6 Hans Heesterbeek,7

Cars Hommes,8,9† Carlo Jaeger,10,11,12

Robert May,13 Marten Scheffer14

Traditional economic theory could not

explain, much less predict, the near

collapse of the financial system and its

long-lasting effects on the global econ-

omy. Since the 2008 crisis, there has

been increasing interest in using ideas

from complexity theory to make sense of eco-

nomic and financial markets. Concepts, such

as tipping points, networks, contagion, feed-

back, and resilience have entered the finan-

cial and regulatory lexicon, but

actual use of complexity models

and results remains at an early

stage. Recent insights and techniques offer

potential for better monitoring and manage-

ment of highly interconnected economic and

financial systems and, thus, may help antici-

pate and manage future crises.

TIPPING POINTS, WARNING SIGNALS. Fi-

nancial markets have historically exhibited

sudden and largely unforeseen collapses, at

a systemic scale. Such “phase transitions”

may in some cases have been triggered by

unpredictable stochastic events. More of-

ten, however, there have been endogenous

underlying processes at work. Analyses of

complex systems ranging from the climate

to ecosystems reveal that, before a major

transition, there is often a gradual and un-

noticed loss of resilience. This makes the sys-

tem brittle: A small disruption can trigger a

domino effect that propagates through the

system and propels it into a crisis state.

Recent research has revealed generic em-

pirical quantitative indicators of resilience

that may be used across complex systems to

detect tipping points. Markers include rising

correlation between nodes in a network and

rising temporal correlation, variance, and

skewedness of fluctuation patterns. These

indicators were first predicted mathemati-

cally and subsequently demonstrated experi-

mentally in real complex systems, including

living systems (1). A recent study of the

Dutch interbank network (2) showed that

standard analysis using a homogeneous net-

work model could only lead to late detection

of the 2008 crisis, although a more realistic

and heterogeneous network model could

identify an early warning signal 3 years be-

fore the crisis (see the first chart) (Fig. 1).

Ecologists have developed tools to quan-

tify the stability, robustness, and resilience

of food webs and have shown how these

depend on the topology of the network and

the strengths of interactions (3). Epidemi-

ologists have tools to gauge the potential for

events to propagate in systems of interacting

entities, to identify superspreaders and core

groups relevant to infection persistence, and

to design strategies to prevent or limit the

spread of contagion (4).

Extrapolating results from the natural

sciences to economics and finance presents

challenges. For instance, publication of an

early warning signal will change behav-

ior and affect future dynamics [the Lucas

critique (5)]. But this does not affect the

case where indicators are known only to

regulators or when the goal is to build bet-

ter network barriers to slow the spread of

contagion.

TOO CENTRAL TO FAIL. Network effects

matter to financial-economic stability be-

cause shock amplification may occur via

strong cascading effects. For example, the

Bank of International Settlements recently

developed a framework drawing on data on

the interconnectedness between banks to

gauge the systemic risk posed to the finan-

cial network by Global Systemically Impor-

tant Banks. Recent research on contagion in

financial networks has shown that network

topology and positions of banks matter; the

global financial network may collapse even

when individual banks appear safe (6). Cap-

turing these effects is essential for quanti-

fying stress on individual banks and for

looking at systemic risk for the network as

a whole. Despite on-going efforts, these ef-

fects are unlikely to be routinely considered

anytime soon.

Information asymmetry within a net-

work—e.g. where a bank does not know

about troubled assets of other banks—can

be problematic. The banking network typi-

cally displays a core-periphery structure,

with a core consisting of a relatively small

number of large, densely interconnected

banks that are not very diverse in terms of

business and risk models. This implies that

core banks’ defaults tend to be highly cor-

related. That, in turn, can generate a collec-

tive moral hazard problem (i.e., players take

on more risk, because others will bear the

costs in case of default), as banks recognize

that they are likely to be supported by the

authorities in situations of distress, the like-

lihood amplifies their incentives to herd in

the first place.

Estimating systemic risk relies on granu-

lar data on the financial network. Unfortu-

nately, business interactions between banks

are often hidden because of confidentiality

issues. Tools being developed to reconstruct

networks from partial information and to

estimate systemic risk (7) suggest that pub-

licly available bank information does not al-

low reliable estimation of systemic risk. The

estimate would improve greatly if banks

publicly reported the number of connec-

tions with other banks, even without dis-

closing their identity.

In addition to data, understanding the ef-

fects of interconnections also relies on in-

tegrative quantitative metrics and concepts

that reveal important network aspects, such

as systemic repercussions of the failure of

individual nodes. For example, DebtRank,

which measures the systemic importance

of individual institutions in a financial net-

work (8), shows that the issue of too-central-

to-fail may be even more important than

too-big-to-fail.

COMPLEX SYSTEMS

Complexity theory and financial regulationEconomic policy needs interdisciplinary network analysis and behavioral modeling

“…policies and financial regulation…are successful in stabilizing experimental macroeconomic systems’’

1Department of Banking and Finance, University of Zurich, 8032 Zürich, Switzerland. 2Institute for New Economic Thinking, Oxford Martin School, and Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK. 3Santa Fe Institute, Santa Fe, NM 87501, USA. 4Department of Sociology, University of Groningen, 9712 TG Groningen, Netherlands. 5Lorentz Institute for Theoretical Physics, University of Leiden, 2311 EZ Leiden, Netherlands. 6Bank of England, London, EC2R 8AH, UK. 7Faculty of Veterinary Medicine, University of Utrecht, 3512 JE Utrecht, Netherlands. 8Amsterdam School of Economics, University of Amsterdam, 1018 WB Amsterdam, Netherlands. 9Tinbergen Institute, 1082 MS Amsterdam, Netherlands. 10Beijing Normal University, 100875 Beijing, China. 11Potsdam University, 14469 Potsdam, Germany. 12Global Climate Forum 10178 Berlin, Germany. 13Department of Zoology, University of Oxford, Oxford OX1 2JD, UK. 14Environmental Sciences, Wageningen University 6708 PB Wageningen, Netherlands. *Authors are in allphabetical order. †Corresponding author. E-mail: [email protected]

POLICY

SCIENCE sciencemag.org 19 FEBRUARY 2016 • VOL 351 ISSUE 6275 819

ILL

US

TR

AT

ION

: C

. S

MIT

H/SCIENCE

AGENTS AND BEHAVIOR. Agent-based

models (ABMs) are computer models in

which the behavior of agents and their in-

teractions are explicitly represented as de-

cision rules mapping agents’ observations

onto actions. Although ABMs are less well

established in analyzing financial-economic

systems than in, e.g., traffic control, epide-

miology, or battlefield conflict analyses, they

have produced promising results. Axtell (9)

developed a simple ABM that explains more

than three dozen empirical properties of

firm formation without recourse to external

shocks. ABMs provide a good explanation

for why the volatility of prices is clustered

and time-varying (10) and have been used

to test systemic risk implications of reforms

developed by the Basel Committee on Bank-

ing Supervision, which show how dynami-

cally changing risk limits can lead to booms

and busts in prices (11, 12). ABMs of market

dynamics can be linked with ABM work on

opinion dynamics in the social sciences (13)

to understand how propagation of opinions

through social networks affects emergent

macro behavior, which is crucial to manag-

ing the stability and resilience of socioeco-

nomic systems.

Laboratory experiments with human

subjects can provide empirical validation

of individual decision rules of agents, their

interactions, and emergent macro behav-

ior. Recent experiments studying behavior

of a group of individuals in the laboratory

show that economic systems may deviate

significantly from rational efficient equi-

librium at both individual and aggregate

levels (14). This generic feature of positive

feedback systems leads to persistent devia-

tions of prices from equilibrium and emer-

gence of speculation-driven bubbles and

crashes, strongly amplified by coordination

on trend-following and herding behavior

(15). There is strong empirical evidence of

these behaviors in financial markets in prac-

tice, and these controlled laboratory experi-

ments provide more detailed understanding

of mechanisms, causality, and conditions for

emergence of macro phenomena.

A simple behavioral model, with agents

gradually switching to better performing

heuristics, explains individual, as well as

emergent, macro behavior in these laboratory

economies. The experiments also provide

a general mechanism for managing social

contagion in such systems. For example,

monetary and fiscal policies and financial

regulation, designed to weaken positive feed-

back, are successful in stabilizing experimen-

tal macroeconomic systems when properly

calibrated (16). Complexity theory provides

mathematical understanding of these effects.

POLICY DASHBOARD. It is an opportune

time for academic economists, complexity

scientists, social scientists, ecologists, epi-

demiologists, and researchers at financial

institutions to join forces to develop tools

from complexity theory, as a complement

to existing economic modeling approaches

(17). One ambitious option would be an on-

line, financial-economic dashboard that in-

tegrates data, methods, and indicators. This

might monitor and stress-test the global so-

cioeconomic and financial system in some-

thing close to real time, in a way similar to

what is done with other complex systems,

such as weather systems or social networks.

The funding required for essential policy-

relevant and fundamental interdisciplinary

progress in these areas would be trivial com-

pared with the costs of systemic financial

failures or the collapse of the global finan-

cial-economic system. ■

REFERENCES AND NOTES

1. M. Scheffer et al., Science 338, 344 (2012). 2. T. Squartini et al., Sci. Rep. 3, 3357 (2013). 3. R. M. May et al., Nature 451, 893 (2008). 4. H. Heesterbeek et al., Science 347, aaa4339 (2015). 5. R. E. Lucas Jr., Carnegie-Rochester Conf. Ser. Public Policy

1, 19 (1976). 6. S. Battiston et al., J. Econ. Dynam. Control 36, 1121 (2012). 7. G. Cimini et al., Sci. Rep. 5, 15758 (2015). 8. S. Battiston et al., Sci. Rep. 2, 541 (2012). 9. R. Axtell, “Endogenous dynamics of multi-agent firms”

(Working paper version 1.5, Univ. of Oxford, 2014); www.css.gmu.edu/~axtell/Rob/Research/Pages/Firms.html.

10. B. LeBaron, in Handbook of Computational Economics, vol.2, Agent-Based Computational Economics, L. Tesfatsion, and K. L. Judd, Eds. (North-Holland, Amsterdam, 2006), pp. 1187–1233.

11. S. Thurner et al., Quant. Finan. 12, 695 (2012). 12. C. Aymanns, J. D. Farmer, J. Econ. Dyn. Control 50, 155

(2015). 13. A. Flache, M. W. Macy, J. Conflict Resolut. 55, 970 (2011). 14. T. Bao, C. Hommes, T. Makarewicz, “Bubble formation

and (in)efficient markets in learning-to-forecast and –optimize experiments” (TI 2015-107/II Working paper, Tinbergen Institute, Amsterdam http://papers.tinbergen.nl/15107.pdf.

15. C. H. Hommes, Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems (Cambridge Univ. Press, Cambridge, 2013).

16. T. Bao, C. H. Hommes, “When speculators meet constructors: Positive and negative feedback in experi-mental housing markets” CeNDEF Working paper 15-10, University of Amsterdam, Netherlands, 2015)]; http://bit.ly/WP15-10.

17. A. G. Haldane, “On microscopes and telescopes,” Workshop on Socio-Economic Complexity, Lorentz Center, Leiden, 23 to 27 March 2015 (Bank of England, London, 2015); http://bit.ly/1VIJlvX.

ACKNOWLEDGMENTS

We acknowledge financial support from the Netherlands Institute of Advanced Studies in the Humanities and Social Sciences, the Netherlands Organisation for Scientific Research, the Lorentz Center, and the Tinbergen Institute.

10.1126/science.aad0299

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14

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2

0

-2

-4

-6

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Homogeneous

Heterogeneous

z-score

Pre-crisis Crisis

Normal z-score range

Early-warning signals of the 2008 crisis in the Dutch interbank network. The figure portrays a temporal

analysis of two loops, pairs of banks that are at the same time debtor and creditor to each other. Although the

raw number of two loops is not very informative about possible ongoing structural changes, its comparison

with a random network model benchmark is. A z-score represents the number of standard deviations by which

the number of two loops in the real network deviates from its expected value in the model. Small magnitude

z-scores (<3) indicate approximate consistency with the model, whereas larger magnitudes indicate statistically

significant deviations. Two different random network models were used: a homogeneous network with the

same total number of links as in the real network (top) and a heterogeneous network where every bank has the

same number of connections as in the real network (bottom). The homogeneous model, often used in standard

analyses, highlights only a late and abrupt structural change (2008). The more realistic heterogeneous model

also identifies a gradual, early-warning “precrisis” phase (2005–2007). [Modified from (2)]

Experiments

Concluding Remarks

New alternative complexity, agent-based approach is on its way...... depends much on new generation of students!policy analysis: identify and model nonlinearities to getgrip on complexity

Thank you very much!

1




Complexity

Published by:Netherlands Organisation for Scientific Research (NWO)

Visiting address:Laan van Nieuwe Oost-Indië 300The Hague

Postal address:P.O. Box 934602509 AL The HagueThe Netherlands

T: +31 (0)70 344 07 09E: [email protected] W: www.nwo.nl/complexity

The Hague, September 2014

This publication is supported by the

Royal Netherlands Academy of Arts and

Sciences (KNAW) Programme “Over Grenzen”.

Grip on ComplexityHow Manageable are Complex Systems?

Directions for future complexity research

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