real and financial crises - university of vermontwgibson/research/gs_slides.pdfintro to complex...

Intro to complex systemsExamples

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Real and Financial Crises

Bill Gibson and Mark Setterfield

Economski Institute 1 June 2012

Bill Gibson and Mark Setterfield University of Vermont & Trinity College


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University of Vermont

Graduate Certificate in

Complex Systems

1-year

5 courses (15 credits)

2 required core courses

3 electives



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Complex Systems at University of Vermont

Core Courses

Principles of Complex SystemsModeling Complex Systems

Electives

Chaos, Fractals & Dynamic SystemsComplex NetworksEvolutionary ComputationNeural ComputationStatistical Pattern RecognitionApplied Time Series & ForecastingApplied Geostatistics



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Electives

Deterministic ModelsMathematical Biology & EcologyArtificial IntelligenceIntelligent Transportation SystemsSystems AnalysisStochastic ProcessesSystems and Synthetic BiologyAdvanced Bioengineering Systems



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Electives

Multi-Scale ModelingDecision Making ModelsSystems Analysis & Strategic ManagementEvolutionThermal PhysicsEvolutionary RoboticsBayesian StatisticsEnvironmental Modeling

Many courses never before offered

Limited prerequisites

Summer School at Sante Fe Institute


http://www.santafe.edu/education/schools/complex-systems-summer-schools/


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Complex systems

Widely applied in social and physical sciences

Biology, medicine and other domains

Self-organizing dynamics of interacting entities

self-interested agentsmolecules and cellsgenes and epigenetic proteinsplants, birds, bacteria

All are complex systems



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Complex systems

Give rise to emergent properties

RigidityConsciousnessCancerGlobal warmingSocial herding and cascades

Example

What do these area share?

Answer: Complex self-organizing structure



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Simulation methodology widely used

Growing use in socialsciences only since 1990

Result of the advent ofpersonal computers

Has become a basic toolof natural sciences

Most if not all economicsarticles published inScience and Nature usethis methodology

Similar to telescope ormicroscope?



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Proceedings of the National Academy of Sciences

Recently published

Helbing and Yu

The outbreak of cooperationamong success-driven individu-als under noisy conditions



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Science and Nature

Evolution of HumanBehaviors & InstitutionsDivision

Numerous graduatestudents

Many students fromcomputer science nowtaking economicsvice-versa

Recently published inScience

Choi and Bowles

The co-evolution of parochialaltruism and war



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Trading at 650ms



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Migration model



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Scientific visualization

Spread of malariainfection

Bioremediation of oil spills

Nano copolymer selfassemblies

Rogue wave prediction

Meteorological models

Turbulent flows (CFD)

Neural connections

Magnetic Field Intensity

Protein folding

Human Dynamics

Nuclear explosions

Cratering models

Time critical socialmobilization

Flight simulators

Medical proceduresimulation

Aids Model in SouthAfrica


https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011

http://www.uvm.edu/~cdanfort/csc-reading-group/foldit-nature-2010.pdf

http://www.uvm.edu/~cdanfort/csc-reading-group/crane-sornette-pnas-2008.pdf

http://www.uvm.edu/~cdanfort/csc-reading-group/pickard-balloons-science-2011.pdf

http://www.uvm.edu/~cdanfort/csc-reading-group/pickard-balloons-science-2011.pdf


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Origin of Complex Systems

Known as multi-agentsystems or

Agent-based models

Have roots in classicalstatisticalthermodynamics

Perfect gas law derivedfrom movement of theindividual molecules

Microfoundations (given)for macro properties(emergent) L. Boltzmann 1844-1906



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References

Literature

Wooldridge Introduction to MultiAgent SystemsEpstein and Axtell Growing Artificial SocietiesAxelrod The Complexity of CooperationDurlaf and Young Social DynamicsTesfatsion Handbook of Computational EconomicsSutton and Barto Reinforcement LearningMiller and Page Complex Adaptive SystemsGilbert and Troitzsch Simulation for the Social Scientistand many, many others....including special issue of PNAS (2002)

My class on computational economics


http://www.uvm.edu/~wgibson/Classes/230s11/230s11_news.html


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Promise of Complex Systems

Predict black swans

No representative agent

Heterogeneity

Behavioral economics

Run controlledexperiments

Has captured publicimagination



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Natural Link to Network Theory

Preferential Attachment

Number of nodes with exactlyk links follows a power law dis-tribution Barabasi and Albert(1999)



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Good Introduction



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Spanned massive literature



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Team Building and Collaboration Networks

Team Links

Guimer, Uzzi,Spiro and Ama-ral, Team Assembly Mecha-nisms Determine CollaborationNetwork Structure and TeamPerformance, Science



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Team Building and Collaboration Networks



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Models: Search for Emergent Properties

Emergent property is a property not defined for the individualcomponents

Appears only in the aggregate behavior of the whole

Examples: viscosity, phase change in H2O

Cannot be deduced from properties of hydrogen and oxygen

May only be available through computer simulations

Telescope analogy applies here perfectly



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Schelling’s (1971) original neighborhood model

Agents’ behavior depends on the actions or more generally thestate of neighbors

White liberals move when surrounded by “too many”neighbors of color

After several iterations, neighborhoods are segregated, despitethe preferences of the whites to live together

Each agent’s decision rule is simple

When a threshold of racial density is surpassed...

Agent relocates to some randomly chosen location

Interesting properties emerge



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Schelling’s segregation model-assumptions

Green squares are houses

Green square will move unless 3 of 8 neighbors are green



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Schelling’s segregation model



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Done before computers



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On a sheet of paper



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Example: Dot circle chaos (NetLogo)



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Example: Order



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Example: Beginning of chaos



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Example: Chaos



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Key Concept: Heterogeneity

Heterogeneity: eliminatesrepresentative agentproblem

Gives meaning to boundedrationality as mips (betterthan experimental)

Gives rise to a clearerempirical conception ofbounded rationalitybandwidth, cps

More realistic approach topublic policy

Platforms

Multi-Agent modeling

Java

Repast

Python

NetLogo



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Overview

Existing lit: either exclusively real or financial

Builds on shoulders of giants in macromodeling

Other giant literature: econophysics

Lively debate stretching back to early 1990s

Refined but not based on anything but simplest physics

Sornette (2003) best intro for neophyte

Not that inaccessible!



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Good intro to field



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Can critical point be identified empirically?

What happens right before something explodes?

How does a phase change occur, say liquid water to ice?

How does fluid pass through a semi-solid?

Clusters form and critical point is when a cluster spanssubstrate

All are instances of percolation models



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Yes...but happen infrequently in real economies



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Can Econphysics predict crisis?

Two statistical signatures: power law acceleration

Log-periodic oscillation before rupture

“it looks like its going to blow!” ...but

Crisis doesn’t always happen



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Power-law acceleration to critical point

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50

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85.0 85.5 86.0 86.5 87.0 87.5 88.0

S&

P 5

00

Trading days



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Log-periodic oscillations before critical point

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85.0 85.5 86.0 86.5 87.0 87.5 88.0

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P 5

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Trading days



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Critical point estimation

Log periodic equation

F (t) = A + B(tc − t)τ (1 + C cos{ω log[(tc − t)/T ]})

Guess values for tc , τ, ω and T .

With these in hand, the equation is then linear in thecoefficients A, B, C and so a regression can be run.

But is this overfitting?



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Why does this happen?

Econophysics: networkstructure

Taken as given!

Hierarchical diamondlattice (HDL)

Would create log-periodicoscillation

1 2

3 4



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How does crisis happen?

Two kinds for crisis: Top down

Arises from external shock, interest rate, gas prices

Bottom up not top down as other post-war recessions

Herd: penguins jumping pushing one their own into orcainfested water...

Herding: all agent select same strategy after a certain date

Cascade: all agents herding

In cascade social learning stops



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Good intro to economics of information



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Herds, herding and cascades



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Coupling and Spin Glass Models

Without dynamic coupling get power-law distribution

Not log-periodicity

Coupling has straight-forward interpretation in economics

Traders only imitate each other

This is public signal

Private signal comes from the firms in which they haveinvested



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What we did

Network structure is not given

Arises out of real-financial interactions

Econ historians Rajan and Ramacharan (2009).

Land concentration gave rise to Banking Concentration

Gave rise to preferentially attached financial structure



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Random network: Erdos and Renyi, 1950



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Preferential attachment: Barabasi and Albert, 1999



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Italian banking system



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Preferential attachment

Grid architecture obviously arbitrary

Real sector generates income and profit

Affects private signal of traders

Real sector generates savings and deposits

Creates preferential attachment

Gives power-law distribution of traders

Result: Real foundation for financial crisis!



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Explaining Crashes

y = 36.164e0.0058x R² = 0.92877

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1800 1

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93

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S&P 500



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What we did

Combines real side with two different financial structures

Define crash by triangle method

Construct typology of crashes based on:

Financial constraint (real sector performance)Initial network structureWeighted vs. unweighted network

Take away: stand alone real or financial models misleading



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Typology of crashes

Real side

Moneyfully en-

dogenous?

Prefattach?

ShareWeights?

ShareWeights?

0 1 2 3

Prefattach?

ShareWeights?

ShareWeights?

4 5 6 7

yes

noyes

noyes

noyes

no

noyes

noyes

noyes

The cause of the crisis can be read from left to right, with pure financialcrises, level 0, gradually evolving to crises that are more real in nature, level 7.Bill Gibson and Mark Setterfield University of Vermont & Trinity College


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Money, credit, and crises

u < 1

Time

Credit

GDP

u = 1



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Traders’ forecast

Traders’ forecast combines the idiosyncratic signal,

Relative utilization rate of client

Subjectively perceived signal that arises from perceptions ofother traders behavior

φj = uj − u + K ∑i⊂J ′

φi + εj

Kt+1 = Kt + ∑i⊂J ′

ωi φi



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Identifying crises

0

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1.5

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2.5

3

0 100 200 300 400 500 600

200

50

!pbuild > 0 !p/pcrash > 0.5



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Borrowing

Traderforecastbullish

LoanblockedDeficit firm

Depositsadequate?

Linkedneighborscan lend?

Loanblocked

InvestInvest

no

no no

yesyes



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Results 1: PA and degree distribution

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1

1.5

2

2.5

3

0 0.2 0.4 0.6

ln

(nu

mber

of lin

ks

per

tra

der

)

ln(number of traders)



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Results 1: PA and degree distribution

y = -1.6812x + 2.3956 R! = 0.84648

0

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1.5

2

2.5

3

0 0.5 1 1.5

ln

(nu

mber

of lin

ks

per

tra

der

)

ln(number of traders)



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Results: Financial crises



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Results: Significance



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Conclusions

Stand alone real or financial models inadequate

Real sector matters for financial performance

Financial network structure also important

Much to learn from disaggregating structuralist model

Importance of intermediationImportance of money creation

Policy implications less clear


real and financial crises - university of vermontwgibson/research/gs_slides.pdfintro to complex...

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