Download The Challenge of Agent-based Modeling in Economics J. Doyne

The Challenge of Agent-based
Modeling in Economics
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ESRC Conference on Diversity in Macroeconomics!
University of Essex
J. Doyne Farmer Ins$tute for New Economic Thinking at the Oxford Mar$n School Mathema0cal Ins0tute, Oxford University External professor, Santa Fe Ins$tute
Feb. 24, 2014
The talk I almost gave: “Three diverse approaches to modeling systemic risk” • Cascading failure network models – interbank lending – overlapping porLolios – combina$on of the two • Dynamical systems models – Liquida$on-­‐based accoun$ng – leverage and overlapping porLolios revisited • Agent-­‐based models – leveraged value investors – housing model – CRISIS (model of “main components” of economy
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(appeared in Complex Systems, 2009)
The Virtues and Vices of Equilibrium,
and the Future of Financial Economics
J. Doyne Farmer and John Geanakoplos†
January 11, 2008
Abstract
The use of equilibrium models in economics springs from the desire for parsimonious models of economic phenomena that take human
reasoning into account. This approach has been the cornerstone of
modern economic theory. We explain why this is so, extolling the
virtues of equilibrium theory, then present a critique and describe why
this approach is inherently limited, and why economics needs to move
in new directions if it is to continue to make progress. We stress that
this shouldn’t be a question of dogma, but should be resolved empirically. There are situations where equilibrium models provide useful
predictions and there are situations where they can never provide useful predictions. There are also many situations where the jury is still
out, i.e., where so far they fail to provide a good description of the
world, but where proper extensions might change this. Our goal is to
convince the skeptics that equilibrium models can be useful, but also to
make traditional economists more aware of the limitations of equilibrium models. We sketch some alternative approaches and discuss why
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Paul Krugman’s view of
agent-­‐based modeling “Oh, and about RogerDoyne Farmer (sorry,
Roger!) and Santa Fe and complexity and all
that: I was one of the people who got all
excited about the possibility of getting
somewhere with very detailed agent-based
models — but that was 20 years ago. And
after all this time, it’s all still manifestos and
promises of great things one of these days.”
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Paul Krugman, Nov. 30, 2010, in response to an article
about INET housing project in WSJ.
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Reminder: All economics models are agent-­‐based models
• ABMs are computa(onal agent-­‐based models (ACE)
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Why isn’t ABM the mainstay of economics?
• Math culture is deeply rooted – papers scored too much on math vs. science – disdain and distrust of simula$on – fascina$on with ra$onality and op$mality • ABM is a fringe ac$vity, hasn’t delivered home runs needed to enter establishment – chicken/egg problem • Lucas cri$que
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Lucas Cri$que
Recession of 70’s. “Keynesian” econometric models. Phillips curve: Rising prices ~ rising employment Following Keynesians, Fed inflated money supply Result: Inflation, high unemployment = stagflation Problem: People can think Conclusion: Macro economic models must incorporate human reasoning • Solution: Dynamic Stochastic General Eq. models
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Advantages of DSGE
• “Micro-­‐founded” (unlike econometric models) – can be used for policy analysis. • Time series models – ini$alizable in current state of the world, can make condi$onal forecasts • Describe a specific economy at a specific $me. • In some sense parsimonious
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Why agent-­‐based modeling?
• Diversifies toolkit of economics: Complements DSGE
and econometric models. Also microfounded
• Time is ripe: increased computer power, Big Data,
behavioral knowledge. Never let a crisis go to waste.
• Hasn’t really been tried yet -- crude estimates:
– econometric models: 30,000 person-years
– DSGE models: 20,000 person-years
– agent-based models: 500 person-years
• Successes elsewhere: Traffic, epidemiology, defense
• Examples of successes in economics: – Endogenous explanations of clustered volatility and heavy
tails; firm size; neighborhood choice
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Advantages
• Can faithfully represent real institutions
• Easily captures instabilities, feedback, nonlinearities,
heterogeneity, network structure,...
• Shocks can be modeled endogenously
• Easy to do policy testing
• Easy to incorporate behavioral knowledge
• Can calibrate modules independently using micro
data -- much stronger test of models!
– In some sense between theory and econometrics
• ABMs synthesize knowledge:
– Possible to understand what is not understood
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Challenges
• Little prior art • Developing appropriate abstractions – What to include, what to omit?
– How to keep model simple yet realistic?
• Micro-data to calibrate decision rules?
• Data censoring problems
• Realistic agent-based models are complicated.
• No theoretical foundation
Cautionary tale of weather forecasting
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Formula$ng decision rules •
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Make something up Take from behavioral literature Perform experiments in context of ABM Interview domain experts Calibrate against microdata Learning and selec$on, Lucas cri$que !
(ABM can respond to Lucas cri$que)
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Exis$ng ABMS in economics
• Almost all are qualita$ve • Range of complexity, e.g. – zero/low intelligence con$nuous double auc$on – latent order book (Bouchaud group) – Lebaron, Brock Hommes trend follow/fundamentalist – Axtell firm size – Thurner et al. leveraged value investors – SFI Stock Market – Dosi-­‐group – EURACE
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Is it possible to make a quan$ta$ve ABM that can be used as a $me series model?
(and therefore can compete with DSGE)
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Housing model project
• Senior collaborators: Rob Axtell, John Geanakoplos, Peter Howil • Junior collaborators: Ernesto Carella, Ben Conlee, Jon Goldstein, Malhew Hendrey, Philip Kalikman • Funded by INET three years ago for $375,000.
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Agent-­‐based model of housing market
• Goal: condi$onal forecasts and policy analysis • Simula$on at level of individual households • Exogenous variables: demographics, interest rates, lending policy, housing supply. • Predicted variables: prices, inventory, default • 16 Data sets: Census, mortgages (Core Logic),tax returns (IRS), real estate records (MLA), ... • Current goal: Model Washington DC metro area • Future goal: All metro areas in US 16
Module examples
• Desired expenditure model – buyers’ desired home price as a func$on of household income and wealth • Seller’s pricing model – seller’s offering price as a func$on of home quality, $me on market, and total inventory • Buyer-­‐seller matching algorithm – links buyers and sellers to make transac$ons • Household wealth dynamics – models consump$on and savings • Loan approval – qualifies buyers for loans based on income, wealth; must match issued mortgages
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Housing model algorithm
At each $me step: • Input changes to exogenous variables • Update state of households – income, consump$on, wealth, foreclosures, ... • Buyers: – Who? Price range? Loan approval, terms? • Sellers: – Who? Offering price? Price updates? • Match buyers and sellers – Compute transac$ons and prices
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Results when we fit parameters to match the target data
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Results obtained by hand-fitting parameters
(this is an early slide)
Tentative conclusion: Lending policy is dominant cause of
housing bubble in Washington DC.
Results when we fit each module separately on data that is not the target data.
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Baseline result
Housing Market Results
Case Shiller
Average House Sale Price
Index, first period = 1
Fraction
5e+05
Model
Data
2.5
Sold Price to OLP
Dollars
1.00
4e+05
2.0
3e+05
0.95
1.5
2e+05
0.90
1.0
1e+05
1998
2000
2002
2004
2006
2008
2010
0.85
1998
2000
Active Listings
120000
2002
2004
2006
2008
2010
1998
Units Sold
Number
30000
250
25000
200
80000
20000
150
60000
15000
100
40000
10000
20000
5000
2000
2002
2004
2006
2008
2010
30
2008
2010
3.5
15
64
10
62
5
0
2002
2004
2006
2008
2010
2002
2004
2006
2008
2010
2008
2010
Percent
Model
Data
2.5
2.0
1.5
60
2000
2000
3.0
66
1998
1998
4.0
68
20
2010
Vacancy Rate
70
25
2008
Days
Homeownership Rate
Percent
Model
Data
2006
0
Months of Inventory
35
2004
50
1998 2000 2002 2004 2006
*Data is smoothed with centered
11−month moving average.
Months
2002
Days on Market
Number*
100000
1998
2000
1.0
1998
2000
2002
2004
2006
2008
2010
1998
2000
2002
2004
2006
fixed interest rate
Housing Market Results
Case Shiller
Average House Sale Price
Index, first period = 1
Fraction
5e+05
Model
Data
2.5
Sold Price to OLP
Dollars
1.00
4e+05
2.0
1.5
3e+05
0.95
2e+05
0.90
1.0
1998
2000
2002
2004
2006
2008
2010
1998
2000
Active Listings
2006
2008
2010
1998
2000
30000
8e+04
25000
2002
2004
2006
2008
2010
2008
2010
2008
2010
Days on Market
Number*
1e+05
Days
200
150
20000
6e+04
100
15000
4e+04
2e+04
1998
2000
2002
2004
2006
2008
10000
50
5000
0
2010
1998 2000 2002 2004 2006
*Data is smoothed with centered
11−month moving average.
Months of Inventory
2008
2010
4.0
70
Model
Data
3.5
68
2006
2008
2010
Model
Data
1.0
0
2004
Percent
1.5
60
2002
2006
2.0
62
5
2004
2.5
64
10
2002
3.0
66
15
2000
2000
Vacancy Rate
Percent
1998
1998
Homeownership Rate
Months
20
2004
Units Sold
Number
25
2002
1998
2000
2002
2004
2006
2008
2010
1998
2000
2002
2004
2006
fixed lending policy
Housing Market Results
Case Shiller
Average House Sale Price
Index, first period = 1
Fraction
5e+05
Model
Data
2.5
Sold Price to OLP
Dollars
1.00
4e+05
2.0
3e+05
0.95
1.5
2e+05
1.0
1e+05
1998
2000
2002
2004
2006
2008
2010
0.90
1998
2000
Active Listings
2002
2004
2006
2008
2010
2000
Units Sold
Number
25000
2002
2004
2006
2008
2010
2008
2010
2008
2010
Days on Market
Number*
Days
150
80000
20000
60000
15000
40000
10000
20000
100
50
5000
0
0
1998
2000
2002
2004
2006
2008
2010
1998 2000 2002 2004 2006
*Data is smoothed with centered
11−month moving average.
Months of Inventory
2008
2010
1998
4.0
70
Model
Data
3.5
68
10
66
6
64
2.0
4
62
1.5
2
60
1.0
2002
2004
2006
2008
2010
2004
2006
Percent
Model
Data
3.0
8
2000
2002
Vacancy Rate
Percent
1998
2000
Homeownership Rate
Months
12
1998
2.5
1998
2000
2002
2004
2006
2008
2010
1998
2000
2002
2004
2006
CRISIS
EUROPEAN
COMMISSION
Complexity Research Initiative
for Systemic InstabilitieS
• Complete agent-­‐based model of economy • Agents: Households, firms, banks, mutual funds, central banks. Both financial and macro. • Goals: – tool for policy decision making – series of models of increasing complexity – create standard sopware library – Be useful for central banks
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CRISIS schema$c
Mark 2
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Produc$on sector
• Input-­‐output economy – firms are myopic profit maximizers that use heuris$cs to set price and quan$ty of produc$on – variable labor supply – finance produc$on via mixture of credit and equity – input-­‐output structure mimicking real economy • For comparison have simpler alterna$ves, e.g. fixed labor Cobb Douglas, exogenous dividends.
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Financial sector
• Banks – take deposits from firms and households, lend to firms, buy and sell shares, par$cipate in interbank market. – Investment strategies: trend following, fundamental Central bank – conven$onal and unconven$onal policy opera$ons – interest rate can be formed endogenously • Firms – borrow from banks to fund produc$on
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Unconven$onal policy opera$ons: purchase & assump$on, bailout, bail in
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Conclusions
• We have lots of work to do to make models that can seriously compete with DSGE • Should be possible to make model with rich ins$tu$onal structure, calibrated to real world • Capability to put an economy in current state of a real economy, make condi$onal forecasts • Economic models of future will be ABM – but when? • Chicken-­‐egg problem to get ABM off the ground
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Conclusions
• Must respect ins$tu$onal structure – Impossible to do everything at once: open in conflict with understanding strategic reasoning – danger of strict requirement for “economic content” • Fundamental problem in macro is lack of data – only hope is ABM with microdata calibra$on • Want different tools for different jobs — diversity – simple models for understanding mechanism – richer models for quan$ta$ve understanding Economics needs to allow more diversity!
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Future versions will include:
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Mortgage markets Realis$c input-­‐output structure Deriva$ve markets Bond markets Shadow banking system
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Design philosophy
• As simple as possible (but no more) • Design model around available data • Fit modules and agent behaviors independently from target data, using several different methods: – micro-­‐data for calibra$on and tes$ng – consult domain experts for behavioral hypotheses – adap$ve op$miza$on to cope with Lucas cri$que – economic experiments • Systema$cally explore model sensi$vi$es • Plug and play • Standardized interfaces • Industrial code, sopware standards, open source
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