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Simula’on The challenge for data science Oxford Man Institute March 9, 2018 J. Doyne Farmer Mathema’cal Ins’tute, Computer Science Dept. and Ins’tute for New Economic Thinking at the Oxford Mar’n School External professor, Santa Fe Ins’tute
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Page 1: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Simula'onThechallengefordatascience

Oxford Man InstituteMarch 9, 2018

J.DoyneFarmerMathema'calIns'tute,ComputerScienceDept.and

Ins'tuteforNewEconomicThinkingattheOxfordMar'nSchoolExternalprofessor,SantaFeIns'tute

Page 2: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Computation has revolutionized physical and natural science

• Makes it possible to study nonlinear dynamics and complex systems.

• Over the last 50 years this has been the most important driver of progress in science.

• Social science (including economics) has not benefitted like physical and natural science.– elementary processes are not well understood– different cultural attitudes 2

Page 3: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Bigdataandmachinelearning

• Computerhasalsogivenusbigdata• Machinelearning– fiGnganonlinearfunc'ontolotsofdata– difficulttomodelsitua'onsithasneverseen

• Simula'onsarebasedonanunderstandingofhowthingswork– canusebigdataforcalibra'on– candopolicyanalysis

3

Page 4: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Simula'oninsocialscienceleadstoABM

• Agent-basedmodels(ABMs)areaclassofcomputa'onalmodelsforsimula'ngtheac'onsandinterac'onsofautonomousagents(individualsorcollec'veen''essuchasorganiza'onsorgroups)withaviewtoassessingtheireffectsonthesystemasawhole.

4

Page 5: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Whyagent-basedmodeling?• Diversifies toolkit of economics: Complements

DSGE and econometric models.• Time is ripe: increased computer power, Big Data,

behavioral knowledge. • 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

5

Page 6: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Advantages• Can faithfully represent real institutions• Easily captures instabilities, feedback,

nonlinearities, heterogeneity, network structure,...• Can capture endogenous dynamics• 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

6

Page 7: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Designphilosophy• Assimpleaspossible• Designmodelaroundavailabledata• Fitmodulesandagentbehaviorsindependentlyfrom

targetdata,usingseveraldifferentmethods:– micro-dataforcalibra'onandtes'ng– consultdomainexpertsforbehavioralhypotheses– adap'veop'miza'ontocopewithLucascri'que– economicexperiments

• Systema'callyexploremodelsensi'vi'es• Plugandplay• Standardizedinterfaces• Industrialcode,soZwarestandards,opensource

7

Page 8: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Formula'ngdecisionrules

• Makesomethingup• Takefrombehavioralliterature• PerformexperimentsincontextofABM• Interviewdomainexperts• Calibrateagainstmicrodata• Learningandselec'on,Lucascri'que• Ra'onality

8

Page 9: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

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?• Realistic ABMs can be complicated• Avoiding black box problem• No theoretical foundation

Cautionary tale of weather forecasting

9

Page 10: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Housingmarkets

10

Page 11: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Washingtonhousingmodelproject

• Seniorcollaborators:RobAxtell,JohnGeanakoplos,PeterHowie

• Juniorcollaborators:ErnestoCarella,BenConlee,JonGoldstein,MaehewHendrey,PhilipKalikman

11

Page 12: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Agent-basedmodelofhousingmarket

• Goal:condi'onalforecastsandpolicyanalysis• Simula'onatlevelofindividualhouseholds• Exogenousvariables:demographics,interestrates,lendingpolicy,housingsupply.

• Predictedvariables:prices,inventory,default• 16Datasets:Census,mortgages(CoreLogic),taxreturns(IRS),realestaterecords(MLA),...

12

Page 13: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Moduleexamples• Desiredexpendituremodel– buyers’desiredhomepriceasafunc'onofhouseholdincomeandwealth

• Seller’spricingmodel– seller’sofferingpriceasafunc'onofhomequality,'meonmarket,andtotalinventory

• Buyer-sellermatchingalgorithm– linksbuyersandsellerstomaketransac'ons

• Householdwealthdynamics– modelsconsump'onandsavings

• Loanapproval– qualifiesbuyersforloansbasedonincome,wealth;mustmatchissuedmortgages

13

Page 14: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

HousingmodelalgorithmAteach'mestep:

• Inputchangestoexogenousvariables• Updatestateofhouseholds– income,consump'on,wealth,foreclosures,...

• Buyers:–Who?Pricerange?Loanapproval,terms?

• Sellers:–Who?Offeringprice?Priceupdates?

• Matchbuyersandsellers– Computetransac'onsandprices

14

Page 15: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Resultswhenwefitparameterstomatchthetargetdata

15

Page 16: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Tentative conclusion: Lending policy is dominant cause of housing bubble in Washington DC.

Results obtained by hand-fitting parameters

(this is an early slide)

Page 17: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Resultswhenwefiteachmoduleseparatelyondatathatisnotthe

targetdata.

17

Page 18: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Case Shiller

1998 2000 2002 2004 2006 2008 2010

1.0

1.5

2.0

2.5

Index, first period = 1ModelData

Average House Sale Price

1998 2000 2002 2004 2006 2008 2010

1e+05

2e+05

3e+05

4e+05

5e+05Dollars

Sold Price to OLP

1998 2000 2002 2004 2006 2008 20100.85

0.90

0.95

1.00

Fraction

Active Listings

1998 2000 2002 2004 2006 2008 2010

20000

40000

60000

80000

100000

120000 NumberUnits Sold

1998 2000 2002 2004 2006 2008 2010

5000

10000

15000

20000

25000

30000Number*

*Data is smoothed with centered11−month moving average.

Days on Market

1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

250Days

Months of Inventory

1998 2000 2002 2004 2006 2008 201005

101520253035

MonthsModelData

Homeownership Rate

1998 2000 2002 2004 2006 2008 2010

60

62

64

66

68

70Percent

Vacancy Rate

1998 2000 2002 2004 2006 2008 2010

1.0

1.5

2.0

2.5

3.0

3.5

4.0Percent

ModelData

Housing Market ResultsBaseline result

Page 19: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Case Shiller

1998 2000 2002 2004 2006 2008 2010

1.0

1.5

2.0

2.5

Index, first period = 1ModelData

Average House Sale Price

1998 2000 2002 2004 2006 2008 2010

2e+05

3e+05

4e+05

5e+05Dollars

Sold Price to OLP

1998 2000 2002 2004 2006 2008 2010

0.90

0.95

1.00

Fraction

Active Listings

1998 2000 2002 2004 2006 2008 2010

2e+04

4e+04

6e+04

8e+04

1e+05

NumberUnits Sold

1998 2000 2002 2004 2006 2008 2010

5000

10000

15000

20000

25000

30000

Number*

*Data is smoothed with centered11−month moving average.

Days on Market

1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

Days

Months of Inventory

1998 2000 2002 2004 2006 2008 20100

5

10

15

20

25Months

ModelData

Homeownership Rate

1998 2000 2002 2004 2006 2008 2010

60

62

64

66

68

70Percent

Vacancy Rate

1998 2000 2002 2004 2006 2008 2010

1.0

1.5

2.0

2.5

3.0

3.5

4.0Percent

ModelData

Housing Market Resultsfixed interest rate

Page 20: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Case Shiller

1998 2000 2002 2004 2006 2008 2010

1.0

1.5

2.0

2.5

Index, first period = 1ModelData

Average House Sale Price

1998 2000 2002 2004 2006 2008 2010

1e+05

2e+05

3e+05

4e+05

5e+05Dollars

Sold Price to OLP

1998 2000 2002 2004 2006 2008 2010

0.90

0.95

1.00

Fraction

Active Listings

1998 2000 2002 2004 2006 2008 2010

0

20000

40000

60000

80000

NumberUnits Sold

1998 2000 2002 2004 2006 2008 2010

5000

10000

15000

20000

25000 Number*

*Data is smoothed with centered11−month moving average.

Days on Market

1998 2000 2002 2004 2006 2008 2010

0

50

100

150

Days

Months of Inventory

1998 2000 2002 2004 2006 2008 2010

2

4

6

8

10

12

MonthsModelData

Homeownership Rate

1998 2000 2002 2004 2006 2008 2010

60

62

64

66

68

70Percent

Vacancy Rate

1998 2000 2002 2004 2006 2008 2010

1.01.52.02.53.03.54.0

PercentModelData

Housing Market Resultsfixed lending policy

Page 21: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

BankofEnglandmodel(Bap'sta,Farmer,Hinterberger,Low,Tang,Uluc)

• UsedtogiveadvicetotheFinancialPolicyCommieeeonlendingpolicyandforpolicyrela'ngtobuytoletinvestors

• Mainconclusion:Restric'ngdebttoincomera'ocanbeeffec'veindampingbubbles

21

Page 22: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Challengeofquan'ta'vesimula'on

• Es'mateparameters• Ini'aliza'on• Valida'on– whatdoesthemodeldowell,whatdoesitdopoorly?

• Aeribu'ngcausality

22

Page 23: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Parameteres'ma'on

• UsuallynoclosedformexpressionofABM• DangerofoverfiGng• Typicalmethod:minimizedistancetodata

23

Page 24: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Timeseriesforecas'ngwithABM?

• ABMsoZenhaveunobservedmicrostates• Wanttoforecastobservedmacrostates• Macrostatesareaggregatedmicrostates• Mustaltermicrostatestomatchmacrostates• Mustbeconsistentwithaeractorofsimula'on– correla'onofmicrostatesmustberespected;oZendifficulttocharacterizeexceptbyrunningsim.

24

Page 25: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Testsystem:Predator-preymodel(JuanSabuco,DanTang,JDF)

• Predatorsandpreyliveona2dimensionalgrid• Preycompetetocollectfoodandreproduce• Predatorscompetetohuntpreyandreproduce• Bothdiffusearoundgrid

• Goal:Forecastpredatorandprey'meseries

25

Page 26: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Geographic distribution of animals (partial view of microstates)

Preys are red dots

Predators are blue dots

Page 27: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Aligningmicrostateswithmacrostates?

• Wanttomatchcurrentpopula'on– suddenrealignmentisdisastrous

• Oursolu'onhere:Nudging– ratherthanalteringmicrostatesallatonce,alterincrementally

– toleratesomedevia'oninmacrostates

27

Page 28: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Whatistheop'malnudge?• Whichmicrostatestoalter?• Howtoalterthem?• Howlongtonudgeandatwhatintensity?

• Forpredator-preysystemmatch'meseriesbyaddingorsubtrac'ngindividualanimals– randomplacementisdisastrous– replica'onofexis'ngindividualsworks“well”

28

Page 29: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Nudging predator-prey model

x are the predators y are the prey

)(),(1 naprednnn xxKyxfx −−=+

)(),(1 napreysnnn yyKyxgy −−=+

Page 30: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

} If the coupling is too strong the model blows up.

Nudging parameters

Page 31: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

} If the coupling is too soft there is no synchronization.

Nudging parameters

Page 32: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

} For the right parameters there is synchronization.

Nudging parameters

Page 33: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Turning nudge off, on, offKpred=0.22 Kpreys=0.08

Page 34: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

ZoomKpred=0.22 Kpreys=0.08

Page 35: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Error Growth: Optimal (black), nudge (blue), sudden (red)

Page 36: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Error Growth

Page 37: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Error Growth

Page 38: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Successofnumericalweatherprediction

• Since1980,everytenyears,predictionaccuracyatdaynhasbecomeasgoodasaccuracyatdayn-1,10yearsearlier.

• Successisdueto3reasons:- bettermodels- biggercomputers- betterdata

• Akeyfactorhasbeendataassimilation

38

Page 39: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

39

Page 40: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Predicting chaotic time series

(farmer and Sidorowich, 1987)

E ⇡ Ce(m+1)�1TN�(m+1)/D

Page 41: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Closelyrelatedtoproblemsoftimeseriesembedding

41

Page 42: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

EconomicSimula'onLibrary

• Createkeybuildingblocksforeconomics• Economicagentsarespecialbecausethey– havebalancesheets(accoun'ng)– makecontracts– interactwithmarkets– formexpecta'ons

• Interchangeableparts• Opensourceproject“wiki-economics”- heps://economicsl.github.io/

42

Page 43: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Myvisionforeconomicsimula'on

• Real'metandemsimula'onofeconomiesofthemajorcountriesoftheworld.

• Micro-bigdatainputsdirectlyfrominternet.• Coupledtoothersocialmodels• Usedbycentralbanks• Teamsfocusingoneachcomponent,e.g.households,firms,banks,…

43

Page 44: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Scurveofscientificinnovation?• Whenanewfieldopensup,thereisaflurryofsignificantnewinnovations;diesoutovertime

• Shifttowardapplications:Papersincreaseexponentially

• Bestresearch:Getinearly• FieldsIhavewitnessedthisin:- chaos- machinelearning- complexsystems

• Conjecture:Simulationisinearlystage44

Page 45: Simulaon The challenge for data science - The challenge for data...Bank of England model (Bap’sta, Farmer, Hinterberger, Low, Tang, Uluc) • Used to give advice to the Financial

Summary

• Simula'onhasenormousbutsofarlargelyunexploredpoten'al.

• Newwayofdoingeconomics:Don’tneedequilibrium,u'litymaximiza'on,closedform.

45


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