Artificial Economics: The Generative Method in Economics

280 Pages·2009·6.95 MB·English

by  Gianfranco Giulioni (auth.)

Lecture Notes in Economics
and Mathematical Systems 631
FoundingEditors:
M.Beckmann
H.P.Künzi
ManagingEditors:
Prof.Dr.G.Fandel
FachbereichWirtschaftswissenschaften
FernuniversitätHagen
Feithstr.140/AVZII,58084Hagen,Germany
Prof.Dr.W.Trockel
InstitutfürMathematischeWirtschaftsforschung(IMW)
UniversitätBielefeld
Universitätsstr.25,33615Bielefeld,Germany
EditorialBoard:
A.Basile,H.Dawid,K.Inderfurth,W.Kürsten
For further volumes:
http://www.springer.com/series/300
Cesáreo Hernández · Marta Posada
Adolfo López-Paredes
Editors
Artificial Economics
The Generative Method in Economics
(cid:65)(cid:66)(cid:67)
Dr.Cesáreo Hernández
Dr. Marta Posada
Dr. Adolfo López-Paredes
Valladolid University
InSiSoc. E.T.S.I. Industriales
Paseo del Cauce, 59
47011 Valladolid
Spain
[email protected]
[email protected]
[email protected]
ISSN0075-8442
ISBN978-3-642-02955-4 e-ISBN978-3-642-02956-1
DOI10.1007/978-3-642-02956-1
SpringerDordrechtHeidelbergLondonNewYork
LibraryofCongressControlNumber:2009931062
(cid:2)c Springer-VerlagBerlinHeidelberg2009
Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis
concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting,
reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication
orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,
1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violations
areliabletoprosecutionundertheGermanCopyrightLaw.
Theuseofgeneral descriptive names,registered names, trademarks, etc. inthis publication does not
imply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotective
lawsandregulationsandthereforefreeforgeneraluse.
Coverdesign:SPiPublisherServices
Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
Acknowledgements
OurthanksgotothemembersoftheScientificCommitteewhoreviewedthesub-
mittedpapersandgavemostvaluablecommentstotheauthors:
Fre´de´ricAmblard-Universite´ deToulouse1,France
•
LuisAntunes-UniversidadedeLisboa,Portugal
•
RobertAxtell-BrookingsInstitution,GeorgeMasonUniversity,USA
•
BrunoBeaufils-Universite´ ofLille1,France
•
OlivierBrandouy-Universite´ deLille1,France
•
CharlotteBruun-AalborgUniversitet,Denmark
•
AlessandroCappellini-Universita` degliStudidiTorino,Italy
•
SilvanoCincotti-Universita` degliStudidiGenova,Italy
•
AndreaConsiglio-Universita` degliStudidiPalermo,Italy
•
ChristopheDeissenberg-Universite´ delaMe´diterrane´e,France
•
GiorgoFagiolo-LEM,ScuolaSuperioreSant’Anna,Pisa,Italy
•
Jose´ ManuelGala´n-UniversidaddeBurgos,Spain
•
FlorianHauser-Universita¨tInnsbruck,Austria
•
WanderJager-RijksuniversiteitGroningen,TheNetherlands
•
MarcoJanssen-ArizonaStateUniversity,USA
•
AlanKirman-GREQAM,France
•
PhilippeLamarre-LINA,Universite´ deNantes,France
•
ThomasLux-Universita¨tzuKiel,Germany
•
MarcoLicalzi-Universita` ”Ca’Foscari”diVenezia,Italy
•
LuigiMarengo-LEM,ScuolaSuperioreSant’Anna,Pisa,Italy
•
PhilippeMathieu-Universite´ deLille1,France
•
AkiraNamatame-NationalDefenseAcademy,Japan
•
JavierPajares-UniversidaddeValladolid,Spain
•
JuanPavo´n-UniversidadComplutensedeMadrid,Spain
•
PaoloPellizzari-Universita` ”Ca’Foscari”diVenezia,Italy
•
DenisPhan-Universite´ deRennesI,France
•
JulietteRouchier-GREQAM,France
•
AnnalisaRussino-Universita` degliStudidiPalermo,Italy
•
LeighTesfatsion-IowaStateUniversity,USA
•
ElpidaTzafestas-NationalTechnicalUniversityofAthens,Greece
•
MuratYildizoglu-Universite´ PaulCe´zanne,GREQAM,France
•
Thanks as well to the authors and to the Springer-Verlag editors. They made it
possibletopublishthisvolumeintimefortheConference.
WeacknowledgefinancialsupportfortheconferenceandthebookbytheSpan-
ish Ministry of Science and Education TIN2008-06464-C03-02, to the Council of
EducationofCastillayLeo´nandtotheUniversityofValladolid.Thanksaswellto
ourcolleaguePabloMart´ınforproducingthemanuscript.
v
Preface
TheorganizersoftheArtificialEconomicsConference2009(AE09)arepleasedto
presentthisbookthatgatherstheacceptedpapers.TheConferenceisthefifthreg-
ularmeetingofresearchersinterestedintheAgentBasedApproachtoEconomics
and Managerial Applications, this year in Valladolid (Spain) September 10-11th,
2009.Themainaimoftheeventistofavourthemeetingofpeopleandideasfrom
both, the Computer Science community and the Economics and Finance commu-
nity,inordertobeabletoconstructamuchstructuredmulti-disciplinaryapproach
to the complexity of Economics. Since this is a book about Artificial Economics,
weshouldbrieflydescribewhatitmeansfortheEditors.
Artificial Economics (AE) is Experimental Economics using Software Agents.
EconometricswasfoundedasabridgebetweenStatisticsandMathematicsinEco-
nomics. AE can be thought of, as a bridging discipline between Economics and
Agent Based Modelling (ABM) and Distributed Artificial Intelligence (DAI). To-
dayweareintheearlystagesofAEbutthereisasolidandgrowingstreamofABM
applications,bothpracticalandfocusedonthedevelopmentofbetterexplanations
of economic observed facts. The proceedings of the Conferences published in the
Lecture Notes in Economics and Mathematical Systems Springer series, sum up
morethan100papersandareagoodsampleofthestateoftheartinAE.
SimulationisusedinEconomicstosolvelargeeconometricmodels,largescale
micro simulations and obtaining numerical solutions for policy design from a top
down established model. But these uses fail to take advantage of the facilities of-
ferbyArtificialIntelligenceandDistributedComputing.AEcanleveragethesefa-
cilities. AE should not be considered a chapter of Computational Economics, the
maindifferencebeingthebottom-upapproachofABMandthegenerativenatureof
the method. We should be grateful to the promoters of the first AE Conference in
September2005atLille,France,P.Mathieu,O.BrandouyandB.Beaufilsforcoin-
ingtheAEnamejustavoidingtheconfusiongoingoninthefield.Infacteveninthis
Conferencewehadtorejectsomepapersbecauseoftheircomputationaltop-down
approach.
As pointed out by Axtell (2008), ABM simulation in Economics, from now on
AE, can be viewed as a very elegant and general class of modelling techniques
vii
viii Preface
that generalize numerical economics, mathematical programming and micro sim-
ulation approaches. When agent models are populated by non autonomous agents
whosebehaviourispre-specifiedandneitheradaptivenorstrategic,theybecomemi-
crosimulations.Whenindividualagentsareoptimizersandthereisasocialplanning
agentthatcanbeviewedascoordinatingoraggregatingtheseindividualdecisions,
then we have ABM behaving as general mathematical programming models. But
AEmodelswithagentsthataresystematicallyheterogeneous,purposeful,bounded
rational and interacting over networks, away from equilibrium, do not generally
correspond to the accepted Economic Theory models. In Axtell (2008) words, so
generalistheagentapproachtoEconomics,andsoprodigioushascomputingtech-
nologybecome,thatitistemptingtocallthisnewapproachcomputationalenabled
Economics.
Economicsisasocialscience.Beingsocialinheritscomplexityandbeingasci-
encecallsforexperimentation.ThecomplexityinEconomicsisnotonlyduetothe
factthatweobservejustrealaggregateddatafromsimpleagentswithmyriadinter-
actions: a hard computational problem. A higher and different kind of complexity
comesfromthepersonalexchangewhereperhapstherearefewagentsbuttheyare
boundedrational,theylearnfromotheragentsbehaviourandtheyareadaptive,pur-
posefulandstrategic(Lo´pez-Paredesetal.,2002).Inbothcasesexperimentationis
necessarytoexploreanddiscovertheemergingmacrobehaviour.Historicalrecords
arenotenoughtodiscoverortesteconomictheories.ExperimentalEconomicswith
humans has provided a replicable Lab that provides further empirical data and it
has been recently welcome by the economist profession. But human behaviour in
theexperimentisnotdirectlycontrollableandthequestionofwhattheagent‘sbe-
haviouris,remainsopen.AEhasbroadenedthescopeofExperimentalEconomics,
allowingthemodellertocheckalternativeindividualbehaviour.InthissenseABM
simulation in feedback, when this is possible, with Experimental Economics with
humans,isa“killerapproach”toEconomicTheory.
Therearethreedimensionsthatareessentialwhenmodellinganyeconomicis-
sue: the institution (I) (the exchange rules, the way the contracts are closed, and
the information network), the environment (E) (the agent’s endowments, values,
resources and knowledge) and the agent behaviour (A). By mapping different ar-
rangementsoftheelementsofthistriplet(IxExA)intogeneratedoutcomesahostof
experimentalresultscanbeobtainedandexplored.ExperimentalEconomistshave
beendoingthatforthelast30yearsandtheyhavegainedveryvaluableinsightin
manydifficultandunsolvedeconomicissues.AEcangoevenfurtherinthisdirec-
tion. Exporting Economic Theory to other fields including natural resources man-
agement has proved forced labour. With ABM, we can export the AE methods to
anyphysicallandscapepopulatedbysocialagentssuchasinthemanagementand
economicsofnaturalresources(Gala´netal.,2009).
The neoclassical conception of Economics, the “value window”, is not capable
to explain dynamics and evolution, a fact widely acknowledged: “For the real dy-
namics,whichinvestigatestheprecisemotions,usuallyfarawayfromequilibria,a
muchdeeperknowledge...isrequired”(VonNeumannandMorgenstern,1944:44-
45).Inthelast20yearsalotofresearchhasbeendoneusingbiologicallyinspired
Preface ix
analogiestoextendthe“valuewindow”inevolutionaryandbehaviouraleconomics.
But as Schredelseker and Hauser (2008) pointed out (although not exactly in this
terms) AE can provide socially inspired analogies to solve complex problems in
Economicswithafullbehaviouralfocus.
Theshockingpredictionaccuracyof“PredictionMarkets”(Arrowetal.,2008)is
anexperimentalproofofthespontaneouscapacityofABMforcomputingequilibri-
ums(whenthereare)inagenerativeway:the“exchangewindow”;thespontaneous
andemergingorderofHayek.AEcanthuscontributetogetdeeperinsightsinthis
fascinating “exchange window” approach to solve the complexity of Economics:
how can the autonomous local exchange of heterogeneous, bounded rational and
purposeful agents generate the observed economic behaviour? AE is a generative
approachtoEconomics.ThepurposeofAEistogrowexplanationsandwhenthere
exist, to generate the equilibriums, not just assuming their existence to force the
economicmodels(Epstein,2006).
Two prestigious invited speakers have contributed to the Conference with top-
ics not included in the book. As in previous meetings they represent some of
the interdisciplinary fields that sustain AE: in this case Artificial Intelligence and
Econophysics.Agents,InformationandNegotiationisthetalkofCarlesSierra,Pro-
fessorattheInstituteofResearchonArtificialIntelligenceoftheSpanishCouncil
for Scientific Research at Barcelona. Successful negotiators fix up by their posi-
tion along five dimensions: Legitimacy, Options, Goals, Independence, and Com-
mitment, (LOGIC). He introduces a negotiation model based on these dimensions
andontwoprimitiveconcepts:intimacy(degreeofcloseness)andbalance(degree
offairness).Theintimacyisapairofmatricesthatevaluatebothanagent’scontri-
butiontotherelationshipanditsopponent’scontribution.Eachmatrixincludesan
informationviewandfromautilitarianviewacrossthefiveLOGICdimensions.The
balanceisthedifferencebetweenthesematrices.Thenegotiationstrategymaintains
a set of Options that are in-line with the current intimacy level. The tactics wrap
theOptionsinargumentationwiththeaimofattainingasuccessfuldealandmanip-
ulating the successive negotiation balances towards the target intimacy. Summary
measuresoftrustandreputationarealsodiscussed.
HowdoMicro-EconomicSimpleRulesGenerateComplexMacro-EconomicBe-
havior?AnAgentBasedModellingApproachforConnectingEmpiricalFactswith
TheoreticalPredictionsisthetalkbySorinSolomon,ProfessorattheRacahInsti-
tuteofPhysicsattheHebrewUniversityofJerusalem.Foraverylongwhileitwas
customaryinEconomicstoformalizemathematicallyacollectionofmanysimilar
objectsintermsof“representativeagents”or“meanfield”:acontinuousfunctionsin
spaceandtimerepresentingthelocalaverageoftheirindividualproperties.Infact,
this“meanfield”,iswhatkeptthevariousscientificdomainsapart.When“MoreIs
Different”(thetitleofthearticlepublished35yearsagobyNobelLaureatePhilAn-
derson) the singular, extreme, and very rare events become crucial. One can think
about the “elementary” objects belonging to the “simpler” level (say firms in mi-
croeconomics) as the nodes of a network and about the “elementary” interactions
betweenthemasthelinksofthenetwork.Thedynamicsoftheentiresystemisthen
emergingfromtheinteractionsoftheindividuallinksandnodes.Theglobalfeatures
x Preface
oftheresultingnetworkcorrespondtothecollectivepropertiesoftheemergingsys-
tem:(quasi-)disconnectednetworkcomponentscorrespondto(almost-)independent
emergenteconomicbranches;scalingpropertiesofthenetworkcorrespondtopower
lawsinthefirmsdistribution,long-lived(meta-stable)networkfeaturescorrespond
tocriticalslowingdownactivity.Theknowledgeofthecollectiveemergingfeatures
of the network allows one to devise methods to expedite by orders of magnitude
desired processes (or to delay or stop un-wanted ones). S. Solomon describes the
applicationofthisapproachtoMacroeconomicsandconfrontstheresultstoempir-
icaldata.
TheAEConferencesareatwo-daymeetingwithoutparallelsessionstopromote
afullparticipationinthediscussions.Thedisadvantageofthischoiceisthelimita-
tioninthenumberofacceptedpapers.21paperswereselectedfrom56submitted
extendedabstractsafterablindreviewingprocess.Wearegratefultoallthesubmit-
ters.Thecontributionsarearrangedinsevenchapters:Macroeconomics,Industrial
Organization, Market Dynamics and Auctions, Finance, Financial Markets, Infor-
mationandLearning,andMethodologicalIssues
Valladolid,Spain Cesa´reoHerna´ndez
May2009 MartaPosada
AdolfoLo´pez-Paredes
References
Axtell,R.(2008).TheRiseofComputationallyEnabledEconomics.EasternEco-
nomicJournal,34,pp.423–428.
Arrow,K.J.,etal.(2008).ThePromiseofPredictionMarkets.Sciencevol32016
may2008.
Epstein,J.M.(2006).GenerativeSocialScience:StudiesinAgent-BasedComputa-
tionalModeling.PrincetonUniversityPress.
Gala´n, J. M., Lo´pez-Paredes, A., and del Olmo, R. (2009), An agent based model
fordomesticwatermanagementinValladolidmetropolitanarea,WaterResource
Research,45,W05401.
Lo´pez-Paredes, A. Herna´ndez, C. and Pajares, J. (2002) Towards a New Experi-
mental Socio-Economics. Complex Behavior in Bargaining. Journal of Socio-
economics.Vol.:31,pp.423–429.Elsevier.
Schredelseker, K., and Hauser, F. (2008).Complexity and Artificial Markets. Lec-
tureNotesinEconomicsandMathematicalSystems.No614.Springer.Berlin.
Von Neumann, J., and Morgenstern, O. (1944). Theory of Games and Economic
Behavior.PrincetonUniversityPress,Princeton,NJ.
Contents
PartI Macroeconomics
1 A Potential Disadvantage of a Low Interest Rate Policy: the
InstabilityofBanksLiquidity ................................. 3
GianfrancoGiulioni
1.1 Introduction.............................................. 3
1.2 TheBank ................................................ 5
1.2.1 ProfitabilityandLiquidity .......................... 6
1.2.2 AnIdealizationoftheBankActivity.................. 6
1.2.3 TheLendingActivity .............................. 7
1.3 Simulations .............................................. 9
1.4 ComparativeStatic,DynamicsandCreditRationing ............ 11
1.5 Conclusions.............................................. 12
References..................................................... 13
2 KeynesintheComputerLaboratory.AnAgent-BasedModelwith
MEC,MPC,LP............................................. 15
GiuliaCanzian,EdoardoGaffeoandRobertoTamborini
2.1 Introduction.............................................. 15
2.2 TheModel............................................... 17
2.2.1 MethodologicalPremises ........................... 17
2.2.2 ModellingtheMarketSentiment..................... 18
2.2.3 TheMarginalEfficiencyofCapital................... 19
2.2.4 TheMarginalPropensitytoConsume................. 20
2.2.5 TheLiquidityPreference ........................... 21
2.2.6 AggregateSupply ................................. 22
2.3 SimulationsResults ....................................... 23
2.3.1 GDPSeries....................................... 23
2.3.2 GDPanditsComponents ........................... 25
2.4 Conclusions.............................................. 26
References..................................................... 27
xi