2025哈佛大學生成式 AI 和仿真建模使用 ChatGPT等大型語言模型

生成式AI和仿真建模使用ChatGPT等大型語言模型FASTGenerativeAIandsimulationmodeling:howshouldyou(not)uselargelanguagemodelslike

生成式AI和仿真建模：使用ChatGPT等大型語言模型growthintheUnitedStates.However,ourguidelinesaregenericandcanbeappliedtoabroaderCopyright?2024TheAuthors.SystemDynamicsReviewpublishedbyJohnWiley&SonsLtdonbehalfofSystemDynamicsSociety.Syst.Dyn.Rev.AdditionalSupportingInformationmaybefoundonlineinthesupportinginformationtabforthisGenerativeAIholdsimmensepotentialforresearchwhileposingchallengesplyprovide“endlessentertainment”(Thorp,2023,p.313).IntheearlydaysofChatGPT,itgeneratedandreferencedscientificstudiesthatdidnotexisteralmajorjournals,includingbutnotlimitedtoNatureandScience,releasedguidanceontheresponsibleuseofgenerativeAI,andNatureaMGHInstituteforTechnologyAssessment,HarvardMedicalSchool,Boston,Massachusetts,bSloanSchoolofManagement,MassachusettsInstituteofTechnology,Cambridge,Massachusetts,*Correspondenceto:MohammadS.Jalali,MGHInstituteforTechnologyAssessment,125NashuaSt.,Boston,MA\hAcceptedbyAndreasGr??ler,Received21February2024;Revised2April2024;Accepted23AprilSystemDynamicsSystemDynamicsPublishedonlineinWileyOnlineLibrary()DOI:10.1002/sdr.1773ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttribution-NonCommercialLicense,whichpermitsuse,

生成式人工智能（AI）工具，如大型語言模型（LLM）和ChatGPTAI?領(lǐng)域的潛力以及缺乏其有效部署指南方面，仍然存在差距。本文旨在彌合這些差距。我們通過一個建模COVID?19對美國經(jīng)濟增長影響的例子來討論ChatGPT的應(yīng)用。然而，我們的指南是通用的，可以應(yīng)用于更廣泛的生成式AI工具。我們的工作提出了一種系統(tǒng)的方法，將生成式AI整合到仿真研究連續(xù)體中，從問題闡述到見?2024·SonsLtdSyst.Dyn.Rev.生成式人工智能（AI）和大型語言模型（LLM），例如ChatGPT，已經(jīng)得到了研審查（例如，Ariyaratne等人，2023；Castellanos?Gomez，2023），但它們對實際上很少產(chǎn)生研究產(chǎn)出。他們指出，像ChatGPT這樣的工具會產(chǎn)生“看似可信但實際上錯誤”的回應(yīng)（Shen等人，2023）以及僅僅提供“無盡的娛樂”（Thorp，2023，第313頁）。在ChatGPT的早期階段，它生成并引用了不存在的科學文獻（Thorp，2023），其他平臺也存在類似問題。Meta的Galactica產(chǎn)生了不準確和具有種族歧視的內(nèi)容（Stokel?Walker和VanNoorden，2023）。在我們的經(jīng)驗中，我們觀察到Google的Bard在聲稱從GoogleScholar發(fā)布了關(guān)于生成式人工智能負責任使用的指南，以及《自然》（NatureaMGHInstituteforTechnologyAssessment，blhlfhfhlbdh）。通訊作者：MohammadS.Jalali，MGHInstituteforTechnologyAssessment，125NashuaSt.，Boston,MA02114USA\hmsjalali@AcceptedbyAndreasGr??ler,Received21February2024;Revised2April2024;Accepted23April于WileyOnlineLibrary(wileyonlinelibrary.comDOI：10.1002/sdr.1773

2Editorial,2023),amongseveralothers,prohibitedtheinclusionofgenerativeintegrityofthework(Flanaginetal.,2023;Thorp,2023).Ontheotherhand,somearguethatgenerativeAIcancontributetotheresearchprocess.TheysuggestthatgenerativeAIoffersgreatpotentialtoimproveaca-demicworkefficiency(Meyeretal.,2023),andtoolslikechatbotsenabletheexplorationof“unchartedscientificterritories”(ThapaandAdhikari,2023,tionmodeling.Inthisarticle,weaimtoclarifyanddemonstratehowtousegenerativeAIBackgroundandofLLMs.Thesemodelsaretrainedonvastamountsoftextdatatopredictthenextwordinasequence,andtheycanbeusedtogeneratecoherentandcontextu-allyrelevantcontent.WhileLLMsareasubsetofgenerativeAIthatfocusesonorotherformsofcontent.DOI:

作品的原創(chuàng)性和完整性負責（Flanagin等人，2023；Thorp，2023）。智能具有提高學術(shù)工作效率的巨大潛力（Meyer等人，2023），而像聊天機?人這fi（ThapaAdhikari，20232647Ghaffarzadegan等人，2024）。例如，ChatGPT可以生成研究想法、文獻綜述和數(shù)據(jù)摘要（Dowling和Lucey，2023）。這些工具有助于研究人員修改和審閱稿件以及開發(fā)或檢查代碼（Stokel?Walker和VanNoorden，2023）。也有人提出，它關(guān)于生成式人工智能應(yīng)用的價值及其對研究過程直接（例如，一手研究過程）本文旨在闡明并演示如何在仿真建模研究中使用生成式AI。我們還討論了如何利用生成式AI將口頭和書面提示轉(zhuǎn)化為輔助研究過程的結(jié)果。我們以COVID?19爆發(fā)及其對美國GDP的影響的簡化案例進行研究。特別是，我們考慮了COVID?19作為一個外生因素影響簡化的消費乘數(shù)模型（薩繆爾森，1939；洛，1980）。最后，該案例提供了一個現(xiàn)實的環(huán)境，可以在實際中測試生成式AI的應(yīng)用。在這篇文章中，我們展示并強調(diào)，生成式AI不應(yīng)取代思考；相反，它是一種有大型語言模型（LLMs）是經(jīng)過深度學習訓練以理解和生成人類語言的模型。ChatGPT和Bard，它們使用生成式Transformer架構(gòu)，是這類模型的例子，屬于更廣泛的LLMs家族。這些模型在大量文本數(shù)據(jù)上訓練，以預(yù)測序列中的下一個單詞，并且可以用來生成連貫且與上下文相關(guān)的文本內(nèi)容。雖然LLMs是關(guān)注文本的生成式AI的一個子集，但生成式AI還可以包括生成圖像、音樂、視頻或其他形式內(nèi)容的最近，不同的AI平臺開始提供組合功能。谷歌的Bard、微軟的Bing和OpenAI的ChatGPT等平臺，被稱為對話式AI模型，它們提供LLMs和其他生成式AI工具（如圖像處理和高級數(shù)據(jù)分析）的混合，使它們成為研究過程中的有用10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

A.Akhavan和M.S.Jalali：生成式AI和仿真建模writingthisarticle.Therefore,wefocusontheapplicationsofGPT-4whileacknowledgingthatmoreevolvedtechnologiesmaybeintroducedinthenearDavisetal.,2007;Lawetal.,2007;Sterman,2000)haveproposedstepsofthecesshasbeenwidelyemphasizedintheliterature(e.g.Harrisonetal.,2007;andmaintenancetoenhancetransparencyandreplicabilityofthesimulationresearch,anareathatstillhassignificantroomforimprovement(Jalalietal.,2021;JalaliandBeaulieu,2023;Monksetal.,2019;RahmandadandSterman,2012).study.Webuildupontheframeworksonhowtoconductsimulationmodelingresearchanddeveloptheoriesfromcasestudies(Davisetal.,2007;Sterman,2000).Weconsideredgeneralstepsthatareapplicabletomostsimulationmodelingcanhelpshapeandrefinetheprocess.Itisimportanttonotethatsimulation(i.e.heterogeneityinhowsentencesareformed),theyshouldreceiveclearinstructions(prompts)fromusers.However,theabsenceofestablishedguidelinessuchcases,werevisedpromptstoachievesatisfactoryresponses,knownaslikeChatGPT.ChatGPT’sresponsesareoftenlong;therefore,tokeepthisarticleconcise,wecreationandenhancementofprompts,forexample,iterativeprocesstorefineUsinggenerativeAIinsimulationmodeling—caseDOI:

截至本文撰寫之時，GPT?4（最新版本）提供了最強大的功能。因此，我們專注于GPT?4的應(yīng)用，同時承認在不久的將來可能會出現(xiàn)更先進的技術(shù)。者（例如，Banks，2005Davis2007Law2007Sterman，2000）提出了模擬研究中的建模步驟，這一過程的迭代性質(zhì)在文獻中得到了廣泛強調(diào)（例如，Harrison等人，2007；Homer，1996）。我們遵循同樣的方法，將自己置于進行模擬研究的學者的位置，并從ChatGPT這一領(lǐng)域仍有很大的改進空間（Jalali等人，2021；Jalali和Beaulieu，2023；Monks等人，2019；Rahmandad和Sterman，2012）。行模擬建模研究和從案例研究中發(fā)展理論（Davis等人，2007；Sterman，2000）方法，并展示了ChatGPT如何幫助塑造和改進這一過程。需要注意的是，模擬建模此外，由于生成式AI工具，如ChatGPT，依賴于隨機模型（即句子形成的異質(zhì)是提供精心制作的提示示例，這些示例說明了如何與生成式AI進行有效溝通。這些示例為用戶提供了實際指南，幫助他們了解如何制定能夠從ChatGPT等工具中獲得以下，我們首先展示這些提示和ChatGPT的相應(yīng)響應(yīng)。ChatGPT的響應(yīng)通常AI在此，我們通過一種交互式對話方法展示了生成式AI在仿真研究中的應(yīng)用。我們的10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

4intheeducationalprocessofrefiningthevariousstepsinvolvedinmodeling.

對于剛開始在仿真建模領(lǐng)域探索的學生或初級研究人員來說，這種方法旨在展示這些工具的利用，并協(xié)助他們在建模過程中各個步驟的教育過程。雖然我們故意引入了一些錯誤（[]標記這些故意錯誤以避免混淆），我們的方法不僅限于錯誤糾正。通過這種基于對話的方法，我們旨在為學習者提供見AIProblemdefinitionandresearchGenerativeAIcanhelprefineresearchquestionsandexploreproblemback-withresearchboundaryconsiderationsbysuggestingaspectsresearchersmaynotResearchquestionandIamstartingsimulationmodelingresearchandwanttocross-checkmyresearchquestion.Ihaveconsideredthisquestion:HowhaseconomicgrowthimpactedCOVID-19?Youshouldbecritical,findpotential?awsinmyresearchquestion,andsuggestimprovements.[WeintentionallyreversedthecausalityandResponseChatGPTprovidedfeedbackonresearchquestionclarityanddirectionality,theresearchquestion’sclarityanddirectionality,itnotedthatour“researchques-economicgrowthhasbeenaffectedbythepandemicorhoweconomicgrowthonyourintention,youmightneedtorewordforclarity.”ityandcorrelation.Itsuggestedthat“Sayingeconomicgrowth‘impacted’COVID-19assumesacausalrelationship.However,inreality,manyvariablesringtothegrowththatoccurredbeforethepandemic,duringit,orafter?Thisneedstobedefined.”DOI:

Problemdefinitionandresearch生成式AI可以幫助細化研究問題和探索問題背景。它可以理解研究問題的廣度和范圍，并分析研究問題變量之間的語義和因果關(guān)系。連接到互聯(lián)網(wǎng)的生成式AI可以搜COVID?19在缺陷，并提出改進建議。我們故意顛倒了因果關(guān)系，并考慮了廣泛的術(shù)語而不是Responsesummar（輸入內(nèi)容未提供足夠信息，無法進行翻譯。ChatGPT了反饋。關(guān)于研究問題的清晰度和方向性，它指出我們的研究問題似乎暗示經(jīng)濟關(guān)于因果關(guān)系，ChatGPT“‘’COVID?19ChatGPT還是大流行后的增長？這需要定義?！盙DP10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

A.Akhavan和M.S.Jalali：生成式AI和仿真建模RevisedresearchThesearegoodpoints.IseethatImadeamistakeinthedirectionofcausalityandthatIwasnotspecificenough.ItshouldhavebeentheimpactofCOVID-19trendfrom2013to2023,andIwanttofocusontheU.S.economy.Withthoseaspects,myrevisedresearchquestionis:HowdidchangesinconsumerbehaviorduringtheCOVID-19pandemicimpactU.S.GDPgrowth?Howdoesthatsound?Responseestablishabaselinetrendbeforethepandemicandthenassessanydeviationsfrombackoncounterfactualscenarios,confoundingfactors,anddatasources.Forexam-Canyousearchforsimilarpeer-reviewedjournalarticlesusingsimulationresearch?CanyoualsointroduceafewotherreliablesourcessothatIcangetdataonGDPgrowthandrelevanteconomicmeasures?ResponserecentversionsofChatGPTproviderelevantliteraturewithlinkstopublisheddata.ChatGPTalsoprovidedabriefoverviewofthecontentofthestudies.Forexample,itsaidthatthegivenarticle“discussesamodelthatinterlinksthelikeCOVID-19,”andaboutthedatabaseitsaid,“St.LouisFederalReservereportedreferencethatwedonotreporthere.WhatChatGPTprovidesisnotexhaus-targetedliteraturesearchandcomplementmanualsearchesindatabases.DOI:

市場表現(xiàn)、就業(yè)創(chuàng)造，還是其他指標？您說的“影響”，是指對病例數(shù)、死亡率、COVID?19關(guān)注GDP增長。關(guān)于時間動態(tài)，我正在考慮2013年至2023年的GDP趨勢，并想專注于美國經(jīng)濟。有了這些方面，我修改后的研究問題是：COVID?19大流行期間消費者行為的變化是如何影響美國GDP增長的？這個想法怎么樣？ResponseChatGPT承認了我們對研究問題的修改，并提供了如下反饋通過指定2023COVID?19對此趨勢的偏離。此外，它還提供了關(guān)于反事實情景、混雜因素和數(shù)據(jù)來源的反饋。例如，它建議考慮在沒有大流行的情況下GDP增長會是什么樣子，或者像財政政策GDP來源，以便我獲取關(guān)于GDP增長和相關(guān)的經(jīng)濟指標的數(shù)據(jù)嗎？Responsesummar（輸入內(nèi)容未提供足夠信息，無法進行翻譯。與早期的聊天機?人不同，它們編造了不存在的引用，ChatGPT的最新版本提供了相關(guān)的文獻，并附有已發(fā)表材料的鏈接。針對我們的查詢，ChatGPT提供了一篇相關(guān)期刊文章（Xiang等人，2021）。此外，它還提到了美國經(jīng)濟分析局和圣路易斯聯(lián)邦儲備銀行（FRED）等數(shù)據(jù)庫，這些數(shù)據(jù)庫報告了GDP增長數(shù)據(jù)。ChatGPT還簡要概述了研究的內(nèi)容。例如，它說，所提供的文章“討論了一個將經(jīng)濟中勞動力供應(yīng)的動態(tài)與COVID?19等傳染病的傳播相互關(guān)聯(lián)的模型”以及關(guān)于數(shù)告。ChatGPT提供的內(nèi)容并不全面；因此，可以更系統(tǒng)地執(zhí)行此類過程，以協(xié)助有10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

6Fig.1.InitialmodelofprovidedinaChatGPT

1由ChatGPT提示提供 Modeltualmodelsandidentifytherelationshipsamongtheidentifiedvariables.Wethechoiceofvariablesandtheirnaming,directionofcausality,polarityoftheConceptualIwantyourfeedbackonmyattempttoidentifyvariablesanddevelopacon-ceptualmodelforincludingCOVID-19shockinasimplifiedmacroeconomicmodeloftheconsumptionmultiplier.Ihaveputtogetherthekeyrelationshipsformymodel.Canyouchecktheattachedphotoandgivemeyourfeedback?Becrit-Responsemadeintentionallywhileacknowledgingthecorrectaspects.Itrecognizedtheditureandaggregatedemandbyspecifying:“Thisisunusual.Generally,anincreaseingovernmentexpenditure,allelsebeingequal,wouldleadtoDOI:

AIAI能夠分析概念模型的復(fù)圖像（圖1）；然而，圖像的內(nèi)容也可以簡單地用文字來呈現(xiàn)。生成式AI可以提供的反饋。生成式AI還可以檢查模型變量的定義并建議新的變量。ConceptualCOVID?19附上的照片并給我反饋嗎？請?zhí)岢雠u意見。我們故意在概念模型中刪除了一些變ResponseChatGPT極性。然后，它識別了我們故意犯的錯誤，同時承認了正確的方面。它通過指出“隨著GDP的增長，我們預(yù)計人口的平均收入或預(yù)期收入將上升”，識別了GDP和預(yù)期收入之間錯誤的因果極性。它還通過指定這是不尋常的。通常情況下，在其”，識別了政府支出和總此外，ChatGPT還提出了額外的變量，建議了一些在GDP宏觀經(jīng)濟模型中常見的因10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

A.Akhavan和M.S.Jalali：生成式AI和仿真建模feedbackloop:“Inreality,anincreaseinaggregatedemandmightleadtoantiontrade-off,suchthatwhileitisimportanttokeepthemodelsimple,weshouldensurethatitstillcapturesthenecessarycomplexityofthereal-worldAsnotedearlier,ChatGPT’sresponsesareoftenlong.WeprovideditsfullIhaverevisedmyconceptualmodelandhaveattachedtheupdatedversion[-Figure2].Ifixedthepolarityerrors.IconsideredtheimpactofNetExportsonGDPinadditiontoAggregateDemand.IalsoaddedthecausallinkbetweenCon-sumptionandAggregateDemand.Forsimplicity,Ihavenotincludedparameterssuchasinitialvaluesandadjustmenttimes,andtheconceptualmodelonlycap-turesthebasicdynamics.IwanttomoveforwardwiththisversioniftherearenoResponserelationshipsonebyone,andprovidedfeedbackaboutthemodel’salignmentbeunderstandabletothosefamiliarwithbasicmacroeconomicprinciples.”Itnotedanimportantobservationthatthemodelmissestimelags:“Whilenotrepre-

加可能會導致GDP的增加，進而影響預(yù)期收入，從而影響消費，形成循環(huán)效應(yīng)?！比缜八觯珻hatGPTS1我考慮了凈出口對GDP的影響，除了總需求之外。我還添加了消費和總需求之間的ResponseChatGPT與已建立的經(jīng)濟理論的吻合度的反饋。它指出，“該模型以清晰的方式呈現(xiàn)，具有易理解的。它還指出一個重要的觀察結(jié)果，即模型缺少時間滯后雖然簡化模型中沒有表示，但請注意這一點?！彼€提醒我們，該模型沒有捕捉到所有潛在的因果關(guān)系，這與我們縮小這個簡單模型邊界的意圖相符。在這方面，它還指出，“雖然你的模型是簡化的，但請注意，在一個更動態(tài)的模型中，GDP的變化可以反饋到政府Fig.2.Revisedconceptualmodel

圖2.在ChatGPT提示DOI:

10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

v18v2exports,creatingcomplexinteractions.”Thisisagreatpoint;however,wedidthreevariables,andassuch,theirhistoricaleffectsarealreadycaptured.IftGPTremindedusthatallmodelsaresimplificationsofreality,andweshouldditionsorpolicyregimes.

出口，創(chuàng)建復(fù)雜交互。這是一個很好的觀點；然而，鑒于我們使用外生數(shù)據(jù)來表示這超出了當前簡單模型的范圍。最后，ChatGPT提醒我們，所有模型都是現(xiàn)實的一Modelformulationandetersareinreasonableranges.Additionally,generativeAItoolsaregreatfortrans-latingwrittenmathematicalequationsandverbalcommunicationsintothecodes.Iwanttodevelopasimulationmodelbasedonmyconceptualmodelandobservethebehaviorofthevariablesovertimeatanaggregatelevel.BasedonwhatIhavelearnedinmyclassesandsomeinitialsearches,Ithinkacompart-mentalmodelingapproachwouldbeareasonablechoice.Whatdoyouthink?ResponseChatGPT’sresponseemphasizedthesuitabilityofusingacompartmentalmodelingapproach,typicallyseeninepidemiologyandsystemsdynamics,foricsofaggregatedemand,supply,income,andconsumption;thecapacitytounderstandthedynamicbehaviorofeconomicsystems,includingdelaysandfeedbackloops;itsutilityinsimulatingpolicyinterventionsandtheirimpactstations:thepotentialoversimplificationofcomplexeconomicinteractions;thereal-worldpolicy.ChatGPTconcludedthatwhileacompartmentalmodelisagoodstartingpointforobservingeconomicvariablebehaviorovertime,espe-ciallyinresponsetoshockslikeCOVID-19,amoreextensiveanalysismayDOI:

AIAI這些工具通過提供編程指南，可以促進代碼開發(fā)過程，并將代碼從一種語言翻譯成另AIResponsesummar（輸入內(nèi)容未提供足夠信息，無法進行翻譯。ChatGPT的回答強調(diào)了使用分室建模方法（重要。然而，ChatGPT也指出了局限性：復(fù)雜經(jīng)濟互動可能被過度簡化；準確參數(shù)挑戰(zhàn)；以及將模擬結(jié)果謹慎地轉(zhuǎn)化為現(xiàn)實世界政策的需要。ChatGPT總結(jié)說，雖然COVID?1910991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

A.Akhavan和M.S.Jalali：生成式AI和仿真建模ModelformulationsandparameterThanks!Ifeelmoreconfidentaboutmydecisiontochooseacompartmentalmodelingapproach.Ihavedefinedtheequationsbelowandamtryingtouseordinarydifferentialequationstomodelthedynamicrelationshipsamongthevariables.Forthisdraft,Ihaveconsideredahypotheticalcaseandassignedarbi-trarynumberstoparameters.Becriticalandgivemefeedbackontheequationsandparametervalues.[Weintentionallyusedawrongequationfor“Aggregate Income=INTEGRAL inExpected ExpectedIncome).Consumption=MarginalPropensitytoConsume×ExpectedtionTime.×NetAdjustmentTime.GovernmentExpenditure=Investment=pensitytoConsume.Alpha=ResponseOverall,ChatGPTacknowledgedthatusingordinarydifferentialequations(ODEs)isagoodstartandremindedustoensurethatoursimulationenvironmentaggregatedemand,indicatingthat“theaggregatedemandistheproductofitsthatparametervaluesseemarbitraryandcommentedontheirintuitions.ForquicklyorslowlyagentsupdatetheirexpectationsinresponsetochangesinGDP.Italsorecognizedtheothererror,suggesting“notethattheMarginalPropen-haveMPC=80,whichsuggeststhatforeveryunitofexpectedDOI:

并為參數(shù)分配了任意數(shù)值。請批評并就方程和參數(shù)值給我反饋。我們故意使用了錯誤的“總需求”方程，并為“邊際消費傾向”分配了一個異常高的值。預(yù)期收入積分（預(yù)期收入變化，初始預(yù)期收入）。?消費消費邊際傾向預(yù)期收入。?（GDP預(yù)期收入）GDP積分（GDP變化，初始GDP）。?總需求=消費投資政府支出×GDP（總需求+—GDP）?2初始預(yù)期收入250。?GDP250。?政府支出80。?10。?凈出口=10。?消費邊際傾向=IF2020時間≤2020.25THEN參考消費邊際傾向×（1—Alpha）ELSE參考消費邊際傾向。?生產(chǎn)調(diào)整時間1。?參考消費邊際傾向=80。Alpha=0。Responsesummar（輸入內(nèi)容未提供足夠信息，無法進行翻譯。總的來說，ChatGPT承認使用常微分方程（ODEs）有方程，并指出了我們在總需求定義中犯的錯誤，指出總需求是其三個組成部分加法模型。它還指出參數(shù)值似乎任意，并對其直覺進行了評論。例如，它指出，預(yù)期形成時間的值應(yīng)該反映代理人對GDP變化的反應(yīng)速度。它還認識到其他錯誤，建議請注意，邊際消費傾向通常是介于0和1之間的分數(shù)。在你的參數(shù)中，你MPC8010991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

10valuebetween0and1.”Italsoaddedthatoverlysimplisticmodelsmightnotfitwithinthebroaderframeworkofthemodel.RevisedequationsandvariableImadeamistakeindefiningAggregateDemandandsettingtheparametervalueofMarginalPropensitytoConsume.IhaverevisedthoseandconsideredtheadditiveformulationforAggregateDemandand0.80forMPC.Ialsoaddedtheunitsofmeasureforeachvariablebelow.Theunitsseemfinetome.Canyoucheckmyrevisedequationsandforunitconsistency?[Weintentionallyused

8001它還內(nèi)容，并考慮了總需求的加法公式和0.80的MPC。我還為下面的每個變量添加了度位一致性嗎？GDP]

[在本文中，我們僅展示修 ed+NetReferenceMarginalPropensitytoConsume=ExpectedIncome:Consumption:ChangeinExpectedIncome:$/(Year×GDP:AggregateDemand:ChangeinGDP:ExpectationFormationTime:InitialGDP:GovernmentExpenditure:Investment:NetExports:MarginalPropensitytoConsume:ResponseChatGPTreviewedandverifiedtherevisedequations.Itcheckedforconsis-fiedinconsistentunits:“TheunitofGovernmentExpenditureshouldbe$/YeartoDOI:

需求總量消費+投資++凈出口。?參考邊際消費傾向0.8預(yù)期收入：$/年。?消費：$/年。?預(yù)期收入變化：$/（第×年）。?GDP：$/年。?總需求：$/年。?GDP變化：$/季度。?預(yù)期形成時間：年。?初始預(yù)期收入：$/年。?初始GDP：$/年。?政府際消費傾向：無量綱。?生產(chǎn)調(diào)整時間：年。AlphaResponseChatGPT的一致性。它識別出不一致的單位$保持一致。它還建議“GDP變化單位的調(diào)整應(yīng)與方程匹配。它還?2024·SonsLtd10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

A.Akhavan和M.S.Jalali：生成式AI和仿真建模butyou’velistedtheunitsas$/Quarter.EitheradjusttheunitsinyourlistorIfixedtheincorrectunits.IthendevelopedthePythoncodebelowformyordi-narydifferentialequations[forpresentationinthisarticle,wereportthecodeinAppendixS2intheonlinesupportinginformation].Iwantthemodeltostartfrom2013totheendof2022andthemodelupdatestohappenattimeintervalsof0.25.Irunthemodel;itworks,andIcanseetheresults.Canyoucheckifthereareanyissueswiththecode?Anythingtoimprove?Donotproviderevisedcode;instead,tellmehowtoimproveitpiecebypiece,asIwouldliketolearnhowtoimprove.Responsetheplotwecreatedtoshowtheresults.Itmentionedthatourcodelackscom-mentsexplainingthepurposeoffunctionsandvariables,makingithardtounder-standthelogicandflow.Italsomentionedseveralvariablenamesareabbreviationsthatmaynotbeimmediatelycleartosomeonereadingthecodetionandsuggesteddefiningseparatefunctionsforeachequationorgroupoftionedthatwespecifiedthetimerangeasnp.arange(2013,2023,0.25),whichdoesnotincludetheendofthelastquarterof2022.Weshouldinsteadextendtherangeto2023.25tomodeltheentireperioduptoandincludingtheendoftion.Italsonotedthattheinitialconditionsshouldmatchthoseof2013andemphasizedtheimportanceofcheckingthemodelagainstreal-worlddata

$$我修復(fù)了錯誤的單位。然后，我開發(fā)了下面的Python代碼來展示我的常微分方程[，我們將在附錄S2的在線補充信息中報告該代碼]。我希望模型從2013年開始20220.25ResponseChatGPT及我們創(chuàng)建的用于展示結(jié)果的圖表中的單位提出了評論。它提到，我們的代碼缺少解釋函數(shù)和變量目的的注釋，這使得理解邏輯和流程變得困難。它還提到，幾個變量名是縮寫，可能對閱讀代碼的人來說并不立即清楚，并建議使用全描述性名稱。此外，ChatGPT“model”np.arange(2013,2023,0.25)20222023.2520222023Python2013ModeltestingandGenerativeAIcanhelpfacilitateevaluatingandassessingsimulationmodels.Theycanalsoprovidedataanalysisandinterpretation,suggestmodelrefine-ments,facilitatemodelcalibrationandoptimization,andhelpwithsensitivityanalysis.Whiletheycansimulateandanalyzecode,theycaninterpretmodelBelow,Iincludetheupdatedversionofmycodebasedonyourfeedback[seeAppendixS2intheonlinesupportinginformation].Now,I’mtryingtoassessDOI:

生成式AI可以幫助促進評估和評估仿真模型。它們還可以提供數(shù)據(jù)分析與解釋，提和分析代碼，但它們可以解釋模型結(jié)果與數(shù)據(jù)的關(guān)系（3）并提供增強S2]?，F(xiàn)在，10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//:sptth(snoitidnoCdnasmreTehteeS.]4202/70/22[noyrarbiLenilnOyeliWlatipsoHlareneGsttesuhcassaMyb3771.rds/2001.01/iod/moc.yeliw.yrarbilenilno//:sptthmorfdedaolnwoD,0

12Fig.3.Usingdata

圖3.在ChatGPT提示 model.Withmymodelvariablesbeingbasedontheirhistoricaldata,Iexpectthatmymodeloutcome,thatis,simulatedGDP,bereasonablyclosetothehistor-icaltrendsofGDP.ButwhenIplottheSimulatedGDPagainsthistoricaldata,Idon’tseeaclosereplicationofthehistoricaltrend.Iamsureaboutthedataqual-ity,though,andIalreadycheckedforunitconsistency.Givemefeedbackbasedonthecodeandtheattachedresultsfigure.Iaimtolearnhowtoimprovethemodeltoreplicatethehistoricaltrendclosely.ResponsetoensuretheCOVID-19shockisappropriatelymodeledandtheassumptionsment,governmentexpenditure,andnetexports.(Inourinitialmodelensurethattheextrapolationbeyondthedatasetisreasonableandalignswitheco-tionFormationTime,ProductionAdjustmentTime,andReferenceMarginalexaminingthesystemofOrdinaryDifferentialEquationstoensuretheycapturethecomplexdynamicsofaneconomyduringshockperiods.Italsosuggestedconsider-DOI:

模型。我的模型變量基于其歷史數(shù)據(jù)，我預(yù)計我的模型結(jié)果，即模擬GDP與GDP的歷史趨勢相當接近。但當我將模擬GDP與歷史數(shù)據(jù)對比時，我沒有看到歷ChatGPT對COVID?19析提供了反饋。首先，它要求我們確保COVID?19沖擊得到適當?shù)慕＃⑶壹僭O(shè)適如投資、政府支出和凈出口。（在我們的初始模型概念化中，如圖1所示，我們只提到了這三個變量，但它們的名稱中沒有包含“數(shù)據(jù)”這樣的字樣；然而，在收到這一10991727,0,Downloaded10991727,0,Downloadedfrom/doi/10.1002/sdr.1773byMassachusettsGeneralHospital,WileyOnlineLibraryon[22/07/2024].SeetheTermsandConditions(/terms-and-conditions)onWileyOnlineLibraryforrulesofuse;OAarticlesaregovernedbytheapplicableCreativeCommonsesneciLsnommoCevitaerCelbacilppaehtybdenrevogeraselcitraAO;esufoselurrofyrarbiLenilnOyeliWno)snoitidnoc-dna-smret/moc.yeliw.yrarbilenilno//: