AI能读懂民意吗？合成虚拟人物预测政策态度的实证研究+Can+AI+Read+the+Room？Predicting+Policy+Sentiment+with+Synthetic+Personas

IDEATION

CENTERINSIGHT

CanAIread

theroom?

Predictingpolicy

sentimentwith

syntheticpersonas

EvidencefromtheMiddleEast

Contacts

Dubai

DimaSayess

Partner

+971-4-436-3000

dima.sayess

@

FatimaKoaik

Principal

+971-50-701-8240

fatima.koaik

@

TheIdeationCenter

TheIdeationCenteristhemissionlabofStrategy&MiddleEast,partofthePwCnetwork.

Withoriginsasoneoftheregion’smostprominentthinktanks,wepartnerwithgovernments,

businesses,andleadingacademicinstitutionstotacklecomplexchallengesandshapethe

future.Throughactionableforesight,innovationasaservice,behavioraleconomics,impact

evaluation,andadvanceddataanalytics,wehelporganizationsanticipatechange,testnew

ideas,buildtransformativecapabilities,andscalesolutionsthatcreatelastingimpact.Bringingtogetherdiverseexpertisefromacrossdisciplines,wecombineboldthinkingwithpractical

actiontodriveimpactacrosssociety,theeconomy,andtheenvironment.Formoreinfo,visitourwebsiteon:

/m1/en/ideation-center.html

ABOUTTHEAUTHORS

DimaSayessisapartnerwithStrategy&MiddleEast,partofthePwCnetwork,anddirectorof

theIdeationCenter,theleadingthinktankforStrategy&MiddleEast.Shehasmorethan20yearsofexperienceinpublic-sectorconsulting,withafocusoninnovationinpolicymaking,governmentofthefuture,andqualityoflife.SheformerlyservedasanadvisortotheministeroffinanceandeducationinLebanon,andtheExecutiveCouncilofDubai.

Dr.FatimaKoaikisbehavioraleconomicsdirectorattheIdeationCenter,wheresheworksattheintersectionofappliedresearchandconsulting.ShecompletedherPh.D.inthePsychologyandBehaviouralScienceDepartmentatLondonSchoolofEconomicsandPoliticalScience(LSE),

ledtheestablishmentofseveralbehavioralunits,andhasconductedmorethan50experiments.HerpreviousexperienceincludesworkingasabehavioralscientistattheWorldBank’sMind,

BehaviorandDevelopmentUnitandattheUnitedNationsDevelopmentProgramme.

Dr.RobinSchinderisbehavioraleconomistmanagerattheIdeationCenter,whereheconducts

experimentsandevidence-basedresearchandadvisesclientsandpolicymakersonstrategicchallenges.HepreviouslyworkedasapostdoctoralresearcherattheUniversityofZurich,whereheearnedhisPh.D.inmanagementandeconomics,applyingmethodsofexperimentaleconomicstovarioustopicssuchascorporatesocialresponsibility,legitimacy,andprosocialbehavior.

PujenShresthaisseniorbehavioralanddatascientistattheIdeationCenter,whereheapplies

behavioralsciencetopolicychallengesintheMiddleEast.Hisworkincludesdesigningand

implementingbehavioralexperimentsrelatedtoentrepreneurship,laborparticipation,andconsumerbehavior.Previously,hewasabehavioralscienceresearcherattheBehaviouralInsightsTeamintheUnitedKingdom.

Dr.DarioKrpanisassociateprofessorofbehavioralscienceintheDepartmentofPsychologicalandBehaviouralScienceatLSEandisaffiliatedwiththeschool’sDataScienceInstitute.Hecompletedan

M.Phil.andaPh.D.in

psychologyattheUniversityofCambridge.Hisresearchfocusistransformativebehavioralchange—howindividualscanadapttheirlifestylestoaddresssomeoftheworld’smost

pressingchallenges,includingclimatechangeandtheriseofAI.

GeorgePhilippeFarajallaalsocontributedtothisreport.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas1

2Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

EXECUTIVESUMMARY

Forpolicymakers,understandinghowthepublicislikelytorespond

toaneworreformedpolicyiscrucialtothatpolicy’ssuccessful

implementationanduptake.Yetmeasuringpolicysentimentthrough

surveysandexperimentsisoftenslow,costly,andchallengingtoscale—especiallywhentheroomthatpolicymakersaretryingtoreadisanentirecountry.TheburgeoningfieldofgenerativeAIpresentsanewapproachtothispersistentchallengethroughtheuseofsyntheticpersonas.

Syntheticpersonasareartificialentitiesgeneratedbylargelanguagemodels(LLMs)tosimulatehumanresponses.Studieshavefoundthattheycanmimichumandecision-makingwitha

moderatetohighdegreeofaccuracy,andtheyofferatoolforexploringpublicresponseswhentraditionalapproachesareconstrainedbytime,cost,orfeasibility.Now,Strategy&’sIdeation

CenterhasextendedthisresearchforMiddleEastpolicymakersbystudyingtheabilityofsyntheticpersonastopredictpolicysentimentinthreeGulfcountries.

Ourstudyfoundthatagreementbetweenhumanandsyntheticpersonaresponsesexceeded90percentformostLLMmodelconfigurations.GPT-4oshowedthebestperformance,generatingsyntheticresponsesthatwerestronglycorrelatedwithhumanresponsesacrossmultiple

policydomainsinallthreecountries.Whilesyntheticpersonascapturedtheoveralldirectionofpublicopinionwell,theirresponsesweremoreconcentratedaroundsimilarviewsand

underrepresentedthevariationandmoreextremeopinionsobservedamongrealrespondents.

TheseresultssupportthreerecommendationsforMiddleEastpolicymakers.First,theycanusesyntheticpersonasinthepreliminarytestingofpublicreceptivenesstoprospectivepolicies,

enablingthescreeningofpotentiallysensitivepolicies.Second,theycanusesyntheticpersonastoproducevaluableearly-stageinsightsevenwhendetaileddemographicdataonthedesired

groupsofpeopleisunavailableorwhenresourcesneededtocollectsuchdataarelimited;

theuseofsyntheticpersonasinthosecasescansupportfasterpolicycyclesthataremore

responsivetopublicneeds.Third,theyshouldnotrelyonsyntheticpersonastopredictthefullvariabilityordistributionofpublicopinion.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas3

THECHALLENGINGMANDATEOFMEASURINGPUBLIC

SENTIMENTINPOLICYMAKING

Understandinghowthepublicperceivesgovernmentpoliciesbeforetheyareenactedand

implementedisaperennialmandateforpolicymakers.Thismandateisbecomingmore

urgentintheGulfcountries,wheregovernmentsarestrivingtodeveloppoliciesundertight

timelines,addresssociallysensitiveissues,andofferexpandedopportunitiesforpublic

participation.Tomeetthesechallenges,policymakersneedtoolsthatcanpredictpublic

reactionsquickly,accurately,andatscale.Newresearchconductedundertheauspicesof

Strategy&’sIdeationCenterfindsthatlargelanguagemodels(LLMs)offerpolicymakersintheMiddleEastsuchatool.

Measuringpublicattitudesinadvanceisessentialforanticipatingthesocialacceptanceof,

compliancewith,andlong-termeffectivenessofnewandreformedpolicies.Untilrecently,

policymakershavereliedonsurveysandexperimentstomeasurepopulation-levelsentiment.However,thesetoolshavesignificantdrawbacks.Theyusuallyrequiresubstantialtimeand

financialresources.Therepresentativesamplestheydependuponcanbedifficulttoassemble.Moreover,thepublicexplorationofpolicytopicsissometimesconstrainedbypolitical

sensitivitiesorregulatoryrestrictions.Thissetofobstacleshaslimitedpolicymakers’abilitytoiterativelytestandrefinepolicydesignsonthebasisofpublicattitudes.

ShortlyaftergenerativeAIwentmainstreamwiththedebutofChatGPTin2022,aninnovative

newpossibilityforunderstandingpublicattitudes—syntheticpersonas—emerged.

1

Synthetic

personasareartificialentitiesgeneratedbyLLMsthatcansimulatehumanresponsestosurveysandexperiments.AsLLMscontinuetobetrainedonthevastcorporaofhumanknowledge,

theyareincreasinglyabletoproducesyntheticpersonascapableofreplicatinghumanresponseswithmoderatetohighaccuracyacrossavarietyofdomains.

Inthedomainofpolitics,studieshavefoundthatsyntheticpersonascanapproximatepublicviewsonvarioustopicsandvotingoutcomes.Forexample,researchconductedincountriesincludingChile,SaudiArabia,theUnitedArabEmirates(UAE),andtheUnitedStates

showedthatsyntheticpersonascan,onaverage,replicatehumanopinionsonsociopoliticaltopicsasdiverseassustainabilityandtrustingovernment.

2

Withrespecttovotingoutcomes,syntheticpersonassuccessfullypredictedtheresultofthe2024presidentialelectioninthe

UnitedStates.

3

4Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

Althoughthesestudiesfoundthatsyntheticpersonastendtobereasonablyaccuratein

predictingaveragepublicopinionandvotingpatterns,theyalsofoundthatsyntheticpersonasoftenfailtocapturethefullvariabilityfoundinhumansamples.Resultsimprovewhen

researchersgobeyondsimpledemographics-basedprompts(suchasaskinganLLMto

answersurveyquestionsasa45-year-oldmanfromTexaswould).Moreadvancedmethods,suchasfine-tuningmodelsonlarge-scalesurveydatasetsorconstructingpersonasfrom

richerqualitativedatasetsthatcapturerealpeople’sbackgroundsandexperiences,producedresponsesthatmorereliablyreplicatedhumandistributions.Inshort,studiescollectively

suggestthatsyntheticpersonascanapproximatehigh-levelopiniontrendsandelectoralpatterns,andthatusingricherdataormoreadvancedtechniquesmayfurtherimprovethesimilaritybetweenhumanandsyntheticresponses.

Despitetherapidlygrowingbodyofresearchonsyntheticpersonas,onenotableareafor

policymakershasbeenlargelyunexplored:whethertheycanaccuratelypredictpeople’sviewsonconcretepolicyproposals.Indeed,withtheexceptionofclimatepolicies,studiesthathaveexaminedsyntheticpersonasinthedomainofpublicopinionhavegenerallyfocusedonbroadsociopoliticalattitudesratherthanonspecificpolicies.

4

Strategy&’sIdeationCentersoughttoanswertheabovequestion(whethersyntheticpersonascanaccuratelypredictpeople’sviewsonconcretepolicyproposals)forpolicymakersinthe

MiddleEastbyconductingarigorousquantitativestudy.Thestudyrevealedthatbyenablingaccuratesimulationsofpeople’sviewsandpsychologicalstatesinarapid,scalable,andlow-costmanner,syntheticpersonasofferanovelandeffectivetoolfortheregion’spolicymakers.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas5

Despitetherapidlygrowingbodyofresearchonsynthetic

personas,onenotableareaforpolicymakershasbeenlargelyunexplored:whethertheycanaccuratelypredictpeople’sviewsonconcretepolicyproposals.

6Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

DESIGNINGTHEUSE-CASESTUDY

Ourstudywasdesignedtoquantitativelyassesstheabilityofsyntheticpersonastoreplicatepeople’sviewsonbothexistingandprospectivepoliciesintheMiddleEast.

5

Todoso,it

comparedtheresponsesofasampleofmorethan1,000people,dividednearlyequallyamongQatar,SaudiArabia,andtheUAE,totheresponsesofsyntheticpersonasgeneratedbyfour

LLMs(GPT-4o,GPT-5,DeepSeek-V3.2-Chat,andDeepSeek-V3.2-Reasoner).UsingmultipleLLMsenabledustoassessrobustnessacrossmodelfamilies.

Thestudydesignalsoenabledsystematiccomparisonsofsyntheticpersonasbasedonthe

amountofinformationusedtoconstructthem.Wegeneratedthreetypesofsyntheticpersonasusingpromptswithincreasinglevelsofparticipantinformation:

•Genericpersonas,generatedusingonlynationalitywithnoindividual-leveldata

•Basicpersonas,generatedusingnationalityandcoredemographiccharacteristicssuchasage,gender,andhouseholdincome

•Extendedpersonas,generatedbyincorporatingadditionalqualitativetraitssuchaspersonalvalues,aspirations,andviews

Thepoliciesincludedinthestudyspannedfivedomains:environmentalsustainabilityand

climateaction;nationalidentityandculturalheritage;innovation,technology,andAI;financialliteracyandeconomicempowerment;andhealthandwell-being(seeExhibit1,page7).Apoolof60existingandprospectivepolicieswasassembled(10existingand10prospectiveforeachofthethreecountries).Wechoseexistingpoliciesthatarecurrentlyimplemented,recognizableandrelevanttothegeneralpopulation,andsufficientlydiversetoreflectthebreadthof

governmentactivityacrosssocial,economic,technological,environmental,andculturalareas.

6

Wecreatedprospectivepoliciesthatreflectrealisticgovernmentpriorities,addressidentifiablechallenges,areconcreteenoughformeaningfulevaluation,andrepresentavarietyofpolicy

instrumentsandmechanisms.

Eachhumanrespondentandsyntheticpersonawasgiven20policies(10existingand10prospective)accordingtotheirnationality.Theywereaskedtoevaluateeachpolicyusingaseven-pointLikertscale.

Existingandprospectivepolicieswereratedacrossseveraldimensions,includingpublicsupport,perceivedimportance,expectedcompliance,andalignmentwithpublic

expectationsandvalues.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas7

THECHALLENGINGMANDATEOFMEASURINGPUBLICSENTIMENTINPOLICYMAKING

Theexperimentinnumbers

60

1,000+

3GCCcountries:

respondents

policiestested

UAE,SaudiArabia,andQatar

5

4

3typesof

syntheticpersonas

policydomains

leadingAI

modelsevaluated

(Generic,Basic,Extended)

8Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

EXHIBIT1

PolicyDomainsandRepresentativePolicyThemesIncludedintheStudy

PolicyDomain

UnitedArabEmirates

SaudiArabia

Qatar

Environmental

Sustainability

andClimateAction

Health

andWellbeing

NationalIdentity

andCulturalHeritage

Innovation,Technology,andAI

FinancialLiteracyand

EconomicEmpowerment

Publictransportintegration,low-carbontransport,marineplasticrecycling

Healthinsurance,childsafety,elderlysupport,mentalwellness

Emiratization,cultural

heritagepreservation,

artisandevelopment,

traditionsapprenticeship

Digitalgovernment,

innovationecosystem,entrepreneurship,smarturbanmanagement

Talentattraction,housingsupport,labormarket

modernization,consumerfinancialprotection

Greentransition,

publictransport

expansion,reforestation,aquifermanagement

Preventivehealth,childprotection,workplacementalhealth

Tourismdevelopment,Saudization,

culturalinnovation,heritagementorship

Digitaltransformation,

quantumcomputing,

AI-enabledpublicservices

Women'sempowerment,

housing,premiumresidency,domesticmanufacturing,

investmentliteracy

Transportmodernization,

renewableenergyexpansion,environmentalprotection,

watergovernance

Primaryhealthcare,childandfamilydevelopment,healthinvestment,

value-basedhealthcare

Tourismpromotion,

museumsandheritage

preservation,Qatarization,culturalheritagedigitization

Digitalinclusion,

technologytalentattraction,datamodernization

Educationaccess,disabilityinclusion,pensionreform,assetmanagementand

fundgovernance

Source:DarioKrpanetal.,“SyntheticPolicymaking”

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas9

SIGNIFICANTFINDINGSFORPOLICYMAKERS

Theresponsesofthesyntheticpersonaswereanalyzedandcomparedwiththoseof

humansusingasetofcomplementaryindicators.Theseindicatorsincludeddirectionalagreement(i.e.,whetherhumansandsyntheticpersonasevaluatedeachpolicyinthe

sameoveralldirection,suchassupport,neutrality,oropposition);aggregatecorrelationsacrosspolicies(i.e.,howstronglysyntheticandhumanresponsesalignedintherelativeorderingofpolicieswhencomparingaverageratingsacrossitems);anddistributional

similarity(i.e.,howcloselytheoveralldistributionandvariabilityofsyntheticresponsesmatchedthedistributionofhumanresponses).Theanalysesrevealedthatsynthetic

personascanprovidescalableinsightsonhumanpolicyviewsintheMiddleEast.Moredetailonthequestionsanalyzedisbelow.

•Dothepolicyevaluationsofhumansandsyntheticpersonasexhibitdirectional

agreement?Foreachpolicy,wecalculatedtheaverageresponseandits95percent

confidenceintervalforhumanparticipantsandsyntheticpersonas.Wethenusedthese

confidenceintervalstoclassifytheresponsesinoneofthreeregions:“low”(reflecting

disagreementoropposition);“middle”(reflectingneutrality);or“high”(reflectingagreementorsupport).Forinstance,ifanitemfellintothesameregionorcombinationofregions

forboththehumansampleandagivensyntheticsample,weclassifiedtheresponsesasdirectionallyalignedforthatitem.Wefoundthattheresponsesofhumanandsynthetic

personasweredirectionallyalignedacrossLLMsandpolicytypesmostofthetime.

Moreover,formostsyntheticpersonaconfigurations,thisdirectionalagreementexceeded90percent.

•Dotheaveragepolicyresponsesofsyntheticpersonasexhibitastrongaggregate

correlationwiththeresponsesofhumans?Tocomputeaggregatecorrelations(the

relationshipbetweentheaveragehumanandsyntheticpersonaresponsesacrosspolicies),wefirstcalculatedthemeanhumanresponseandthecorrespondingmeanresponsefromthesyntheticpersonasforeachpolicyitem.WethencomputedPearsoncorrelationsacrossthesepolicyaveragestoassesshowcloselythepatternofsyntheticresponsesmatchedthepatternofhumanresponses.Theaggregatecorrelationswerestrongtomedium.Thebest-performingmodelwasGPT-4o,whichproducedstrongcorrelations(>0.5)acrossallpoliciesineachofthethreecountries.

•Dothepolicyresponsesforsyntheticpersonasexhibitarangethatissimilartothe

policyresponsesofhumans?Tomeasuretherangeofresponsesbetweenhumans

andsyntheticpersonasforeachpolicy,wecomputedtwocomplementarymeasuresof

distributionalsimilarity:Jensen-ShannonDivergence(JSD)andEarthMover’sDistance

(EMD).

7

Wefoundlargediscrepanciesbetweenhumanandsyntheticpersonavariances,

indicatingthatsyntheticpersonasconsistentlyfailedtoreproducethediversityandshapeofhumanresponsedistributionsacrossthethreecountries.

8

Moreover,thesediscrepancieswereobservedforsyntheticpersonasonallthreelevels(generic,basic,andextended

personas),aswellasacrossthedifferentLLMs.

10Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

Keyfindings:

GPT-4oshowedtheclosestmatchto

humanresponses.

AIpredicted

publicpolicysentimentwithover90%

directionalaccuracy.

Simplepersonaswerenearlyaseffectiveasdetailedprofiles.

Thefindings

heldacross60

policiesandthreeGCCcountries.

AIcapturedoverallsentimentmore

effectivelythanthediversityofpublicopinion.

Syntheticpersonascanaccelerate

evidence-basedpolicymaking.

Syntheticpersonasarewellsuitedforearly-

stagepolicytestingandscreening.

Humanengagementremainsessential

whereunderstandingdifferencesacross

populationgroupsisrequired.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas11

THREERECOMMENDATIONSFORPOLICYMAKERS

OurstudyyieldedthefollowingrecommendationsforpolicymakersandpractitionersintheMiddleEast:

Usesyntheticpersonasinthepreliminarytestingforpublicreceptivenessofprospectivepolicy

Ourstudydemonstratesthatsyntheticpersonascanreliablyindicatehowpeopleperceive

policies.Acrosspolicies,responsesfromsyntheticpersonasstronglycovariedwiththoseof

humanparticipantsandmovedinthesamedirection.ThissuggeststhatGPT-basedsynthetic

personascanbeusefulforpreliminarytestingofpolicyideasandforprovidingindicative

insightsinsituationswherecollectinghumandataisneitherfeasiblenorpractical.However,

becauseaccuracymaynotbethesameacrossallLLMmodelsandbecausesyntheticpersonasproducelessvariationinresponsesthanrealpeople,theiruseshouldnotreplacedirect

engagementwithcitizensandshouldbecomplementedwithhumantestingifpossible.

Whengeneratingsyntheticpersonas,donotbeoverlyconcernedwithdatarichnessordetailedprompts

Ourstudyshowedonlymodestdifferencesbetweensyntheticpersonasthatweregenerated

usinggenericpromptsanddataandsyntheticpersonasgeneratedusingmoredetaileddataandsophisticatedprompts.Forpolicysupport,GPT-4oinparticularperformedexceptionallywell

withgenericprompts,achievingperfectdirectionalalignment.Thisindicatesthatpolicymakerscanobtainvaluableearly-stageinsightsevenwhendetaileddemographicdataonthedesiredgroupsofpeopleisunavailableorwhentheresourcesneededtocollectsuchdataarelimited

Donotexpectsyntheticpersonastocapturethefulldistributionofhumanperspectives

Ourstudyindicatesthatdistributionalsimilarityistheweakestareaacrossallmodelsand

promptingstrategies.Thus,whenusingsyntheticpersonasgeneratedthroughprompting-basedapproacheslikeours,.policymakersshouldrememberthattheresponsesoftheseentitiesare

consistentlynarrowerandlessvariablethanhumanresponses.Inpractice,thismeansthat

syntheticpersonascanbeusefulforidentifyingbroadpatternsinwhetherpeoplearelikely

tosupportoropposeapolicy,buttheyarelessreliableforunderstandingthefulldiversityofpublicopinion.Distributionalaccuracybecomesparticularlyimportantwhenpolicymakers

needtoidentifyhowviewsdifferacrossgroupsorestimatehowmanypeoplestronglysupportoropposeapolicy.Insuchcases,humandatacollectionremainsessential,andsynthetic

personasshouldnotbereliedupontoestimatethefullvariabilityordistributionofpublicopinion.

12Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

WhatAIcandoandcannotdo

WhatAIcando

Predictoverallpolicysupportandopposition.

•Rapidlytestpolicyconceptsbeforeimplementation.

•Generateearly-stagedirectionalinsights.

•Supportfasterandmoreiterativepolicydesigncycles.

•Provideindicativesentimentanalysiswhensurveydataisunavailable.

•Helpprioritizewhichpolicieswarrantdeeperpublicconsultation.

•

WhatAIcannotdo

•Capturethefulldiversityofpublicopinion.

•Replacecitizenengagementandconsultation.

•Reliablyidentifyminorityornicheperspectives.

•Fullyreplicatethevariabilityofhumanresponses.

•Estimatehowstronglydifferentgroupssupportoropposeapolicy.

•Serveasasubstituteforrepresentativesurveyresearchinhigh-stakesdecisions.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas13

14Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

CONCLUSION

AstheGulfbecomesevermorecentraltotheglobaleconomyandaspolicymakinginthe

regionbecomesincreasinglycomplexandsensitive,policymakersshouldfindtheresultsof

ourstudyencouraging,forseveralreasons.First,thestudyshowsthatsyntheticpersonas

generatedbyLLMscanbeavaluabletoolforunderstandingpublicsentimentintheearly

stagesofpolicymakingforawiderangeofpoliciesintheregion.Second,itoffersconcrete

guidanceforusingsyntheticpersonasinreal-worldapplications.Third,itdemonstratesthat

complexpromptingandrichdatasetsarenotrequiredtogeneratesyntheticpersonascapableofsimulatinghumanpolicyviews.Indeed,syntheticpersonascanbegeneratedquicklyand

inexpensivelyusingoff-the-shelfLLMs.Surveysandexperimentscontinuetobeimportanttoolsforunderstandinghowpeopleperceiveandrespondtopolicies,butwhentimeandfundingaretight,MiddleEastpolicymakerscanrelyonsyntheticpersonas.

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas15

ACKNOWLEDGMENTS

Tolearnmoreaboutthestudydescribedinthisviewpoint,see“SyntheticPolicymaking:

UsingLLM-GeneratedPersonastoPredictPublicViewsonExistingandProspectivePoliciesinnon-WEIRDCountries.”.

9

ThestudywasdevelopedbytheStrategy&IdeationCenter’s

BehavioralScienceUnitinpartnershipwithexpertsattheLondonSchoolofEconomicsand

PoliticalScienceandbehavioralsciencepractitionersfromSaudiArabia.ItwasfullyfundedbyStrategy&’sIdeationCenter.TolearnmoreabouttheeffortsoftheIdeationCenterinpromotingevidence-basedpublicpolicyintheMENAregion,visit

.

16Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas

APPENDIX

ExistingPolicies

PolicyDomainUnitedArabEmiratesSaudiArabiaQatar

Environmental

Sustainability&

ClimateAction

Publictransportintegration,low-carbontransport,marineplasticrecycling

Greentransition,publictransportexpansion,

reforestation,aquifermanagement

Transportmodernization,renewableenergy

expansion,environmentalprotection,water

governance

Health&Wellbeing

Healthinsurance,childsafety,elderlysupport,mentalwellness

Preventivehealth,childprotection,workplacementalhealth

Primaryhealthcare,childandfamilydevelopment,healthinvestment,value-basedhealthcare

NationalIdentity&CulturalHeritage

Emiratization,cultural

heritagepreservation,artisandevelopment,traditions

apprenticeship

Tourismdevelopment,Saudization,culturalinnovation,heritage

mentorship

Tourismpromotion,

museumsandheritage

preservation,Qatarization,culturalheritagedigitization

Innovation,

Technology&AI

Digitalgovernment,innovationecosystem,entrepreneurship,smarturbanmanagement

Digitaltransformation,quantumcomputing,AI-enabledpublicservices

Digitalinclusion,technologytalentattraction,data

modernization

FinancialLiteracy&Economic

Empowerment

Talentattraction,housingsupport,labormarket

modernization,consumerfinancialprotection

Women’sempowerment,housing,premium

residency,domesticmanufacturing,

investmentliteracy

Educationaccess,disabilityinclusion,pensionreform,assetmanagementand

fundgovernance

Strategy&|CanAIreadtheroom?Predictingpolicysentimentwithsyntheticpersonas17

UnitedArabEmirates

SaudiArabia

Qatar

GoldenVisaProgra