人工智能风险的生态体系 The ecology of AI risk

npjComplexity

/10.1038/s44260-026-00090-2

ArticleinPress

TheecologyofAIrisk

Received:4August2025

Accepted:9June2026

publishedonline:25June2026

Citethisarticleas:Geist,E.,Meyer,

A.D.,Moon,A.etal.TheecologyofAIrisk.npjComplex(2026).

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/10.1038/s44260-026-00090-2

EdwardGeist,AlexanderDolnickMeyer,AlvinMoon,AishaNájera,JamesHollandJones&AntonWu

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THEECOLOGYOFAIRISK

EDWARDGEIST1,ALEXANDERDOLNICKMEYER2,ALVINMOON1,*,AISHANJERA1,JAMES

HOLLANDJONES3,ANDANTONWU1

1RANDCorporation,SantaMonica,CA,USA.

2UniversityofNotreDame,DepartmentofBiologicalSciences,NotreDame,IN,USA.

3StanfordDoerrSchoolofSustainability,StanfordUniversity,Stanford,CA,USA.

*Correspondingauthor.Email:

alvinm@

ABsTRAcT.Understandingtheriskfromapplicationsofartificialintelligence(AI)isacriticalpartofcreatingAIgovernancestrategies.BuildingontheideaofstudyingAIusingecologicalandevolutionaryperspectives,weproposeanovelapproachforassessingriskfromAIusingindica-torsderivedfromtheoreticalecologymodels.Weillustrateourmethodsbyderiving3indicatorsfrompopulationandecosystemmodelsoriginatingfromtheoreticalecology.WeconcludewithadiscussionoflimitationsofouranalysisandconsiderationsforimprovingAIgovernancepolicy.

INTRoDucTⅠoN

Sincetheintroductionoflargelanguagemodels(LLMs)andotherformsofgenerativeartificialintelligence(AI)inthe2010s,muchattentionhasbeengiventothetaskofforecastingthefuturecosts,capabilities,andeconomiceffectsofAI.ConcreteandrapidimprovementsinAIinrecentyearshavefueledconcernsthatthistechnologycouldbecomeanexistentialrisktohumanity[5,1,9,43].AndconcernsaboutpotentialharmsfromAIhaveledtocallstobetterunderstandthecapabilitiesofAIandtoregulateAIdevelopment[

3

].

AIgovernance,ortheoversightofAIdevelopmentandapplicationsthroughregulatorypolicyandlegislation,hasatalltaskaheadofit.AIdevelopmentisarapidlychangingfieldandAIapplicationsarenotlimitedtoanysingledomain,meaningaccompanyingregulationsmustconsiderawiderangeofriskstoindividuals,societies,andthehumanpopulationasawhole.AIgovernancemustalsoconsideranequallywiderangeoffacetstoregulate,fromthesoftwareandhardwarecomponentsthatmakeupAIsystemstothecompaniesandpeoplewhodevelopthem.

AcriticalpartofcraftingAIgovernancestrategiesisassessingtheriskofharmposedbyAI.AnaccurateassessmentofriskgivesgovernancestrategiesbetterchanceatmitigatingorpreventingharmfromAIwithoutundulyburdeningAIdevelopmentorapplication.Toostrictofaregulatoryenvironmentwillstifleprogressindevelopingthisemergingtechnology,whileunfocusedandun-targetedregulationsmaybeineffectiveatpreventingharm.

ContentionsabouthowtousefullyandaccuratelymeasuretheriskfromAIhavesparkedvigorousdebatesinthepolicysphere,inpartduetoalackofagreedframeworksforevaluatingplausibilityofrisk.LeadingAIexpertsbothsupport[

50

,

3

]anddeny[

59

]thatadvancedartificialintelligencecouldthreatenhumanity,butargumentsonbothsidesofthisdebatetendnottobegroundedinphysicalandbiologicalscience.Instead,presentanalysestendtobebasedonintuitionorextrapolations

Date:Thursday16thApril,2026.

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fromempiricalobservationsabouttherelationshipofpresentAItechnologytoenablerssuchascomputationalcapacity.Weassertthatthelatterstrategyisinsufficientforaccuratelymeasuringrisk.

BecausewearesoearlyintheevolutionofAI,weneedtoolsthatallowustooutlinethebroadrangeofpossiblefutures.Theoryisessentialforthistask,sincescientifictheoryisthetoolthatallowsustobridgethepartiallyknownpresentandthecompletelyunknownfuture[

49

].Acentralelementoftheoryisthedevelopmentofformaltheoreticalmodels,whichare“alogicalenginetoturnassumptionsintoconclusions”[

53

].Suchmodelsallowusto“explorepossibleworlds”[

37

],whichmaybeparticularlyhelpfulwhenfacedwithblue-skyproblemssuchasthefutureofAIandhowwemightbestregulate.Thechoiceofmodelforprobingpotentialoutcomesiscritical.HollingandMaynotedthatmodelsmeanttomakeshort-termpredictionsofspecificpopulationshaveadifferentstructurethanmodelsmeanttoillustrategeneralprinciples[

25

,

36

],whileNisbetandGurneymadetheimportantdistinctionbetweenstrategicandtacticalmodelsinscience[

40

].Tacticalmodelsare“designedtoyieldaccurateshort-termforecastsofpopulationchanges,”whileastrategicmodelsaresimpleandmathematicallytractable,“constructedwiththeaimofidentifyingpossibleecologicalprinciples”[

40

].

Inthispaper,wewillemploymodelsfromtheoreticalecology,suchastheframeworkoftheLotka-Volterracompetitionmodel,toinvestigategeneralprinciplesofAIrisk.Inthisway,wetakethestrategic-modeltack,posingandexploringscenariosconcerningco-existence,competitiveexclusion,andgeneralfeaturesthatindicaterisktohumansocietyfromAI.Insuchscenarios,AIsystemsactascompetitorsforecologicalresources(suchasenergyandrawmaterials)withhumansandotherbiologicalorganisms.IftheseAIsystemsaremoresuccessfulthanhumansatsecuringtheseresources,humanwell-beingorevensurvivalcouldbeimperiled.

ArgumentsthatAIposesaseriousthreatofcausinghumanextinctioncommonlyassumeavari-antofthisecologicalmechanism,eventhoughitistypicallystatedinnon-ecologicalterms(

e.g.as

anunintendedsideeffectofextremegoal-seekingbehavior

).Forexample,YudkowskyandSoares[

63

]arguethatAIwilllikelycausehumanextinctionnotbecauseitwouldseektokillhumansonpurpose,butbecauseitwillwish“tousetheiratomsforsomethingelse.”Themechanismenvisionedcausinghumanextinctioninthisscenariowould,inecologicalterms,constituteacom-binationofpredationandhabitatloss.Meanwhile,othersarguethatthisandotherpathwaystocatastrophicharmfromAItechnologiesareunsubstantiated[

2

]andlogicallyunsound[

39

].Forexample,Bareis,Ackerl,andHeilfindin[

2

]thatcommonassumptionsaboutthesentience,con-sciousness,andgeneralintelligenceoffutureAIsystems,whichunderpinmanyAIextinctionorcatastrophescenarios,aremorespeculativethanbasedonafoundedtheory.Ultimately,thetensionbetweentheseschoolsofthoughtidentifiesaneedtorigorouslyevaluatepotentialrisksandharmsfromfutureAIsystems.WefocusontheproblemofrigorouslyanalyzingclaimsaboutecologicalrisksfromAIsystems.Forexample,severalrecentanalysesproposeframeworksforAIgovernancethatinvokeconceptsfromevolutionarybiology,suchastheconceptof“fitness”[

21

,

19

,

6

].Webelievethatsuchframingshaveconsiderablepotential,butthesepapersdonotemploytheformalmodelsofcontemporarytheoreticalecologyandpopulationbiology.ThispaperaimstofillthecurrentgapinthediscussionofAIrisksandformalizetheinsightthatthesefieldsofferpowerfulconceptualandanalytictoolsforscientistsandpolicymakerstoassesstherisksthatfutureAItechnologiesmayormaynotpose.

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Results

Inthissection,wesummarizeourassumptionsforourworkbeforepresentingourmainresults.

Assumptions.WedonotassumethatAInecessarilyposesextremeecologicalriskstohumanity.Theformalmodelsinthispaperaimtobefalsifiable,allowingthembothtochallengeaswellastobolsterclaimsthatAIwillthreatenhumans.ThemodelspresentedinthispaperarealsointendedtobecapableofrepresentingscenariosinwhichAIdoesnotposeseriousecologicalthreatstohumaninterestsaswellasthoseinwhichitdoes.ItisourhopethatfutureresearchofthesortpresentedinthispaperwillultimatelyshoweitherthatAIisunlikelytoposeasignificantecologicalthreattohumanity,orthatsuchriskscanbepredicted,controlled,andameliorated.

WerecognizethatnotallriskfromAIcanbecharacterizedasecologicalrisk.WealsodonotcharacterizeallwaysthatecologicalriskscanemergefromAI;notably,wedonotdiscusspotentialharmsfromtransientdynamics.AnindividualAIsystemcouldalsocausecatastrophicharmthroughasingleactionorbyamplifyingotherrisksinuniqueways[

22

,

60

].WefurtherdiscusstherelationshipbetweenecologicalAIriskandothertypesofAIriskintheDiscussionsection.

Ouranalysisdrawsonthelongstandingfieldofartificiallifeandargumentsthattheremayexistdigitalorganismsthatsharemanyaspectsofbeingalivewithconventional,biologicalorganisms[

47

].Forthepurposesofouranalysis,wedefineAIasakindofdigitalorganismcapableofsurviving,reproducingindependentlyofhumans,andcompetingforresources,withoutnecessarilybeingaliveinabiologicalsense.InthecasesconsideredbythemodelsinthispaperweassumethattheabilityofAItostablyexist,evenwithouthumanintervention,isanecessaryconditionforthesedigitalorganismstocompetewithhumansandotherbiologicalorganismsforecologicalresources.

WealsodonotclaimthatthetypesofLLMsthathavebecomeubiquitoussincetheintroductionofChatGPTin2022constitutedigitalorganismsaccordingtothisdefinition.Instead,weassertthatcurrenttrendscombinewithexistingobservationstomaketheemergenceofsuchdigitalorganismsapossible,ifnotnecessarilylikely,outcome:

(1)(Autonomy):AIdevelopersandresearchersareincreasinglyattemptingtointegrateexist-ingtechniques,suchasLLMs,intoagentsthatperformcomplextasksautonomouslyoverincreasinglylongtimescales.

(2)(Cyber/physicalrepresentation):Inordertoperformthesecomplextasks,theseAIagentsneedtodrawonandmanipulateresources,includingphysicalresources.Theymustac-complishthesemanipulationsbysomemeans,possiblybutnotnecessarilyinvolvingsomeformofroboticembodiment.TrendsinAItechnologydevelopmentpointtowardsworkinresolvingthecyber/physicalinterfaceproblem,suchasintheusecasesofself-drivingcars[

18

,

15

]orautonomousweaponsplatforms[

34

].

(3)(Evolutionarypressure):Computer-basedsimulationsdemonstratethat“digitalpopula-tions”canbedesignedtoevolveovertimeasiftheyweresubjecttonaturalselectionpressures([

31

,

47

],cf.[

29

]forareview.)Thefitnessoftheindividuals(digitalorganisms)inthesepopulationschangesastheyundergoevolutionandtheirenvironmentchanges.

Inisolation,noneofthethreeconsiderationsabovewouldnecessarilyleadtotheemergenceofself-sustainingAIdigitalorganisms.However,inscenarioswhereAIsystemsdemonstratealloftheseproperties(actingautonomously,manipulatingandusingresources,andexperiencingcompetitivepressures),weassertthatAIsystemswiththeabilitytoself-reproducewillenjoymarkedadvantagesoverthosethatcannot,resultinginthepotentialemergenceofAIdigitalorganisms–aconclusion

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whichreflectsresultsbyKozain[

29

]andhasoriginsdatingbacktovonNeumannandhisthoughtexperimentaboutself-replicatingmachines[

61

].

MainResults.UsingadefinitionofAIasnon-biologicalsystemswithintenttosurvive,thispaper’sprimarycontributionistoexploreindicatorsandqualitativeregimeswhichcharacterizeAIriskasaformofecologicalriskusingtoolsfromtheoreticalecology.Weanalyzethreedifferentmodelsfromtheoreticalecology(aLotka-Volterracompetitionmodel,amultipatchpopulationmodelwithmigration,andamodelofastableecosystembasedonMargalef’sprinciple),eachwiththeirownassumptionsandcontexts,toderivethreeexampleindicatorsthatAImaydeveloprobustpopulationswhosenumberscannoteasilybelimitedbyhumanactivity.Atahighlevel,theindicatorsthatwederivearethefollowing.

•CompetitionbetweenAI,whichcanbemeasured,forexample,astherateatwhichAImutatetobecomeantagonisticAIthatmayactcontrarytohumaninterests.

•Complexinterconnectivity,measuringofhowdifferenttypesofAI,e.g.indifferentsectorsoftheeconomy,caninteractwithandsupporteachother.

•Changestointernalstructures,measuredbyawayofcharacterizingtheefficiencyorotherpropertiesofsystemsofAIbasedonmeasurableoutputs,suchasheatandotherby-productsofmaintainingAIs.

OurresultshereidentifynewindicatorsofAIriskwhichmaybeadaptedinfutureresearchtofurtherrefineAIriskforecasting,augmentexistingAIriskmodels,andleadtothediscoveryofotherusefulAIriskframeworks.Whenappropriate,ourargumentsaremathematicallyrigorous,andsoourmethodsandresultsmaybeofinteresttothebroadertheoreticalecologycommunity.

Discussion

Inthissection,wediscusshowourresultsrelatetoexistingframeworksforAIrisk.ManyframeworksforassessingriskfromAI,rangingfrompolicywhitepapers[

10

,

8

]tofederalandstatelevelproposals[

26

,

51

],proposeacompute-centricapproachtoindicatingAIrisk,withafocusontheresourcecostsofproducingAImodels.ThereareseveraladvantagestosummarizingAIriskthroughcompute,i.e.abstractrepresentationsofcomputationalresources.Forexample,computegovernancedoesnotrequireexaminingspecificapplicationsorarchitecturesofAIsystems—onlythecostsrequiredtocreatethem,assumingapowerlawrelationshipbetweenthecomputational

costoftraininganAImodelanditscapabilitieswhichcanbeheuristicallysummarizedbythefollowingequation,

(4)AIcapability=Computed.

Motivatedbyresultsfromtechnicalpre-printsonmodelperformance[

28

,

24

],scalinglawas-sumptionsarebuiltonasoundempiricalfoundation.Asecondadvantageisthatcomputeiseasilyquantifiableintermsofthetechnicalcharacteristicsofdatacentersandtheircomputingunits,suchasGPUsandspecializedAIcomputerchips[

33

].Byassociatingcapabilitiesandriskswithquantitativemetricssuchasrequisitelevelsoffloatingpointoperations,computegovernanceallowsforriskforecastingusingnumericalmethods.

ToillustrateonewayhowcomputehasbeenusedinthepolicyliteraturetoforecastAIrisk,webrieflysummarizethecomputeandscalinglawassumptionsinmanypopulardiscussionsofAIcapabilitiesandrisk.Thecoreideaisapplyingaformulaforeconomicortechnologyforecasting

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topredicttheyearthatAIwillemerge.Thiscouldbedone,forexample,byapplyingJones’seconomicformulasforendogenousgrowthoftechnologyresearchanddevelopment[

27

]tomodelAIcapabilityovertimeasanabstractvariableS(t)whosebehaviorisdeterminedbyadifferentialequation,

whererSisaconstantrepresentingefficiencyofreturnstosoftware,QS(t)≥0isaccumulatedresearchprogressfromaninitialtimet0uptotimet,andPS(t)≥0representsa“diminishingreturns”factor.Solvingthedifferentialequationin(5)revealsascalinglawmechanismwhichdrivesimprovementstoAIcapabilityaccordingtothisassumption.

(6)S(t)=Constant×QS(t)rSPS(t)×e—dsrSP˙S(s)log(QS).

Seethesupplementalinformationforthederivationof(6)from(5).Inthismodel,thedecayofPS(t)determinesatimescaleduringwhichapowerlawforsoftwareimprovementiseffectivelyvalid.Forexample,ifreturnsdonotdiminish,thenPS(t)isconstantovertime,andthedescriptionofAIcapabilityinEquation(6)reducestotheheuristicinEquation(4).Withinthemodel,differentparameterregimesandquantificationsofcapabilitybycomputemetricsleadtovaryingpredictionsofwhenhighlyadvancedAIwillemerge.

Despitetheirfoundationinempiricaltraining-basedresults,theirtheoreticalsuccinctness,andtheirappealingexplanativepower,scalinglaws,astheyareappliedinthepopulardiscussionofAIcapabilitiesandrisks,havenotbeenrigorouslyvalidatedandaremostlyheuristic.Becauseofthis,thetimelinesandconclusionsofscalinglawmodelsarefiercelydebated(cf.[

46

]).Intheend,webelievecomputeisanimportantindicatorofriskfromAI,butultimatelyitisonlyonedimensionofriskassessment.Andeveniftheywerevalidated,weassertthatwhethercomputemetricsandscalinglawslikeEquation(6)sufficientlyrepresentcapabilitiesofAIasatechnology,accuratelyenoughtopredictAIdevelopmentandmitigateunacceptablerisksfromAI,isanopenquestion.Dependingonthetechnology,one-dimensionalmodelshavevaryingabilitytocaptureandforecastprogressovertime.Moore’slaw,thefamousexponentialrelationshipbetweentimeandtransistordensity,heldaccuratelyfordecadesbeforedeviating[

52

].Inadditiontohighlightingatheoreticalconsiderationforscalinglawpredictions,namelygivinganexampleofhowlongascalinglawcouldpersist,thefailureofMoore’slawalsohasdirectimplicationsforanyAIforecastswhichdependonMoore’slaw-typeargumentstojustifyarateofhardwareimprovementovertime:Intheabsenceofadrivingexponentialimprovementtohardware,howeveritisquantified,otherfactorsbesidesscalinglawsshouldexplainpredictionsofAIdevelopment.Asanotherexample,in[

64

],Zhangetal.examinehistoricaldataonairplaneperformancefrom1960to1998toshowthatbothlogisticandexponentialgrowthmodelsfailtoaccuratelypredictkeymilestonesinpassengerplanedevelopment.Morelikelythannot,simplepowerorexponentiallawsareinsufficienttocaptureandforecastcapabilityoverlongperiodsoftime,attimescaleswhichrealisticallydescribeemergingtechnologytimelines.

Basedontheabovediscussion,theremaybemanyotherindicatorsofAIriskwhichwecandiscoverthroughtheapplicationofothertypesofmodelsandtheories.Andcombiningourthreeecologicalindicatorsorothercandidateindicatorswithcomputemayleadtodifferentconclusionsthanthosederivedfromcompute-basedanalysisalone,whichwouldleadtoaricherdecisionspaceforAIgovernanceandmoreconfidenceinpredictions.

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Lastly,ourresultshavelimitationswhichpointtowardspotentiallyfruitfuldirectionsforfutureresearch.Oneimportantlimitationisthatourmodelsaresimple,andourassumptionsarechosentoillustrateaproofofconcept.Inpractice,mathematicalmodelsaretoolsusedtoexaminecomplexstructures,makepredictions,andsimplifyreality.AssuchourresultsrelyonseveralassumptionsaboutAIbehaviorandinteractionwhichmaynotfullycapturethecomplexityandvariabilityofAIsystemsinreal-worldfuturescenarios.Forexample,ifwestartedwiththepremisethatanindividualAIcouldcausecatastrophicharmthroughasingleaction,thenourindicatorswouldfailtoaccuratelyassesstheriskofharm.

Thislimitationextendstoourchoiceofmodelsaswell.Onewayourresultscouldbesharpenedisbyrefiningthemodelswechose.ChangestoourperturbedLotka-Volterramodelmaybeabletotransposeandincorporatemorenuancedphenomena,suchasS-shapedgrowthofadoptionintheliteratureondiffusionofinnovationsandculturalevolution[

48

,

23

,

4

],orcyclingineconomics,politics,andconsumerbehavior.ThecompetitionbetweenmultipleAI“species”orAIwithhumanscouldthusconceivablyyieldeitherlimitcyclesorchaoticoutcomes.ThemodelfromElas-Wolffetal.thatweusetoderiveoursecondindicatorassumesRickerdensity-dependence,whichisknownforitsovercompensatorydynamics,meaningthatwhendensitydependenceisstrong,ittendstoovershootequilibria,leadingtocyclingand,forhighpopulationgrowthrates,chaos[

58

].Inadditiontorefiningmodels,anotherwayourresultscouldbesharpenedisbyexploringdifferenttypesofmodels,potentiallyfromotherfields.Generally,itisimperativethatfutureresearchintoAIcapabilityconsidersarangeofscenariosfromrelevantfields,includingconsiderationforappropriateandaccuratemeasuresofrisk.

Asecondlimitationisthatourindicators–competitiveness,complexinterconnectivity,andchangestointernalstructures–aretheoreticalconstructsthatmaybechallengingtomeasureaccuratelyinpractice.Wehavemadenoattempttooperationalizeourindicatorsand,unlikecompute,therearenotnecessarilynaturalorcanonicalwaystoquantifythem.Thisisespeciallyrelevantinourfirsttwoindicators,whereabstractratesofmutationorinterconnectednesshaveclearmeaningwithinmodelsbutrequirefurtherworktorelatethemtoreal-lifesystemsofAI.Forexample,ourperturbedLotka-Volterramodeldescribes“mutation”frombenigntoantagonistic.AsthemutationrateisactuallyjustatransitionratebetweenthetwopossibleAIstates,themodelisactuallyadeterministic,large-populationapproximation.Givenitsimportancefortheoutcomesofthesemodels,itisessentialthatwedevelopmethodstoaccuratelyestimateandsubsequentlymonitorthistransitionrate.Futureresearchshouldstrivetoderiveindicatorsthathavemeasurablequantitiesandrealisticdynamics.

Weconcludewiththefactthatwehavenotpresentedapolicyframeworkwhich,givenouriden-tifiedindicators,couldeffectivelycontrolAIpopulations.Suchaframeworkisacriticalcomponentofusingriskindicatorstopreventharm.Determiningthesepoliciesisoutsidethescopeofourpresentwork,butweproposethatdevelopingpoliciestoincludeadiversityofriskindicatorsthatarederivedfrommathematicalandscientificfieldsofstudywillleadtogreaterconfidenceintheirabilitytopreventharm,comparedpolicieswhicharedevelopedwithoutthem.Inthiscontext,ourpresentedresultsareafirststeptowardsfindingeffectivegovernancestrategiesforpreventingharmfromAI.

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METHoDs

Tomatchthescopeofscenariosweoutlinedabove,weconsidereddifferentindicatorsthatAIwillbeabletopersistintheworld,stablyexistwithouthumanintervention,andformecosystemswhichchangetobecomemoreefficientandresistanttodecay.Givenourassumptionsintheintroduction,theseindicatorsmayrepresentsignsthatAIposesathreatforpotentiallysevereconsequences,suchasmassiveeconomicupheavalorwidespreadlossofhumanlife.Toderivetheseindicators,aswementionedpreviously,weframedtheactionsofAIaspartofacompetitionforresourcesanddrawindicatorsofriskfromtheoreticalecologymodels.AswedescribedintheIntroduction,weassumedAIsarenon-biologicalsystemsthatseektoperpetuatetheirownexistenceandwhicharecapableofmanifestingsophisticatedadaptivebehaviorinpursuitofthisgoal.Thesesystemsareassumedtobeintelligentinthesensethattheycancompeteeffectivelyagainsthumansandwieldindependentagency.Fromthestandpointoftheproposedframework,whatmattersisthattheseentitieswouldcompetewithhumansforresourcessuchashabitablespaceandfreeenergy,notwhethertheypossessintelligenceofthesamekindhumanspossess.Thesecompetitionsarenotnecessarilyzero-sum,butunderextremecircumstancestheycouldseverelydisadvantagehumans. Iftheassumptionfromtheintroductionhold,theparametersoftheseecologicalmodelscanthenbeconvertedintoindicators,ormeasurablepropertiesthatmaytellwhennecessaryconditionsforsubstantialriskofharmfromAIaresatisfied.Ifvalidated,indicatorscanbemonitoredandacteduponbyAIgovernancestrategies.Broadlyspeaking,byrelatingindicatorstowell-establishedscientificprinciples,itmaybecomepossiblenotjusttoidentifyindicatorsofriskfromAIforpoli-cymakingpurposes,buttoassessthequestionofwhetherthistechnologyposesanexistentialrisk.

Thisapproachaimstobridgetheoreticalmodelswithgovernancestrategies,ultimatelycontribut-ingtoamoreinformedandeffectivemanagementofAIdevelopmentanditspotentialrisks.Weleavethetasksofvalidatingindicatorsandconstructingandcomparingpotentialindicator-basedAIgovernancestrategiesasfuturework.

FirstIndicator:MutationRatesandCompetitivenessofAI.Asanillustrativeexample,ourfirstindicatorcomesfromtheLotka-Volterramodelforthepopulationdynamicsoftwoin-teractingspecies.ThisseminalmodelinecologywasproposedbyAlfredLotka(1926,1920)andVitoVolterra(1926)todescribepredator-preydynamics,andmodifiedbyGause(1934)toexplainexperimentalobservationsofcompetingspeciesofParamecium.ThecompetitiveLotka-Volterraequationswereintrod