cities, ai, design, & the futurespatialcomplexity.blogweb.casa.ucl.ac.uk/files/... ·...
TRANSCRIPT
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Centre for Advanced Spatial Analysis, University College LondonCentre for Advanced Spatial Analysis
Cities,AI,Design,&theFutureCanArtificialIntelligenceImproveDesignIntelligence?
MichaelBatty
http://www.spatialcomplexcity.info/http://www.casa.ucl.ac.uk/
[email protected]@jmichaelbatty
March27th 2018
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AnOutlineoftheTalk• ArtificialIntelligenceandDesignIntelligence
• BasicConceptsaboutComplexityTheory
• MakingSenseofUrbanDevelopment:KeyFactors
• RelatedConcepts:Geodesign,Networks,ABM
• DesignSolutionsasWeightedAveraging
• ActualDevelopmentusingNeuralNetworks
• ASimpleExample:AveragingbyOverlay
• Generalisation:ModellingattheVeryLocalScale
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ArtificialIntelligenceandDesignIntelligenceBudhu askedmetospeakonAIandCities. Iaminwayanexpertbutletmethrowoutsomeideas
Iwillnottalkabouthowwegetholdofmassivedatasetsandsearchforunderlyingpatternbutaboutdesignintelligenceandhowthisdiffersfromartificialintelligence
Theproblemwehaveincitiesiswhatweseeisnotnecessarilywhatwewant.Inshortifweexplainhowthingsemergeandevolve– actualdevelopment – thisisusuallydifferentfromoptimal,idealdevelopment
Soinaway,AIasitisdevelopingtomakesenseofwhatweseeisnotsomethingweseeverymuchofsofar– wedoseealotofmodellingwhichinawayisakindofAI
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BasicConceptsaboutComplexityTheoryThiswillbemythemethen– howwegeneratedesignintelligenceandthenhowwecanthinkofthisasartificialintelligence. Firstletmedescribesomebasicassumptions
• Citiesdevelop,grow&changefromthebottomup
• Countless ‘comparativelyuncoordinated’decisions(rationalwithintheirownframe)generate coordinationacrossmanyscales– AdamSmith’s InvisibleHand
• Thismanifestsitselfspatiallyasorderandpatternwhichissaidto‘emerge’athigherscalesfromthatwhichtheforcesthatdeterminethemoriginate.
• Formanyyearswehaveacceptedthatwemightbeabletosimulatethiskindofemergence
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• Thesimplestexamplesarefractals– thedendriticpatternofstreetsincitiesthatdetermineoptimalspatialpatternsofhowcitiesareresourced,howthehierarchyofcentralplacesisorderedandsoon
• Therehasbeenplentyofthinkingaboutcitiesintheseterms.MyownworkonFractal Cities whichdatesfromthemid1980sisonestream
• Inthissense,ourmodelsembodyadegreeofintelligence–artificialtoanextentalthoughtheassumptionisthatsuchintelligenceshouldmirrorhowthesystemactuallydevelops.
• Inshortourmodelsshouldnotbeaboutartificialprocessesbutreal.Thistalkisaboutthetensionbetween real andartificial butalsobetweenorganic anddesigned.
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• Buttherehasbeenverylittlethinking intermsofhowplansaremade.Wetendtothinkoftheseasbeingsomehowimposedonthecityastopdown,yetplansusuallyemergefromthebottomup
• Theclearesttheoriesofdesignreflectthisnotionthataplanissuccessivelydeveloped fromasimpleseedbyadesignerwhoworksawayatitrecursively.
• Inthissensethendesignisaboutakindofartificialintelligencebutmoreimportantaboutintelligencethatleadstobettersystems,solutions
• Infact,designoftenconflictswithAIinthatAIdoesnotnecessarilyproducebetterresultsinanysense– fortoreplicatewhatwedo,doesnotmeanthatwhatwedoisbest.SointhistalkIwillquestionAIinhelpingustodesign.
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MakingSenseofUrbanDevelopment:KeyFactors• Letmereturntourbandevelopment. Thecomplexity
modelofemergencesuggeststhatmanyfactorsdeterminethepatternofurbandevelopment thatoccurs,andweneedtoknowthese
• Thereisthussomesensethatwemightbeabletoproducemodelsthatcombineaseriesofindependent variables–factors– thatcanbeusedtopredictsuchpatterns.IndeedoururbanmodelstendtoattemptthissuchasCAmodels
• Recentlydevelopments inAIsuggestthatwemightbeabletofindthepatternsthatleadtoactualdevelopment butthisisnotnecessarilythebestplan
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• SoIamgoingtobeginwithshowingyouhowwecangenerateaplanwhichisbestbutwhatIwilldohereisgeneratetheplanasaprocessofgroupdecision-making–againfromthebottomupinsuchawaythattheplanemergesfromdifferentandoftenconflictingindividualplans.Inasense,mymodelwillbebasedonakindofintelligencebutnotonewhichnecessarilyleadstooneactuallyhappens
• Themodelisbasedonanoldideaofpoolingopinionsbutithassurfacedoverthe last60yearsinmanycontexts
• Iwilldevelop ithereforaverysimpleexampleandthenpointthewaytomoreinformationaboutit
• IthasaquitewelldefinedformalrepresentationbuthereIwilldeveloptheideaherevisually
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RelatedConcepts:Geodesign,Networks,ABM• Geodesign:groupdecision-making:…designingforchange
cannotbeasolitaryactivity.Rather,itinevitablyisateamendeavorwithmanyparticipants(fromthedesignprofessionsandgeographicsciences)…
CarlSteinitz(2012)AFrameworkforGeodesign,ESRIPress,p.ix
• AgentsandActors:amodelofhowagentscombinetheirconflictingviewsofadesignsolutiontoaconsensus;anagentbasedmodel(ABM)
• GraphsandNetworksbutnon-spatialnetworks– socialnetworks:asocialpowerstructure
• BuildingModelsinvolvesmanyformsofintelligence
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Centre for Advanced Spatial Analysis, University College LondonCentre for Advanced Spatial Analysis
Factor1AccesstoHousing
Factor2AccesstoRetailing
Factor3AccesstoHealthCare
Factor4AccesstoEducation
DesignSolutionsasWeightedAveraging
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Centre for Advanced Spatial Analysis, University College LondonCentre for Advanced Spatial Analysis
+ +
1
1
1
1
1
0
0
0
0
0
AddingorSynthesisingPhysicalInfluences
BooleanOperations
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+
Factor1
Factor2
Factor3
Factor4
Solution
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DesignSolution?
Factor1
Factor2
Factor3
Factor4
ObservedOutcome
ActualDevelopmentusingNeuralNetworks
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ASimpleExample:AveragingbyOverlayThelistoffactors:• accessibilitytoexistingurbanservices,• costsofspatialcongestion,• accessibilitytorecreationalamenities,• areasofacceptablemicro-climate,• areasofwatercatchmentandpoordrainage, institutional
constraintsimposedbygovernment,• accessibilitytoexternalurbanmarkets,• subsidenceandextensive industrialpollution,• areasofsuitabletopography,• ruralamenityareas,• historicurbanareas,and• conservationofhighqualityagriculturalquality.
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Generalisation:ModellingattheVeryLocalScale• Thenetworkofrelationsbetweenfactors:actors&agents
• Theproblem– theresolutionofconflictoverachangeinuseoflandinadenseurbanarea– designmaybe,decision
• Theagentsinthemodels– actors,stakeholdersversussites/buildings
• Thewaytheagentsinteractacrossthemapsofwhattheyconsidersignificanttochangeofuse
• Thewaytheagentseffectcompromise– twoproblemswhicharedualsofoneanother– rathertechnicalbutasketchofhowwemightproceed
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• Alongpreamble Iknowbutletmebeginwiththeproblemfirstandthen Iwillsketchthemodel
• Theproblemisoneofreconcilingdifferentinterestsinlanddevelopment intheheartofaworldcity:London
• ItisasclosetotheheartofthecityaspossibleforitcentresonthepostcodeEC1A1AAwhichistheoldGeneralPostOfficeandisnowadjacenttothenewLondonStockExchange(whichisalmostvirtualnow)–
• Averyhistoricareawithenormousdevelopment pressures
• It’saTOYMODELwith6agentsoractorsand8sites– letusseehowitworks
• Ofcoursetomake itrealwecanscaleitinmanyways– manyactorsmanymorebuildingsetc.andalotofdataonprocesses
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1
8
7
6
42
3
5
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Actors/Stakeholders1CityCorporation
2Residents
3HospitalNHS
4Developers
5PropertySpec
6Banks
Sites/Buildings/Locations1AldersgateComplex
2StBotolph’s
3NomuraHouse
4MiltonHouse
5Postmans’Park
6BankofAmerica
7BartsNewBuilding
8BartsOldBuilding
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1 2 3 4 65 7 8
1CityCorporation
2Residents
3HospitalNHS
4Developers5PropertySpeculators6Banks
Agents0 0 1 0 0 1 0 1
0 0 0 0 0 0 0 1
0 0 1 0 0 1 0 1
Sites/Buildings
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001100001011111111110100101001001000000010100100
=M(ap)
1CityCorporation
2Residents
3HospitalNHS4Developers
5PropertySpec
6Banks
1AldersgateComplex
2StBotolph’s
3NomuraHouse4MiltonHouse
5Postmans’Park
6BankofAmerica
7BartsNewBuilding
8BartsOldBuildingAgentsSites/Bu
ildings
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001100001011111111110100101001001000000010100100
011111001000111101111000010000011101010000010000
222101274313245313133313011111133313
=
A=MMT
ThePrimal:Interactionsbetweenactorswrt sites
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001100001011111111110100101001001000000010100100
011111001000111101111000010000011101010000010000
5142141111110100415314012133121110111111414214111011111110111111
=
S=MT M
TheDual:Interactionsbetweensiteswrt actors
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2/82/82/81/801/82/207/204/203/101/203/202/184/185/183/181/183/181/143/143/143/141/143/14
01/51/51/51/51/51/143/143/143/141/143/14
001100001011111111110100101001001000000010100100
TheNetworkAveragingXSetofMaps
yields
ANewAveragedSetofMaps
1.000.750.250.750.250.250.950.650.350.850.350.350.940.610.220.830.220.220.930.500.210.860.210.210.800.400.200.800.200.200.930.500.210.860.210.21
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AndthenweaveragethemagainusingthesamenetworkAndthisyieldsanewmap,Andsoonuntilallthedifferencesbetweentheactorswithrespecttotheirmapsareironedoutandwegetthefollowingmap
0.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.94
Wecandothisonthedualproblem,onthesitesandironoutthedifferencesbetweensiteswithrespecttotheiractors
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7%
19%
5%
21%
7%7%
18%
14%
Agents
1CityCorporation 17%
2Residents6%
3HospitalNHS17%
4Developers 23%
5PropertySpec25%
6Banks10%
Buildings
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NextSteps
Realproblems– verylargenetworks,typesofconnection
Intensityordesirabilitymaps;spatialaveragingasdevelopedquitewidelyinoverlayanalysisinGIS
Rationalaveraging,simpleaveraging,weightingaveraging,dominance,andotherstrategiesofcompromiseornot;networksthatdon’tleadtosolutions
Themodelislongstanding– notnew,whatisnewisthedualprimalandtheembeddingofmapsintoit
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Centre for Advanced Spatial Analysis, University College LondonCentre for Advanced Spatial Analysis
ReferencesoverManyYearsFrench,J.R.P.(1956)AFormalTheoryofSocial
Power,PsychologicalReview,63,181-194.
Batty,M.(1971)AnApproachtoRationalDesign:Part1:TheStructureofDesignProblems,Part2:DesignProblemsasMarkovChains,ArchitecturalDesign,41,436-439,498-501
Batty,M.(1984)PlanDesignandCommitteeDecision-Making,EnvironmentandPlanningB,11,279-295.
Blondel,V.D.,Hendrickx,J.M.,Olshevsky,A.,andTsitsiklis,J.N.(2005)ConvergenceinMultiagent Coordination,Consensus,andFlocking,InProceedingsoftheJoint44thIEEEConferenceonDecisionandControl,EuropeanControlConference,Seville,Spain,December12-15,2005,
Batty,M.(2013)TheNewScienceofCities,MITPress,Cambridge,MA,inpress http://www.mitpress.mit.edu/
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Centre for Advanced Spatial Analysis, University College LondonCentre for Advanced Spatial Analysis
Thankshttp://www.spatialcomplexity.info/
http://www.complexcity.info/http://blogs.casa.ucl.ac.uk/
http://www.casa.ucl.ac.uk/