cities, ai, design, & the futurespatialcomplexity.blogweb.casa.ucl.ac.uk/files/... ·...

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


AnOutlineoftheTalk• ArtificialIntelligenceandDesignIntelligence

• BasicConceptsaboutComplexityTheory

• MakingSenseofUrbanDevelopment:KeyFactors

• RelatedConcepts:Geodesign,Networks,ABM

• DesignSolutionsasWeightedAveraging

• ActualDevelopmentusingNeuralNetworks

• ASimpleExample:AveragingbyOverlay

• Generalisation:ModellingattheVeryLocalScale


ArtificialIntelligenceandDesignIntelligenceBudhu askedmetospeakonAIandCities. Iaminwayanexpertbutletmethrowoutsomeideas

Iwillnottalkabouthowwegetholdofmassivedatasetsandsearchforunderlyingpatternbutaboutdesignintelligenceandhowthisdiffersfromartificialintelligence

Theproblemwehaveincitiesiswhatweseeisnotnecessarilywhatwewant.Inshortifweexplainhowthingsemergeandevolve– actualdevelopment – thisisusuallydifferentfromoptimal,idealdevelopment

Soinaway,AIasitisdevelopingtomakesenseofwhatweseeisnotsomethingweseeverymuchofsofar– wedoseealotofmodellingwhichinawayisakindofAI


BasicConceptsaboutComplexityTheoryThiswillbemythemethen– howwegeneratedesignintelligenceandthenhowwecanthinkofthisasartificialintelligence. Firstletmedescribesomebasicassumptions

• Citiesdevelop,grow&changefromthebottomup

• Countless ‘comparativelyuncoordinated’decisions(rationalwithintheirownframe)generate coordinationacrossmanyscales– AdamSmith’s InvisibleHand

• Thismanifestsitselfspatiallyasorderandpatternwhichissaidto‘emerge’athigherscalesfromthatwhichtheforcesthatdeterminethemoriginate.

• Formanyyearswehaveacceptedthatwemightbeabletosimulatethiskindofemergence


• Thesimplestexamplesarefractals– thedendriticpatternofstreetsincitiesthatdetermineoptimalspatialpatternsofhowcitiesareresourced,howthehierarchyofcentralplacesisorderedandsoon

• Therehasbeenplentyofthinkingaboutcitiesintheseterms.MyownworkonFractal Cities whichdatesfromthemid1980sisonestream

• Inthissense,ourmodelsembodyadegreeofintelligence–artificialtoanextentalthoughtheassumptionisthatsuchintelligenceshouldmirrorhowthesystemactuallydevelops.

• Inshortourmodelsshouldnotbeaboutartificialprocessesbutreal.Thistalkisaboutthetensionbetween real andartificial butalsobetweenorganic anddesigned.


• Buttherehasbeenverylittlethinking intermsofhowplansaremade.Wetendtothinkoftheseasbeingsomehowimposedonthecityastopdown,yetplansusuallyemergefromthebottomup

• Theclearesttheoriesofdesignreflectthisnotionthataplanissuccessivelydeveloped fromasimpleseedbyadesignerwhoworksawayatitrecursively.

• Inthissensethendesignisaboutakindofartificialintelligencebutmoreimportantaboutintelligencethatleadstobettersystems,solutions

• Infact,designoftenconflictswithAIinthatAIdoesnotnecessarilyproducebetterresultsinanysense– fortoreplicatewhatwedo,doesnotmeanthatwhatwedoisbest.SointhistalkIwillquestionAIinhelpingustodesign.


MakingSenseofUrbanDevelopment:KeyFactors• Letmereturntourbandevelopment. Thecomplexity

modelofemergencesuggeststhatmanyfactorsdeterminethepatternofurbandevelopment thatoccurs,andweneedtoknowthese

• Thereisthussomesensethatwemightbeabletoproducemodelsthatcombineaseriesofindependent variables–factors– thatcanbeusedtopredictsuchpatterns.IndeedoururbanmodelstendtoattemptthissuchasCAmodels

• Recentlydevelopments inAIsuggestthatwemightbeabletofindthepatternsthatleadtoactualdevelopment butthisisnotnecessarilythebestplan


• SoIamgoingtobeginwithshowingyouhowwecangenerateaplanwhichisbestbutwhatIwilldohereisgeneratetheplanasaprocessofgroupdecision-making–againfromthebottomupinsuchawaythattheplanemergesfromdifferentandoftenconflictingindividualplans.Inasense,mymodelwillbebasedonakindofintelligencebutnotonewhichnecessarilyleadstooneactuallyhappens

• Themodelisbasedonanoldideaofpoolingopinionsbutithassurfacedoverthe last60yearsinmanycontexts

• Iwilldevelop ithereforaverysimpleexampleandthenpointthewaytomoreinformationaboutit

• IthasaquitewelldefinedformalrepresentationbuthereIwilldeveloptheideaherevisually


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


Factor1AccesstoHousing

Factor2AccesstoRetailing

Factor3AccesstoHealthCare

Factor4AccesstoEducation

DesignSolutionsasWeightedAveraging


+ +

1

1

1

1

1

0

0

0

0

0

AddingorSynthesisingPhysicalInfluences

BooleanOperations


+

Factor1

Factor2

Factor3

Factor4

Solution


1 2

34


1 2

34

1 2

34


DesignSolution?

Factor1

Factor2

Factor3

Factor4

ObservedOutcome

ActualDevelopmentusingNeuralNetworks


ASimpleExample:AveragingbyOverlayThelistoffactors:• accessibilitytoexistingurbanservices,• costsofspatialcongestion,• accessibilitytorecreationalamenities,• areasofacceptablemicro-climate,• areasofwatercatchmentandpoordrainage, institutional

constraintsimposedbygovernment,• accessibilitytoexternalurbanmarkets,• subsidenceandextensive industrialpollution,• areasofsuitabletopography,• ruralamenityareas,• historicurbanareas,and• conservationofhighqualityagriculturalquality.


Generalisation:ModellingattheVeryLocalScale• Thenetworkofrelationsbetweenfactors:actors&agents

• Theproblem– theresolutionofconflictoverachangeinuseoflandinadenseurbanarea– designmaybe,decision

• Theagentsinthemodels– actors,stakeholdersversussites/buildings

• Thewaytheagentsinteractacrossthemapsofwhattheyconsidersignificanttochangeofuse

• Thewaytheagentseffectcompromise– twoproblemswhicharedualsofoneanother– rathertechnicalbutasketchofhowwemightproceed


• Alongpreamble Iknowbutletmebeginwiththeproblemfirstandthen Iwillsketchthemodel

• Theproblemisoneofreconcilingdifferentinterestsinlanddevelopment intheheartofaworldcity:London

• ItisasclosetotheheartofthecityaspossibleforitcentresonthepostcodeEC1A1AAwhichistheoldGeneralPostOfficeandisnowadjacenttothenewLondonStockExchange(whichisalmostvirtualnow)–

• Averyhistoricareawithenormousdevelopment pressures

• It’saTOYMODELwith6agentsoractorsand8sites– letusseehowitworks

• Ofcoursetomake itrealwecanscaleitinmanyways– manyactorsmanymorebuildingsetc.andalotofdataonprocesses


1

8

7

6

42

3

5


Actors/Stakeholders1CityCorporation

2Residents

3HospitalNHS

4Developers

5PropertySpec

6Banks

Sites/Buildings/Locations1AldersgateComplex

2StBotolph’s

3NomuraHouse

4MiltonHouse

5Postmans’Park

6BankofAmerica

7BartsNewBuilding

8BartsOldBuilding


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


001100001011111111110100101001001000000010100100

=M(ap)

1CityCorporation

2Residents

3HospitalNHS4Developers

5PropertySpec

6Banks

1AldersgateComplex

2StBotolph’s

3NomuraHouse4MiltonHouse

5Postmans’Park

6BankofAmerica

7BartsNewBuilding

8BartsOldBuildingAgentsSites/Bu

ildings


001100001011111111110100101001001000000010100100

011111001000111101111000010000011101010000010000

222101274313245313133313011111133313

=

A=MMT

ThePrimal:Interactionsbetweenactorswrt sites


001100001011111111110100101001001000000010100100

011111001000111101111000010000011101010000010000

5142141111110100415314012133121110111111414214111011111110111111

=

S=MT M

TheDual:Interactionsbetweensiteswrt actors


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


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


7%

19%

5%

21%

7%7%

18%

14%

Agents

1CityCorporation 17%

2Residents6%

3HospitalNHS17%

4Developers 23%

5PropertySpec25%

6Banks10%

Buildings


NextSteps

Realproblems– verylargenetworks,typesofconnection

Intensityordesirabilitymaps;spatialaveragingasdevelopedquitewidelyinoverlayanalysisinGIS

Rationalaveraging,simpleaveraging,weightingaveraging,dominance,andotherstrategiesofcompromiseornot;networksthatdon’tleadtosolutions

Themodelislongstanding– notnew,whatisnewisthedualprimalandtheembeddingofmapsintoit


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/


Thankshttp://www.spatialcomplexity.info/

http://www.complexcity.info/http://blogs.casa.ucl.ac.uk/

http://www.casa.ucl.ac.uk/

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