Culture on the Edge of Chaos

112 Pages·2018·5.092 MB·English

by  Robert G. Reynolds

SPRINGER BRIEFS IN COMPUTER SCIENCE
Robert G. Reynolds
Culture on the
Edge of Chaos
Cultural
Algorithms and
the Foundations of
Social Intelligence
123
SpringerBriefs in Computer Science
Moreinformationaboutthisseriesathttp://www.springer.com/series/10028
Robert G. Reynolds
Culture on the Edge of Chaos
Cultural Algorithms and the Foundations
of Social Intelligence
RobertG.Reynolds
ComputerScienceDepartment
WayneStateUniversity
Detroit,MI,USA
ISSN2191-5768 ISSN2191-5776 (electronic)
SpringerBriefsinComputerScience
ISBN978-3-319-74169-7 ISBN978-3-319-74171-0 (eBook)
https://doi.org/10.1007/978-3-319-74171-0
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Thanks to my family, Kathy, Lauren,
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Contents
1 TheCulturalAlgorithm:CultureontheEdgeofChaos. . . . . . . . . 1
1.1 CulturalAlgorithms:Data-DrivenProblemSolvinginComplex
Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 CultureasaProblem-SolvingProcess. . . . . . . . . . . . . . . . . . . . 2
1.3 TheCulturalEngine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 CultureontheEdgeofChaos. . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 TheSocialOrganizationModels. . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 OrganizationoftheBook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 CulturalAlgorithmFramework. . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 TheCulturalAlgorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 TheCulturalAlgorithmKnowledgeSources. . . . . . . . . . . . . . . . 15
2.4 TheCommunicationProtocol. . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 ThePopulationSpace. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 ModelingtheSocialFabric. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 HomogeneousTopologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3 HeterogeneousTopologies.. . . .. . . .. . . .. . . .. . . .. . .. . . .. 31
3.4 SubculturedHeterogeneousTopologies. . . . . . . . . . . . . . . . . . . 35
3.5 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4 GeneratingChaos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 TheConesWorldGenerator. . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5 SocialMetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2 TheDispersionMetric:SocialTension. . . . . . . . . . . . . . . . . . . . 46
vii
viii Contents
5.3 TheMajorityWinScoresandtheInnovationCosts. . . . . . . . . . . 47
5.4 TrackingProblem-SolvingBehaviorinTermsoftheSocial
FabricMetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.5 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6 TheCulturalEngine:PuttingIndividualstoWork. . . . . . . . . . . . . 53
6.1 KnowledgeSwarmsandMaxwell’sDemon. . . . . . . . . . . . . . . . 53
6.2 TheBasicLawsofThermodynamics. . . . . . . . . . . . . . . . . . . . . 53
6.3 Maxwell’sDemon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6.4 TheCulturalEngine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
6.5 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
7 ComparingNuclearFamilyandExtendedFamilySocial
Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7.2 OverallPerformanceComparison. . . . . . . . . . . . . . . . . . .. . . . . 60
7.3 LearningCurves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
7.4 t-TestsforPerformanceDifferences. . . . . . . . . . . . . . . . . . . . . . 68
7.5 UsingSocialMetricstoAssessEnginePerformance. . . . . . . . . . 69
7.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
8 ThePowerofSubcultures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
8.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
8.2 OverallPerformanceComparison. . . . . . . . . . . . . . . . . . .. . . . . 78
8.3 SubculturalSupportofProblemSolvingPredictability. . . . . . . . 83
8.4 StatisticalComparisonofSubculturesandHomogeneous
Usingt-Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
8.5 TheStatisticalComparisonofSubculturesandHeterogeneous
Usingt-Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
8.6 TheAbilityofSubculturestoSupportIncreased
PopulationSize. . . .. . . . . .. . . . . . .. . . . . .. . . . . .. . . . . .. . 88
8.7 HowSubculturesImpactCulturalEnginePerformanceinTerms
oftheSocialMetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
8.8 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
9 ConclusionsandFutureWork. . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Chapter 1
The Cultural Algorithm: Culture
on the Edge of Chaos
1.1 Cultural Algorithms: Data-Driven Problem Solving
in Complex Systems
Traditionallyevolutionarycomputationhasfocusedonpopulation-basedmodels.In
thesemodels,apopulationofproblemsolversexchangeslimitedamountsofknowl-
edgeaboutaproblemtobesolvedandcanusethisknowledgetocollectivelysearch
for a solution. Each problem solver makes decisions based upon the knowledge
availabletoit.Thisapproachissuitableforproblemswheretheknowledgeneeded
tosolveaproblemislimited.
Data- or knowledge-driven systems on the other hand reflect problems gener-
ated by complex systems that can be described at many levels of detail where the
typeofknowledgeavailablemaychangefromleveltolevelinthesystemandwhere
manylevelsmayneedtobeexaminedinordertofindasolutiontoagivenproblem.
Jayyousi and Reynolds (2014) define a complex system as one made up of an
organized group of heterogeneous independent units who interact with their envi-
ronment and each other. They adapt based on feedback from the environment. In
other words, a complex system is a mixture of relatively simple components or
buildingblocksthatarecombinedtogetherthroughaseriesofbasicinteractions.The
interaction between these building blocks or basic units can produce emergent
behaviorsthatcannotbepredictedfromknowledgeoftheindividualunitsalone.
Acomplexsystemoftenhasthefollowingcharacteristics(JayyousiandReynolds
2014):
1. Relationshipsinacomplexsystemareoftennonlinear,i.e.,effectsarenotdirectly
proportionaltocauses.
2. Complexsystemscontainpositiveandnegativefeedbackloops.
3. Complex systems are open, i.e. usually far from equilibriums, but may form
patternsofstability.
©TheAuthor(s),underexclusivelicencetoSpringerInternationalPublishingAG, 1
partofSpringerNature2018
R.G.Reynolds,CultureontheEdgeofChaos,SpringerBriefsinComputerScience,
https://doi.org/10.1007/978-3-319-74171-0_1
2 1 TheCulturalAlgorithm:CultureontheEdgeofChaos
4. Complexsystemshavememory,i.e.,historymatters.
5. Complexsystemmayproduceemergentphenomena.
DataScienceisconcernedwiththedevelopmentofpredictivemodelsofcomplex
systemsthroughthe use oftools frommathematics,statistics,machinelearning, and
other disciplines. Complex systems can operate on numerous temporal and spatial
levels.Itisfrequentlythecasethatthedatacollectionprocessisconcentratedonone
level or another. Questions or hypotheses posed at the macro-level may not have
sufficientdatatotestthematthatlevel.Thus,itisimportantforadatascientisttobe
abletotraverselevelssuchthatquestionsaskedononelevelwithsparsedatacanbe
re-expressed at another level with sufficient data. As a result, the problem-solving
processcanmovefromonelevelofgranularitytoanotherbasedupondataavailability.
Theexample inFig.1.1correspondstoanancienturban centerintheValleyof
Oaxaca,Mexico,thatdatesto500BC(JayyousiandReynolds2014).Thediagram
shows three different levels of detail at which the system can be described: the
macro-level, the meso-level, and the micro-level. Hypotheses reflecting the overall
designofthecitycanbeexpressedatthemacro-level.Themeso-levelcorresponds
to the basic neighborhood or barrio structure within the city. The micro-level
describes the basic household terraces along with city structures. While the most
interesting general hypotheses can be posed at the macro-level, much of the data
collected by archaeologists was at the micro-level. So, the knowledge sources
needed at one level may be different from those needed at another level. For an
evolutionary algorithm to be successful as a tool for the data scientist, these
knowledge sources need to operate in parallel and communicate with each other
overthedifferentlevelsduringtheproblem-solvingprocess.Thus,thecomputation
isdirectedbytheneedsoftheknowledgesourcesratherthanthoseofthepopulation
ofproblemsolvers.Thepopulationofproblemsolversmediatesthetransmissionof
knowledgefromonelevelofabstractiontoanother.
Deep learning inits mostgeneral sense reflects this traversal ofdata abstraction
levels, from fine-grained detail to coarse grained detail and vice versa. While the
termdeeplearningisoftenrelatedtoworkinneuralnetworks,thegeneralprinciples
can,ofcourse,applytoalltypesoflearningapproachesusedwithcomplexsystems.
The Cultural Algorithm is a nature-inspired algorithm designed to navigate the
hierarchy of hypotheses often found associated with complex systems. As such,
wedesignateitasatoolformulti-levellearningincomplexsystems.
1.2 Culture as a Problem-Solving Process
CulturalAlgorithmsweredevelopedasamechanismbywhichtodealwithproblem
solving andlearning withincomplexsystemsbyReynolds (1979).Complexsocial
systems can be found at various scales, and the types of problems that they are
presentedwithareaffectedbythatscale.InFig.1.2,varioussocialsystemsthatare
used as the basis for models of social problem solving, such as Particle Swarm
Optimization(BrattonandKennedy2007)andAntColonysystems,arecomparedin