Self-organizing Coalitions for Managing Complexity: Agent-based Simulation of Evolutionary Game Theory Models using Dynamic Social Networks for Interdisciplinary Applications

348 Pages·2018·24.9 MB·English

by  Juan C. Burguillo (auth.)

Emergence, Complexity and Computation ECC
Juan C. Burguillo
Self-organizing
Coalitions for
Managing
Complexity
Agent-based Simulation of Evolutionary
Game Theory Models using Dynamic
Social Networks for Interdisciplinary
Applications
Emergence, Complexity and Computation
Volume 29
Series editors
Ivan Zelinka, Technical University of Ostrava, Ostrava, Czech Republic
e-mail: [email protected]
Andrew Adamatzky, University of the West of England, Bristol, UK
e-mail: [email protected]
Guanrong Chen, City University of Hong Kong, Hong Kong, China
e-mail: [email protected]
Editorial Board
Ajith Abraham, MirLabs, USA
Ana Lucia C. Bazzan, Universidade Federal do Rio Grande do Sul, Porto
Alegre, RS, Brazil
Juan C. Burguillo, University of Vigo, Spain
Sergej Čelikovský, Academy of Sciences of the Czech Republic, Czech Republic
Mohammed Chadli, University of Jules Verne, France
Emilio Corchado, University of Salamanca, Spain
Donald Davendra, Technical University of Ostrava, Czech Republic
Andrew Ilachinski, Center for Naval Analyses, USA
Jouni Lampinen, University of Vaasa, Finland
Martin Middendorf, University of Leipzig, Germany
Edward Ott, University of Maryland, USA
Linqiang Pan, Huazhong University of Science and Technology, Wuhan, China
Gheorghe Păun, Romanian Academy, Bucharest, Romania
Hendrik Richter, HTWK Leipzig University of Applied Sciences, Germany
Juan A. Rodriguez-Aguilar, IIIA-CSIC, Spain
Otto Rössler, Institute of Physical and Theoretical Chemistry, Tübingen, Germany
Vaclav Snasel, Technical University of Ostrava, Czech Republic
Ivo Vondrák, Technical University of Ostrava, Czech Republic
Hector Zenil, Karolinska Institute, Sweden
The Emergence, Complexity and Computation (ECC) series publishes new
developments, advancements and selected topics in the fields of complexity,
computation and emergence. The series focuses on all aspects of reality-based
computation approaches from an interdisciplinary point of view especially from
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interdisciplinary insight on the mutual intersection of subareas of computation,
complexity and emergence and its impact and limits to any computing based on
physical limits (thermodynamic and quantum limits, Bremermann’s limit, Seth
Lloyd limits…) as well as algorithmic limits (Gödel’s proof and its impact on
calculation,algorithmiccomplexity,theChaitin’sOmeganumberandKolmogorov
complexity, non-traditional calculations like Turing machine process and its
consequences,…) and limitations arising in artificial intelligence field. The topics
are (but not limited to) membrane computing, DNA computing, immune
computing, quantum computing, swarm computing, analogic computing, chaos
computingandcomputingontheedgeofchaos,computationalaspectsofdynamics
of complex systems (systems with self-organization, multiagent systems, cellular
automata, artificial life,…), emergence of complex systems and its computational
aspects,and agent based computation. The main aim ofthis series it todiscussthe
above mentioned topics from an interdisciplinary point of view and present new
ideas coming from mutual intersection of classical as well as modern methods of
computation.Withinthescopeoftheseriesaremonographs,lecturenotes,selected
contributions from specialized conferences and workshops, special contribution
from international experts.
More information about this series at http://www.springer.com/series/10624
Juan C. Burguillo
Self-organizing Coalitions
for Managing Complexity
Agent-based Simulation of Evolutionary
Game Theory Models using Dynamic Social
Networks for Interdisciplinary Applications
123
JuanC. Burguillo
Department ofTelematics Engineering,
SchoolofTelecommunicationsEngineering
University of Vigo
Vigo
Spain
ISSN 2194-7287 ISSN 2194-7295 (electronic)
Emergence, Complexity andComputation
ISBN978-3-319-69896-0 ISBN978-3-319-69898-4 (eBook)
https://doi.org/10.1007/978-3-319-69898-4
LibraryofCongressControlNumber:2017959900
©SpringerInternationalPublishingAG2018
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To my family:
Alejandra, Sofía and Álex.
They are my motivation,
and the coauthors of my Life!
Preface
This book mainly deals with the paradigm of how using self-organized coalitions,
in games played by selfish agents, can support the emergence of cooperation in
complex scenarios. The main idea was to convert pure competitive games into a
mixofcompetitionandcooperationthatprovidesahighprofittotheselfishplayers
and also to the whole population. At the same time, the whole game outcome
should increase the efficiency in the resource assignment from the game designer
perspective.
The use of self-organized coalitions is presented in this book from several
perspectives, considering combinatorial optimization problems, time series pre-
diction, smart grid infrastructures, game theory competitions, indirect reciprocity
and even thepopular Conway’s Game of Life. My interest in these topics, and the
useofmixedcompetition/cooperationscenarios,cametomefifteenyearsagowhen
reading the seminal works from Robert Axelrod and Martin Nowak about how
cooperation may emerge in complex scenarios played by selfish agents. Both
authors used game theory and evolutionary games as tools to model and simulate
complexscenarioswithareducedsetofsimplerules.Infact,alonglastdecades,the
useofevolutionalgametheoryhasbecomearelevanttechnictosimulatethem,and
to perform experiments in many different scientific fields as evolutionary biology,
sociology, economics, computer science and networked computing among others.
The first part of this book gives an introductory background on several disci-
plinesthatarestronglyinterconnectedandrelatedtotheresearchchapterspresented
afterwards. These disciplines are complex systems, complex networks, cellular
automata, multi-agent systems, self-organization and game theory. After the
background part, the next two parts of this book provide some research scenarios
and deal with my interest in exploring, together with several coauthors, the use of
self-organizing coalitions to allow the emergence of cooperation in competitive
gamesplayedbyselfishagents.Thesecondpartdescribestheuseofself-organized
coalitions in algorithms dealing with optimization problems, time series prediction
ormanagingenergydistributioninsmartgridenvironments.Thethirdpartismore
orientedtogamescenarios,consideringapossession-tradinggame,theevolutionof
indirect reputation or the classical and popular Game of Life from John Conway.
vii
viii Preface
Partial and more focused versions of most of the chapters of the research part
werepublishedsince2010injournals,bookchaptersandinternationalconferences
mainlyrelatedtoartificialintelligencetopicslikeoptimization,multi-agentsystems,
computerscience,parallelcomputing.Nevertheless,thepresentchaptershavebeen
reorganized integrating several publications, re-simulated again in more complex
scenarios, rewritten and deeper extended than the ones provided in the original
versions. The last chapter was not published before, and it is a direct application
of the ideas presented in the previous ones into the complex scenario provided by
the Game of Life. Publishing all these works together offers a wider perspective
of the potential of self-organizing coalitions for finding and evaluating optimal
solutions to complex problems.
Almost all the research chapters presented in this book, except the one about
smart grids, have been simulated using CellNet: a Java simulating framework
released with this book as free and open-source software, to support a hands-on
approach to the scenarios and experiments described along the book. An appendix
describes how to download CellNet, execute it, navigate through its user-friendly
interface and to access or modify its source code.
Considering the introductory chapters, the research ones and the CellNet sim-
ulating framework provided as supplementary material; this book should be
accessible for graduated andmaster students that wanttoenhancetheir knowledge
and perform their thesis related to the topics discussed in this book. However, this
bookisparticularlyinterestingforPh.D.andpostdoctoralstudentsthatarefamiliar
with some of these topics, but want to have a more general perspective to explore
new ideas and to design and create their own new models, games and scenarios.
Finally, I would like to thank my research colleagues from the Telematics
Department attheUniversity ofVigofor discussions, opinions, research proposals
andmainlyfun.Fundingsupportforseveralworkspresentedherehasbeenprovided
bytheAtlanticResearchCenterforInformationandCommunicationTechnologies
(AtlantTIC).Someoftheseideas,andmanymorenotincludedinthisvolume,came
from stays in relevant institutions, whose researchers quizzed me with interesting
problemstosolvefromaninterdisciplinarypointofview.IwouldliketothankProf.
Ivan Zelinka, who invited me years ago to meet his research group in Czech
Republic and was the catalyzer of my closer approach to complex systems. I also
wanttothankDr.DjamelKadrahouiforinvitingmetostayatthePublicResearch
Center Henri Tudor in Luxembourg that allowed me to meet new colleagues that
have also influenced this book. My special thanks to Prof. Michael Luck at Kings
CollegeLondon,whereIenjoyedaniceworkingatmospherethathelpedmetofinish
someoftheintroductorychaptersofthisbook.Ialsowanttothankthecoauthorsof
some of the chapters for their generous support and collaboration. Finally, I also
wanttothankMs.VarshaPrabakaranandDr.TomDitzingerfromSpringer,fortheir
patienceand supportwhilecompleting this endeavour.
Vigo, Spain Juan C. Burguillo
September 2017
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Juan C. Burguillo
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Part I Background
2 Complex Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Ivan Zelinka and Juan C. Burguillo
2.1 Short Historical Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Basic Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Computational and Algorithmic Complexity. . . . . . . . . . . . . . . 14
2.3.1 Examples of Computational Complexity . . . . . . . . . . . 14
2.3.2 Computational Complexity Theory . . . . . . . . . . . . . . . 17
2.3.3 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4 Chaos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Fractals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.6 Complex Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.7 Adaptive Behavior and Evolutionary Computation . . . . . . . . . . 29
2.8 Modeling and Simulating Complex Systems. . . . . . . . . . . . . . . 31
2.9 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.10 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 Complex Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Juan C. Burguillo
3.1 Short Historical Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 Network Models and Applications. . . . . . . . . . . . . . . . . . . . . . 36
3.2.1 Graph Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.2 Network Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.3 Complex Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . 37
ix
x Contents
3.3 Mathematics of Complex Networks . . . . . . . . . . . . . . . . . . . . . 37
3.3.1 Matrix Representation. . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.2 Directed and Weighted Networks . . . . . . . . . . . . . . . . 38
3.3.3 Tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3.4 Degree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.5 Graph Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.6 Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.7 Random Walks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.8 Distances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.9 Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.4 Metrics in Complex Networks. . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.1 Degree Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.2 Eigenvector Centrality . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.3 Closeness Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.4 Betweenness Centrality. . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.5 Groups: Cliques, Plexes and Cores . . . . . . . . . . . . . . . 45
3.4.6 Transitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4.7 Clustering Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4.8 Degree Distributions. . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.9 Power Laws. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.5 Relevant Topologies in Complex Networks . . . . . . . . . . . . . . . 48
3.5.1 Regular Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.5.2 Random Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.5.3 Small World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.5.4 Scale Free . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.7 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4 Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Juan C. Burguillo
4.1 Short Historical Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Basic Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Basic Cellular Automata Definition . . . . . . . . . . . . . . . . . . . . . 61
4.4 Types of Neighborhoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.5 Cellular Automata Classification . . . . . . . . . . . . . . . . . . . . . . . 62
4.6 Extended Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.6.1 Asynchronous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.6.2 Continuous State-Space . . . . . . . . . . . . . . . . . . . . . . . 64
4.6.3 Non-homogeneous . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.6.4 Stochastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.6.5 Memory-Based. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.6.6 Mobile. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.6.7 Dynamic Lattices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65