Muutke küpsiste eelistusi

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

Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 184,63 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Self-organizing Coalitions for Managing Complexity: Agent-based Simulation of Evolutionary Game Theory Models using Dynamic Social Networks for Interdisciplinary ApplicationsSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book provides an interdisciplinary approach to complexity, combining ideas from areas like complex networks, cellular automata, multi-agent systems, self-organization and game theory. The first part of the book provides an extensive introduction to these areas, while the second explores a range of research scenarios. Lastly, the book presents CellNet, a software framework that offers a hands-on approach to the scenarios described throughout the book.

In light of the introductory chapters, the research chapters, and the CellNet simulating framework, this book can be used to teach undergraduate and master’s students in disciplines like artificial intelligence, computer science, applied mathematics, economics and engineering. Moreover, the book will be particularly interesting for Ph.D. and postdoctoral researchers seeking a general perspective on how to design and create their own models.

1 Introduction
1(10)
Juan C. Burguillo
References
7(4)
Part I Background
2 Complex Systems
11(24)
Ivan Zelinka
Juan C. Burguillo
2.1 Short Historical Notes
12(1)
2.2 Basic Properties
12(2)
2.3 Computational and Algorithmic Complexity
14(7)
2.3.1 Examples of Computational Complexity
14(3)
2.3.2 Computational Complexity Theory
17(3)
2.3.3 Information Theory
20(1)
2.4 Chaos
21(3)
2.5 Fractals
24(4)
2.6 Complex Networks
28(1)
2.7 Adaptive Behavior and Evolutionary Computation
29(2)
2.8 Modeling and Simulating Complex Systems
31(1)
2.9 Conclusion
32(1)
2.10 Further Reading
33(2)
References
33(2)
3 Complex Networks
35(22)
Juan C. Burguillo
3.1 Short Historical Notes
35(1)
3.2 Network Models and Applications
36(1)
3.2.1 Graph Theory
36(1)
3.2.2 Network Theory
36(1)
3.2.3 Complex Networks
37(1)
3.3 Mathematics of Complex Networks
37(6)
3.3.1 Matrix Representation
37(1)
3.3.2 Directed and Weighted Networks
38(1)
3.3.3 Tree
39(1)
3.3.4 Degree
40(1)
3.3.5 Graph Density
40(1)
3.3.6 Paths
41(1)
3.3.7 Random Walks
41(1)
3.3.8 Distances
41(1)
3.3.9 Components
42(1)
3.4 Metrics in Complex Networks
43(5)
3.4.1 Degree Centrality
43(1)
3.4.2 Eigenvector Centrality
43(1)
3.4.3 Closeness Centrality
43(1)
3.4.4 Betweenness Centrality
44(1)
3.4.5 Groups: Cliques, Plexes and Cores
45(1)
3.4.6 Transitivity
46(1)
3.4.7 Clustering Coefficient
46(1)
3.4.8 Degree Distributions
47(1)
3.4.9 Power Laws
48(1)
3.5 Relevant Topologies in Complex Networks
48(7)
3.5.1 Regular Networks
49(1)
3.5.2 Random Networks
49(2)
3.5.3 Small World
51(1)
3.5.4 Scale Free
52(3)
3.6 Conclusion
55(1)
3.7 Further Reading
55(2)
References
55(2)
4 Cellular Automata
57(12)
Juan C. Burguillo
4.1 Short Historical Notes
57(3)
4.2 Basic Notation
60(1)
4.3 Basic Cellular Automata Definition
61(1)
4.4 Types of Neighborhoods
61(1)
4.5 Cellular Automata Classification
62(1)
4.6 Extended Cellular Automata
63(2)
4.6.1 Asynchronous
63(1)
4.6.2 Continuous State-Space
64(1)
4.6.3 Non-homogeneous
64(1)
4.6.4 Stochastic
64(1)
4.6.5 Memory-Based
64(1)
4.6.6 Mobile
65(1)
4.6.7 Dynamic Lattices
65(1)
4.6.8 Nested and Hierarchical
65(1)
4.7 Conclusion
65(1)
4.8 Further Reading
66(3)
References
66(3)
5 Multi-agent Systems
69(20)
Juan C. Burguillo
5.1 Short Historical Notes
70(1)
5.2 Intelligent and Autonomous Agents
71(8)
5.2.1 Deliberative Agents
71(2)
5.2.2 Reactive Agents
73(1)
5.2.3 Hybrid Agents
74(2)
5.2.4 Multi-agent Architectures
76(2)
5.2.5 Mobile Agents
78(1)
5.3 Ontologies
79(1)
5.4 Communication
80(2)
5.5 Coordination
82(2)
5.6 Methodologies
84(1)
5.7 Modeling and Simulating Complexity
85(1)
5.8 Conclusion
85(1)
5.9 Further Reading
86(3)
References
86(3)
6 Self-organization
89(12)
Juan C. Burguillo
6.1 Short Historical Notes
90(1)
6.2 Concepts of Self-organizing Systems
91(2)
6.3 Emergence
93(2)
6.4 Self-organization Versus Emergence
95(1)
6.5 Mechanisms for Self-organizing Multi-agent Systems
95(3)
6.5.1 Information-Based Perspectives
95(1)
6.5.2 Interaction-Based Perspectives
96(1)
6.5.3 Other Self-organizing Mechanisms
97(1)
6.6 Conclusion
98(1)
6.7 Further Reading
99(2)
References
99(2)
7 Game Theory
101(38)
Juan C. Burguillo
7.1 Short Historical Notes
102(1)
7.2 Representation of the Games
103(2)
7.2.1 Strategic Form
103(1)
7.2.2 Extensive Form
104(1)
7.2.3 Coalitional Form
105(1)
7.3 Types of Games
105(2)
7.3.1 Cooperative, Competitive and Hybrid Games
105(1)
7.3.2 Symmetric Versus Asymmetric Games
105(1)
7.3.3 Zero-Sum Versus Non-zero-Sum Games
106(1)
7.3.4 Simultaneous Versus Sequential Games
106(1)
7.3.5 Perfect, Imperfect and Complete Information Games
106(1)
7.3.6 Combinatorial Games
107(1)
7.4 Two-Person Zero-Sum Games
107(2)
7.4.1 The Minimax Criterium
108(1)
7.5 Relevant Concepts
109(2)
7.5.1 Best Response
109(1)
7.5.2 Dominant Strategies
110(1)
7.5.3 Pareto Optimality
110(1)
7.5.4 Nash Equilibrium
110(1)
7.6 Games in Coalitional Form
111(4)
7.6.1 N-Person TU Games
111(1)
7.6.2 Stages for Cooperating
112(1)
7.6.3 Imputations
112(1)
7.6.4 The Core
113(1)
7.6.5 The Shapley Value
114(1)
7.7 Popular Games
115(9)
7.7.1 Stag Hunt
115(1)
7.7.2 The Battle of Sexes
116(1)
7.7.3 Hawks and Doves
116(1)
7.7.4 The Prisoner's Dilemma (PD)
117(2)
7.7.5 The Iterated Prisoner's Dilemma (IPD)
119(3)
7.7.6 Similar Games and Mechanisms for Enforcing Cooperation
122(1)
7.7.7 Social Altruism
123(1)
7.8 Evolutionary Game Theory (EGT)
124(5)
7.8.1 Replicator Dynamics
125(1)
7.8.2 Evolutionary Stable Strategies (ESS)
125(2)
7.8.3 Cyclic Behavior
127(1)
7.8.4 Coevolution
128(1)
7.8.5 Extensions of the Evolutionary Game Theory Model
128(1)
7.9 Behavioral Game Theory
129(1)
7.10 Mechanism Design
130(1)
7.11 Heuristic Game Coalitions
131(2)
7.12 Conclusion
133(1)
7.13 Further Reading
134(5)
References
134(5)
Part II Self-Organizing Algorithms
8 Optimization Models with Coalitional Cellular Automata
139(32)
Juan C. Burguillo
Bernabe Dorronsoro
8.1 Introduction
139(2)
8.2 Evolutionary Algorithms
141(4)
8.3 Decentralized Evolutionary Algorithms
145(1)
8.4 Population Topologies for Evolutionary Algorithms
146(4)
8.4.1 Cellular Evolutionary Algorithms
146(1)
8.4.2 Enhanced Cellular Topologies
147(1)
8.4.3 Hierarchical Populations
148(1)
8.4.4 Population Structures Based on Social Networks
148(1)
8.4.5 Dynamic Topologies
149(1)
8.5 Evolutionary Algorithms with Coalitions
150(3)
8.5.1 Algorithmic Description of EACO
151(2)
8.6 Set of Problems
153(4)
8.6.1 Massively Multimodal Deceptive Problem (MMDP)
154(1)
8.6.2 Multimodal Problem Generator (P-PEAKS)
154(1)
8.6.3 Error Correcting Code Design Problem (ECC)
155(1)
8.6.4 Maximum Cut of a Graph (MAXCUT)
155(1)
8.6.5 Minimum Tardy Task Problem (MTTP)
156(1)
8.7 Results
157(7)
8.7.1 Selecting the Population Size
157(1)
8.7.2 Comparing cGA Versus EACO
158(1)
8.7.3 Influence of Parameters
159(3)
8.7.4 Complex Networks
162(1)
8.7.5 Changing the Neighborhood
163(1)
8.8 Conclusions
164(7)
References
166(5)
9 Time Series Prediction Using Coalitions and Self-organizing Maps
171(36)
Juan C. Burguillo
Juan Garcia-Rois
9.1 Introduction
171(2)
9.2 Time Series Prediction (TSP)
173(1)
9.3 Self-organizing Maps (SOM)
174(4)
9.3.1 SOM Formal Definition
175(2)
9.3.2 The VQTAM Model
177(1)
9.4 Context of the Simulation Framework
178(1)
9.5 Analyzing SOM over Spatial Networks
179(4)
9.5.1 Evaluation of Regular Topologies
179(1)
9.5.2 Number of Neurons m and Updating Probability Pu
180(3)
9.6 Analyzing SOM Performance over Complex Networks
183(3)
9.7 Analyzing SOM Performance over Real Time Series
186(5)
9.8 Coalitions and Complex Networks for SOM
191(4)
9.8.1 Introducing a General Coalitional Algorithm for SOM
191(1)
9.8.2 CASOM: A Coalitional Algorithm for SOM
192(3)
9.9 Experimental Results Obtained with CASOM
195(6)
9.9.1 Influence of the Infection Parameter
196(2)
9.9.2 Infection Versus Joining
198(1)
9.9.3 SOM Versus CASOM
199(2)
9.10 Dynamic Networks
201(2)
9.11 Conclusions
203(4)
References
204(3)
10 Coalitions of Electric Vehicles in Smart Grids
207(60)
Gabriel de O Ramos
Juan C. Burguillo
Ana L.C. Bazzan
10.1 Introduction
208(2)
10.2 Coalitions and Smart Grids
210(4)
10.2.1 Coalition Formation Background
210(2)
10.2.2 Coalition Formation in Smart Grids
212(1)
10.2.3 Coalitions in Complex Systems
213(1)
10.3 Smart Grid Scenario: Coalitions of Electric Vehicles
214(4)
10.3.1 The Scenario
214(1)
10.3.2 Constraints
215(1)
10.3.3 Communication Layer
216(1)
10.3.4 Problem Formulation
216(1)
10.3.5 Simulation
217(1)
10.4 Geographic-Based Constraints
218(23)
10.4.1 Modelling Constraints
218(2)
10.4.2 Dynamic Constrained Coalition Formation
220(4)
10.4.3 Self-adapting Coalition Formation
224(6)
10.4.4 Empirical Evaluation
230(9)
10.4.5 Summary
239(2)
10.5 User-Based Constraints
241(17)
10.5.1 Modelling Constraints
242(1)
10.5.2 Self-adapting Coalition Formation with Changing Coalitions
243(7)
10.5.3 Empirical Evaluation
250(8)
10.5.4 Summary
258(1)
10.6 Discussion
258(1)
10.7 Research Directions
259(8)
References
261(6)
Part III Evolutionary Games
11 Ownership and Trade in Complex Networks
267(26)
Juan C. Burguillo
11.1 Introduction
267(2)
11.2 Game Model
269(3)
11.2.1 Game Basic Strategies
269(2)
11.2.2 Network Topologies
271(1)
11.2.3 Rewiring (Partner Switching)
272(1)
11.2.4 Memetics Scenario
272(1)
11.3 Results
272(18)
11.3.1 Possessor's Game
273(4)
11.3.2 Trader's Game
277(5)
11.3.3 Cost Value Effect
282(1)
11.3.4 Accumulating Payoff
283(3)
11.3.5 A Traders' Coalition
286(4)
11.4 Conclusions
290(3)
References
291(2)
12 Promoting Indirect Reciprocity Using Coalitions
293(30)
Juan C. Burguillo
Ana Peleteiro
12.1 Introduction
293(2)
12.2 Donation Game Rules
295(1)
12.3 Model
296(6)
12.3.1 Reputation Sharing
297(1)
12.3.2 Action Selection
298(1)
12.3.3 Coalition Formation
299(1)
12.3.4 Changing the Strategy
299(2)
12.3.5 Network Topologies
301(1)
12.3.6 Rewiring
301(1)
12.4 Results
302(17)
12.4.1 Experimental Settings
302(1)
12.4.2 Emergence of Cooperation (Micro-analysis)
303(4)
12.4.3 Emergence of Cooperation (Macro-analysis)
307(3)
12.4.4 Regular (SP) Versus Random Networks (RN)
310(1)
12.4.5 Topology Influence
311(4)
12.4.6 Random Versus Selected Rewiring
315(1)
12.4.7 Alternative Strategy Dynamics
316(2)
12.4.8 Mutation
318(1)
12.4.9 Dependance on the Initial Conditions
318(1)
12.5 Conclusions
319(4)
References
321(2)
13 A Coalitional Game of Life
323(16)
Juan C. Burguillo
13.1 Introduction
323(1)
13.2 Life Rules
324(1)
13.3 Patterns
325(3)
13.3.1 Static Patterns
326(1)
13.3.2 Oscillators
326(1)
13.3.3 Spaceships
327(1)
13.3.4 Guns
328(1)
13.4 Properties
328(2)
13.4.1 Algorithmic Complexity
329(1)
13.4.2 Emergence and Self-replication
329(1)
13.4.3 Bounding an Unbounded Life
329(1)
13.4.4 Variants of Life
330(1)
13.5 A Coalitional Game of Life
330(5)
13.5.1 Rules of CoaLife
331(1)
13.5.2 CoaLife Scenarios
331(3)
13.5.3 Running CoaLife
334(1)
13.6 An Iterated Prisoner's Dilemma for CoaLife
335(2)
13.6.1 IPD-Life and IPD-CoaLife Rules
335(1)
13.6.2 Running the IPD-Based CoaLife
336(1)
13.7 Conclusions
337(2)
References
338(1)
Appendix: CellNet: A Hands-On Approach for Agent-Based Modeling and Simulation 339(2)
Index 341
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97833196989842e.html
Märksõnad: