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Simulation for the Social Scientist [Pehme köide]

  • Formaat: Paperback / softback, 288 pages, kõrgus x laius x paksus: 229x155x20 mm, kaal: 470 g, illustrations
  • Ilmumisaeg: 16-Apr-1999
  • Kirjastus: Open University Press
  • ISBN-10: 0335197442
  • ISBN-13: 9780335197446
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  • Formaat: Paperback / softback, 288 pages, kõrgus x laius x paksus: 229x155x20 mm, kaal: 470 g, illustrations
  • Ilmumisaeg: 16-Apr-1999
  • Kirjastus: Open University Press
  • ISBN-10: 0335197442
  • ISBN-13: 9780335197446
Teised raamatud teemal:
* What can computer simulation contribute to the social sciences?

* Which of the many approaches to simulation would be best for my social science project?

* How do I design, carry out and analyse the results from a computer simulation?

Simulation for the Social Scientist is a practical textbook on the techniques of building computer simulations to assist understanding of social and economic issues and problems.

Interest in social simulation has been growing very rapidly world-wide as a result of increasingly powerful hardware and software and also a rising interest in the application of ideas of complexity, evolution, adaptation and chaos in the social sciences. This authoritative book outlines all the common approaches to social simulation at a level of detail which will give social scientists an appreciation of the literature and allow those with some programming skills to create their own simulations.

Social scientists in a wide range of fields will find this book an essential tool for research, particularly in sociology, economics, anthropology, geography, organizational theory, political science, social policy, cognitive psychology and cognitive science. It will also appeal to computer scientists interested in distributed artificial intelligence, multi-agent systems and agent technologies.

Arvustused

"This will be a very useful book and will be greeted with some relief to improve reading lists on this and related fields."- Professor Carol Smart

Preface ix
Simulation and social science
1(13)
What is simulation?
2(4)
The history of social science simulation
6(3)
Simulating human societies
9(3)
Conclusion
12(2)
Simulation as a method
14(13)
The logic of simulation
15(2)
The stages of simulation-based research
17(8)
Conclusion
25(2)
System dynamics and world models
27(26)
Software
30(1)
An example: Hawks, doves and law-abiders
31(11)
Commentary
42(1)
World models
43(5)
Problems and an outlook
48(3)
Further reading
51(2)
Microanalytical simulation models
53(21)
Methodologies
56(5)
Software
61(1)
Examples
62(9)
Commentary
71(1)
Further reading
72(2)
Queuing models
74(18)
Characteristics of queuing models
75(6)
Software
81(1)
Examples
81(9)
Commentary
90(1)
Further reading
90(2)
Multilevel simulation models
92(29)
Some synergetics
94(5)
Software: MIMOSE
99(6)
Examples
105(13)
Commentary
118(1)
Further reading
119(2)
Cellular automata
121(37)
The Game of Life
123(2)
Other cellular automata models
125(11)
Extensions to the basic model
136(5)
Software
141(16)
Further reading
157(1)
Multi-agent models
158(37)
Agents and agency
159(5)
Agent architecture
164(4)
Examples of multi-agent modelling
168(6)
Building multi-agent simulations
174(19)
Further reading
193(2)
Learning and evolutionary models
195(43)
Artificial neural networks
197(3)
Using artificial neural networks for social simulation
200(5)
Designing neural networks
205(2)
Implementation
207(11)
Genetic algorithms
218(18)
Further reading
236(2)
Appendix A Web sites 238(12)
General
238(1)
Programs, packages and languages
239(2)
Electronic journals
241(1)
System dynamics
241(1)
Microsimulation
242(2)
Queuing models
244(1)
Cellular Automata
245(1)
Distributed artificial intelligence
246(1)
Genetic algorithms
247(1)
Neural networks
248(2)
Appendix B Linear stability analysis of the hawk--dove--lawabider model 250(4)
Appendix C Random number generators 254(3)
References 257(9)
Author index 266(3)
Subject index 269


Nigel Gilbert. University of Surrey





Klaus G. Troitzsch. University Coblenza-Landau