Abstract
We have developed a new agent-based simulation tool to model social dilemmas for the case of a large number of not necessarily rational decision-makers (Szilagyi and Szilagyi, 2000). The combination of various personalities with stochastic learning makes it possible to simulate the multi-person Prisoners' Dilemma game for realistic situations. A variety of personality profiles and their arbitrary combinations can be represented, including agents whose probability of cooperation changes by an amount proportional to its reward from the environment. For the case of such agents the game has non-trivial but remarkably regular solutions. We discuss a method and present an algorithm for making accurate advance predictions of these solutions. We also propose our model as a viable approach for the study of populations of cells, organisms, groups, organizations, communities, and societies. It may lead to better understanding of the evolution of cooperation in living organisms, international alliances, sports teams, and large organizations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 281-290 |
| Number of pages | 10 |
| Journal | Systems Research and Behavioral Science |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 2002 |
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ASJC Scopus subject areas
- Management of Technology and Innovation
- Strategy and Management
- Social Sciences(all)
Cite this
Non-Trivial Solutions to the N-Person Prisoners' Dilemma. / Szilagyi, Miklos N; Szilagyi, Zoltan C.
In: Systems Research and Behavioral Science, Vol. 19, No. 3, 05.2002, p. 281-290.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Non-Trivial Solutions to the N-Person Prisoners' Dilemma
AU - Szilagyi, Miklos N
AU - Szilagyi, Zoltan C.
PY - 2002/5
Y1 - 2002/5
N2 - We have developed a new agent-based simulation tool to model social dilemmas for the case of a large number of not necessarily rational decision-makers (Szilagyi and Szilagyi, 2000). The combination of various personalities with stochastic learning makes it possible to simulate the multi-person Prisoners' Dilemma game for realistic situations. A variety of personality profiles and their arbitrary combinations can be represented, including agents whose probability of cooperation changes by an amount proportional to its reward from the environment. For the case of such agents the game has non-trivial but remarkably regular solutions. We discuss a method and present an algorithm for making accurate advance predictions of these solutions. We also propose our model as a viable approach for the study of populations of cells, organisms, groups, organizations, communities, and societies. It may lead to better understanding of the evolution of cooperation in living organisms, international alliances, sports teams, and large organizations.
AB - We have developed a new agent-based simulation tool to model social dilemmas for the case of a large number of not necessarily rational decision-makers (Szilagyi and Szilagyi, 2000). The combination of various personalities with stochastic learning makes it possible to simulate the multi-person Prisoners' Dilemma game for realistic situations. A variety of personality profiles and their arbitrary combinations can be represented, including agents whose probability of cooperation changes by an amount proportional to its reward from the environment. For the case of such agents the game has non-trivial but remarkably regular solutions. We discuss a method and present an algorithm for making accurate advance predictions of these solutions. We also propose our model as a viable approach for the study of populations of cells, organisms, groups, organizations, communities, and societies. It may lead to better understanding of the evolution of cooperation in living organisms, international alliances, sports teams, and large organizations.
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UR - http://www.scopus.com/inward/citedby.url?scp=0036246603&partnerID=8YFLogxK
U2 - 10.1002/sres.435
DO - 10.1002/sres.435
M3 - Article
AN - SCOPUS:0036246603
VL - 19
SP - 281
EP - 290
JO - Systems Research and Behavioral Science
JF - Systems Research and Behavioral Science
SN - 1092-7026
IS - 3
ER -