TY - GEN
T1 - An experimental study of n-person iterated prisoner’s dilemma games
AU - YAO, Xin
AU - DARWEN, Paul J.
PY - 1995
Y1 - 1995
N2 - The Iterated Prisoner’s Dilemma game has been used extensively in the study of the evolution of cooperative behaviours in social and biological systems. There have been a lot of experimental studies on evolving strategies for 2-player Iterated Prisoner’s Dilemma games (2IPD), However, there are many real world problems, especially many social and economic ones, which cannot be modelled by the 2IPD. The n-player Iterated Prisoner’s Dilemma (NIPD) is a more realistic and general game which can model those problems. This paper presents two sets of experiments on evolving strategies for the NIPD. The first set of experiments examine the impact of the number of players in the NIPD on the evolution of cooperation in the group. Our experiments show that cooperation is less likely to emerge in a large group than in a small group. The second set of experiments study the generalisation ability of evolved strategies from the point of view of machine learning. Our experiments reveal the effect of changing the evolutionary environment of evolution on the generalisation ability of evolved strategies. © Springer-Verlag Berlin Heidelberg 1995.
AB - The Iterated Prisoner’s Dilemma game has been used extensively in the study of the evolution of cooperative behaviours in social and biological systems. There have been a lot of experimental studies on evolving strategies for 2-player Iterated Prisoner’s Dilemma games (2IPD), However, there are many real world problems, especially many social and economic ones, which cannot be modelled by the 2IPD. The n-player Iterated Prisoner’s Dilemma (NIPD) is a more realistic and general game which can model those problems. This paper presents two sets of experiments on evolving strategies for the NIPD. The first set of experiments examine the impact of the number of players in the NIPD on the evolution of cooperation in the group. Our experiments show that cooperation is less likely to emerge in a large group than in a small group. The second set of experiments study the generalisation ability of evolved strategies from the point of view of machine learning. Our experiments reveal the effect of changing the evolutionary environment of evolution on the generalisation ability of evolved strategies. © Springer-Verlag Berlin Heidelberg 1995.
UR - http://www.scopus.com/inward/record.url?scp=84958977108&partnerID=8YFLogxK
U2 - 10.1007/3-540-60154-6_50
DO - 10.1007/3-540-60154-6_50
M3 - Conference paper (refereed)
SN - 9783540601548
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 90
EP - 108
BT - Progress in Evolutionary Computation : AI '93 and AI '94 Workshops on Evolutionary Computation, Melbourne, Victoria, Australia, November 16, 1993, Armidale, NSW, Australia, November 21-22, 1994. Selected Papers
A2 - YAO, Xin
PB - Springer
ER -