Abstract
Inspired by the fact that people make mistakes in a transient, fluctuating or chaotic environment, we establish a spatial prisoner's dilemma model where an agent commits action errors proportionally varying with the increasing/decreasing rate of the global cooperation fraction. A series of numerical simulations reveal that the cooperation level is enhanced in games in which the stag hunt (SH)-type dilemma is dominant; however, it is slightly diminished in games in which the chicken-type dilemma is dominant, compared with the standard network reciprocity model. Intensive analysis reveals that the noise created by the action error contribute to the spatial expansion of a cooperators' cluster, because a dilemma that is less chicken-type and more SH-type makes it disadvantageous for defectors to neighbor cooperators. Our finding, that errors in behavior in a chaotic environment contribute to the evolution of cooperation, might aim to explain the problem of how network reciprocity works.
| Original language | English |
|---|---|
| Article number | P01033 |
| Journal | Journal of Statistical Mechanics: Theory and Experiment |
| Volume | 2015 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
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All Science Journal Classification (ASJC) codes
- Statistical and Nonlinear Physics
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Dynamic noise from action errors enhances network reciprocity in the prisoner's dilemma game. / Tanimoto, Jun; Ogasawara, Takashi.
In: Journal of Statistical Mechanics: Theory and Experiment, Vol. 2015, No. 1, P01033, 2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Dynamic noise from action errors enhances network reciprocity in the prisoner's dilemma game
AU - Tanimoto, Jun
AU - Ogasawara, Takashi
PY - 2015
Y1 - 2015
N2 - Inspired by the fact that people make mistakes in a transient, fluctuating or chaotic environment, we establish a spatial prisoner's dilemma model where an agent commits action errors proportionally varying with the increasing/decreasing rate of the global cooperation fraction. A series of numerical simulations reveal that the cooperation level is enhanced in games in which the stag hunt (SH)-type dilemma is dominant; however, it is slightly diminished in games in which the chicken-type dilemma is dominant, compared with the standard network reciprocity model. Intensive analysis reveals that the noise created by the action error contribute to the spatial expansion of a cooperators' cluster, because a dilemma that is less chicken-type and more SH-type makes it disadvantageous for defectors to neighbor cooperators. Our finding, that errors in behavior in a chaotic environment contribute to the evolution of cooperation, might aim to explain the problem of how network reciprocity works.
AB - Inspired by the fact that people make mistakes in a transient, fluctuating or chaotic environment, we establish a spatial prisoner's dilemma model where an agent commits action errors proportionally varying with the increasing/decreasing rate of the global cooperation fraction. A series of numerical simulations reveal that the cooperation level is enhanced in games in which the stag hunt (SH)-type dilemma is dominant; however, it is slightly diminished in games in which the chicken-type dilemma is dominant, compared with the standard network reciprocity model. Intensive analysis reveals that the noise created by the action error contribute to the spatial expansion of a cooperators' cluster, because a dilemma that is less chicken-type and more SH-type makes it disadvantageous for defectors to neighbor cooperators. Our finding, that errors in behavior in a chaotic environment contribute to the evolution of cooperation, might aim to explain the problem of how network reciprocity works.
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U2 - 10.1088/1742-5468/2015/01/P01033
DO - 10.1088/1742-5468/2015/01/P01033
M3 - Article
AN - SCOPUS:84921811266
VL - 2015
JO - Journal of Statistical Mechanics: Theory and Experiment
JF - Journal of Statistical Mechanics: Theory and Experiment
SN - 1742-5468
IS - 1
M1 - P01033
ER -