Abstract

In this article, we propose an aspiration-based coevolution of link weight, and explore how this set-up affects the evolution of cooperation in the spatial prisoner's dilemma game. In particular, an individual will increase the weight of its link to its neighbours only if the payoff received via this interaction exceeds a pre-defined aspiration. Conversely, if the received payoff is below this aspiration, the link weight with the corresponding neighbour will decrease. Our results show that an appropriate aspiration level leads to a high-cooperation plateau, whereas too high or too low aspiration will impede the evolution of cooperation. We explain these findings with a comprehensive analysis of transition points and with a systematic analysis of typical configuration patterns. The presented results provide further theoretical insights with regards to the impact of different aspiration levels on cooperation in human societies.

Highlights

  • The emergence and maintenance of cooperation have long puzzled scientists of different disciplines due to the fact2018 The Authors

  • Increasing induces the increasing of the average link weight, which plays a crucial role in promoting cooperation as shown in [49,50]

  • Different from these dynamical networks, we adopt static weighted network in combination with the individual’s aspiration, but we find that intermediate aspiration level boosts cooperation

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Summary

Introduction

In view of the above situation, we first present our aspiration-based coevolution model of the PDG where link weight will be strengthened as a reward once the focal player is satisfied with the gains from his neighbours, otherwise link weight will be weakened as the punishment. If the payoff pxy from neighbour y exceeds his aspiration, player x will reinforce the relationship with player y in order to gain more benefits for the interaction, otherwise player x will weaken the link weight to reduce his losses. This process can be described as the following equation: wxy = wxy + , if Pxy ≥ A wxy = wxy −.

Results
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Conclusion
Opportunistic migration in spatial evolutionary
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