Buesser Pierre, Tomassini Marco, Antonioni Alberto
Information Systems Institute, HEC, University of Lausanne, CH-1015 Lausanne, Switzerland.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Oct;88(4):042806. doi: 10.1103/PhysRevE.88.042806. Epub 2013 Oct 11.
We study evolutionary games in a spatial diluted grid environment in which agents strategically interact locally but can also opportunistically move to other positions within a given migration radius. Using the imitation of the best rule for strategy revision, it is shown that cooperation may evolve and be stable in the Prisoner's Dilemma game space for several migration distances but only for small game interaction radius while the Stag Hunt class of games become fully cooperative. We also show that only a few trials are needed for cooperation to evolve, i.e., searching costs are not an issue. When the stochastic Fermi strategy update protocol is used cooperation cannot evolve in the Prisoner's Dilemma if the selection intensity is high in spite of opportunistic migration. However, when imitation becomes more random, fully or partially cooperative states are reached in all games for all migration distances tested and for short to intermediate interaction radii.
我们在空间稀释网格环境中研究进化博弈，在这种环境中，主体在局部进行策略性互动，但也可以随机移动到给定迁移半径内的其他位置。使用模仿最佳策略进行策略修正的方法，结果表明，在囚徒困境博弈空间中，对于几个迁移距离，合作可能会进化并稳定，但仅适用于较小的博弈互动半径，而猎鹿类博弈则会完全合作。我们还表明，合作的进化只需要进行少量试验，即搜索成本不是问题。当使用随机费米策略更新协议时，如果选择强度较高，尽管存在随机迁移，囚徒困境中的合作也无法进化。然而，当模仿变得更加随机时，对于所有测试的迁移距离以及短到中等的互动半径，在所有博弈中都会达到完全或部分合作状态。
