In social dilemmas, individual behavior generally follows the characteristics of conditional cooperation and emotional conditional cooperation. However, it is hard to adequately explain the behavior patterns of conditional cooperation with the evolutionary game theory. This paper introduces expectation-based reinforcement learning methods in the public goods game to investigate and account for the behavior patterns. Instead of letting individuals occupy the entire network as previous studies have done, we focus on studying individual behavior patterns on a network with empty nodes. The results under total population density show the effectivity of our model as they are consistent with those of the previous studies, that is, individuals’ behavior exhibits conditional cooperation and its variant moody conditional cooperation. However, in the network with empty nodes, conditional cooperation shows opposite trends. We finally demonstrate that an appropriate population density can facilitate the maintenance and development of cooperation.

在社会困境中，个体行为一般遵循条件合作和情感条件合作的特征。然而，很难用进化博弈论充分解释条件合作的行为模式。本文介绍了公共物品游戏中基于期望的强化学习方法来调查和解释行为模式。我们不像以前的研究那样让个人占据整个网络，而是专注于研究具有空节点的网络上的个人行为模式。总人口密度下的结果表明我们模型的有效性，因为它们与先前的研究一致，即个体的行为表现出条件合作及其变异的喜怒无常的条件合作。然而，在有空节点的网络中，有条件的合作表现出相反的趋势。我们最终证明了适当的人口密度可以促进合作的维持和发展。