Mikkelsen Kaare B, Bach Lars A
Interacting Minds Center, Aarhus University, DK-8000 Aarhus C, Denmark.
Department of Engineering, Aarhus University, DK-8000 Aarhus C, Denmark.
PLoS One. 2016 Feb 4;11(2):e0147207. doi: 10.1371/journal.pone.0147207. eCollection 2016.
The study investigates the effect on cooperation in multiplayer games, when the population from which all individuals are drawn is structured-i.e. when a given individual is only competing with a small subset of the entire population.
To optimize the focus on multiplayer effects, a class of games were chosen for which the payoff depends nonlinearly on the number of cooperators-this ensures that the game cannot be represented as a sum of pair-wise interactions, and increases the likelihood of observing behaviour different from that seen in two-player games. The chosen class of games are named "threshold games", and are defined by a threshold, M > 0, which describes the minimal number of cooperators in a given match required for all the participants to receive a benefit. The model was studied primarily through numerical simulations of large populations of individuals, each with interaction neighbourhoods described by various classes of networks.
When comparing the level of cooperation in a structured population to the mean-field model, we find that most types of structure lead to a decrease in cooperation. This is both interesting and novel, simply due to the generality and breadth of relevance of the model-it is likely that any model with similar payoff structure exhibits related behaviour. More importantly, we find that the details of the behaviour depends to a large extent on the size of the immediate neighbourhoods of the individuals, as dictated by the network structure. In effect, the players behave as if they are part of a much smaller, fully mixed, population, which we suggest an expression for.
本研究调查当所有个体所来自的群体具有结构时——即当一个给定个体仅与整个群体的一小部分子集竞争时,对多人游戏中合作的影响。
为了优化对多人效应的关注,选择了一类游戏,其收益非线性地依赖于合作者的数量——这确保了该游戏不能表示为成对互动的总和,并增加了观察到与两人游戏中不同行为的可能性。所选择的这类游戏被称为“阈值游戏”,并由一个阈值(M>0)定义,该阈值描述了在给定匹配中所有参与者获得收益所需的最小合作者数量。该模型主要通过对大量个体的数值模拟进行研究,每个个体的互动邻域由各种类型的网络描述。
当将结构化群体中的合作水平与平均场模型进行比较时,我们发现大多数类型的结构会导致合作减少。这既有趣又新颖,仅仅是因为该模型的普遍性和相关性广度——任何具有类似收益结构的模型可能都表现出相关行为。更重要的是,我们发现行为细节在很大程度上取决于个体直接邻域的大小,这由网络结构决定。实际上,玩家的行为就好像他们是一个小得多的完全混合群体的一部分,对此我们提出了一种表达方式。