论文标题
导师和重组者：多维社会学习
Mentors and Recombinators: Multi-Dimensional Social Learning
论文作者
论文摘要
我们研究了一组策略是多维的游戏，新代理商可能会从不同导师那里学习各种战略维度。我们介绍了一个新的动力学家族，即重组动力学，其特征是单个参数，重组速率r在[0,1]中。 R = 0的情况与标准复制器动力学一致。 r = 1的相反情况对应于一个设置，在该设置中，每个新代理商都从另一个导师那里学习每个新的战略维度，并将这些维度结合到她采用的策略中。我们在这些动态下充分表征了固定状态和稳定状态，我们表明它们可以预测各种应用中的新行为。
We study games in which the set of strategies is multi-dimensional, and new agents might learn various strategic dimensions from different mentors. We introduce a new family of dynamics, the recombinator dynamics, which is characterised by a single parameter, the recombination rate r in [0,1]. The case of r = 0 coincides with the standard replicator dynamics. The opposite case of r = 1 corresponds to a setup in which each new agent learns each new strategic dimension from a different mentor, and combines these dimensions into her adopted strategy. We fully characterise stationary states and stable states under these dynamics, and we show that they predict novel behaviour in various applications.
