模仿学习作为集体大脑的连接器
Imitative learning as a connector of collective brains
摘要：
合作可以帮助一组代理人更有效地解决问题的概念比如果这些代理人在孤立的工作是普遍的，尽管很少有定量的基础来支持它。在这里，我们考虑一种原始的合作形式-模仿学习-允许代理人之间的有效信息交换，这被视为一个社会智能系统或集体大脑的处理单元。特别是，我们使用基于代理的模拟来研究一组代理在解决密码算法问题的性能。代理可以执行局部随机移动来探索问题的解决方案空间，也可以模仿模型代理-其影响网络中表现最好的代理。代理的数量N和模仿概率p之间有一个复杂的权衡，为了这些参数之间的最佳平衡，我们观察到与独立搜索相比，找到密码算法问题的解决方案的计算成本减少了30倍。然而，如果这些参数的选择远离最佳设置，那么模仿学习就会大大削弱群体的表现。观察到的适应不良的模仿学习大N提供了一个替代的解释群体规模的社会动物。
英文摘要：
The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent, despite the little quantitative groundwork to support it. Here we consider a primordial form of cooperation -- imitative learning -- that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent -- the best performing agent in its influence network. There is a complex trade-off between the number of agents N and the imitation probability p, and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group. The observed maladaptation of imitative learning for large N offers an alternative explanation for the group size of social animals.
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