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'If you can't be with the one you love, love the one you're with': How individual habituation of agent interactions improves global utility

机译：“如果你不能与你所爱的人在一起，那就爱上你所爱的人”：个体对代理人互动的习惯如何提高全球效用

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Simple distributed strategies that modify the behaviour of selfish individuals in a manner that enhances cooperation or global efficiency have proved difficult to identify. We consider a network of selfish agents who each optimise their individual utilities by coordinating (or anti?coordinating) with their neighbours, to maximise the pay-offs from randomly weighted pair-wise games. In general, agents will opt for the behaviour that is the best compromise (for them) of the many conflicting constraints created by their neighbours, but the attractors of the system as a whole will not maximise total utility. We then consider agents that act as 'creatures of habit' by increasing their preference to coordinate (anti-coordinate) with whichever neighbours they are coordinated (anti?coordinated) with at the present moment. These preferences change slowly while the system is repeatedly perturbed such that it settles to many different local attractors. We find that under these conditions, with each perturbation there is a progressively higher chance of the system settling to a configuration with high total utility. Eventually, only one attractor remains, and that attractor is very likely to maximise (or almost maximise) global utility. This counterintuitive result can be understood using theory from computational neuroscience; we show that this simple form of habituation is equivalent to Hebbian learning, and the improved optimisation of global utility that is observed results from well?known generalisation capabilities of associative memory acting at the network scale. This causes the system of selfish agents, each acting individually but habitually, to collectively identify configurations that maximise total utility.
机译：事实证明，很难以简单的分布式策略来改变自私者的行为，从而增强合作或全球效率。我们考虑一个自私的代理商网络，每个代理商通过与邻居进行协调（或反协调）来优化各自的效用，以最大程度地提高随机加权两两博弈的收益。通常，代理会选择由邻居创建的许多冲突约束中（对他们而言）最佳折衷的行为，但整个系统的吸引者不会使总效用最大化。然后，我们考虑通过增加他们倾向于与当前被协调（反协调）的任何邻居进行协调（反协调）的行为来充当“习惯的创造者”。当系统反复受到干扰时，这些偏好会缓慢变化，从而使其适应许多不同的本地吸引者。我们发现，在这些条件下，每次扰动都会使系统逐渐适应具有较高总效用的配置的机会越来越大。最终，仅剩一个吸引子，并且该吸引子极有可能最大化（或几乎最大化）全球效用。可以使用计算神经科学的理论来理解这种违反直觉的结果。我们表明，这种简单的习惯化形式等同于Hebbian学习，并且所观察到的全局效用的改进是由众所周知的网络规模的关联存储器的泛化能力导致的。这导致自私行为体的系统共同地识别使总效用最大化的配置，每个自私行为体各自独立但习惯地行动。

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