Social interactions involving coordination between individuals are subject to an “evolutionary trap.” Once a suboptimal strategy has evolved, mutants playing an alternative strategy are counterselected because they fail to coordinate with the majority. This creates a detrimental situation from which evolution cannot escape, preventing the evolution of efficient collective behaviors. Here, we study this problem using evolutionary robotics simulations. We first confirm the existence of an evolutionary trap in a simple setting. We then, however, reveal that evolution can solve this problem in a more realistic setting where individuals need to coordinate with one another. In this setting, simulated robots evolve an ability to adapt plastically their behavior to one another, as this improves the efficiency of their interaction. This ability has an unintended evolutionary consequence: a genetic mutation affecting one individual's behavior also indirectly alters their partner's behavior because the two individuals influence one another. As a consequence of this indirect genetic effect, pairs of partners can virtually change strategy together with a single mutation, and the evolutionary barrier between alternative strategies disappears. This finding reveals a general principle that could play a role in nature to smoothen the transition to efficient collective behaviors in all games with multiple equilibriums.

中文翻译：

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间接遗传效应可以避免协调游戏中低效的平衡

涉及个人之间协调的社会互动会受到“进化陷阱”的约束。一旦制定了次优策略，就会因为无法与大多数协调而重新选择扮演替代策略的突变体。这造成了不利的局面，进化无法从中逃脱，从而阻碍了有效集体行为的发展。在这里，我们使用进化机器人仿真来研究这个问题。我们首先确认在简单环境中存在进化陷阱。但是，我们然后发现，进化可以在更现实的环境中解决此问题，在这种环境中，个人需要相互协调。在这种情况下，模拟机器人发展了一种能力，可塑性地彼此适应，因为这提高了交互的效率。这种能力具有意想不到的进化结果：影响一个人行为的基因突变也间接改变了其伴侣的行为，因为两个人彼此影响。这种间接遗传效应的结果是，伴侣对可以虚拟地改变策略以及单个突变，并且替代策略之间的进化障碍消失了。这一发现揭示了一条通用原则，该原则可以在自然界中发挥作用，以平滑所有具有多重均衡的博弈中向有效集体行为的过渡。一对伙伴可以虚拟改变一个突变的策略，替代策略之间的进化障碍就消失了。这一发现揭示了一条通用原则，该原则可以在自然界中发挥作用，以平滑所有具有多重均衡的博弈中向有效集体行为的过渡。一对伙伴可以虚拟改变一个突变的策略，替代策略之间的进化障碍就消失了。这一发现揭示了一条通用原则，该原则可以在自然界中发挥作用，以平滑所有具有多重均衡的博弈中向有效集体行为的过渡。