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Complementary Variety: When Can Cooperation in Uncertain Environments Outperform Competitive Selection?

机译:互补品种:不确定环境下的合作何时能胜过竞争选择?

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Evolving biological and socioeconomic populations can sometimes increase their growth rate by cooperatively redistributing resources among their members. In unchanging environments, this simply comes down to reallocating resources to fitter types. In uncertain and fluctuating environments, cooperation cannot always outperform blind competitive selection. When can it? The conditions depend on the particular shape of the fitness landscape. The article derives a single measure that quantifies by how much an intervention in stochastic environments can possibly outperform the blind forces of natural selection. It is a multivariate and multilevel measure that essentially quantifies the amount of complementary variety between different population types and environmental states. The more complementary the fitness of types in different environmental states, the proportionally larger the potential benefit of strategic cooperation over competitive selection. With complementary variety, holding population shares constant will always outperform natural and market selection (including bet-hedging, portfolio management, and stochastic switching). The result can be used both to determine the acceptable cost of learning the details of a fitness landscape and to design multilevel classification systems of population types and environmental states that maximize population growth. Two empirical cases are explored, one from the evolving economy and the other one from migrating birds.
机译:不断发展的生物和社会经济人口有时可以通过在其成员之间合作重新分配资源来提高其增长率。在不变的环境中,这只是归因于将资源重新分配给装配工类型。在不确定和多变的环境中,合作不能总是胜过盲目的竞争选择。什么时候可以条件取决于健身景观的特定形状。这篇文章得出了一个单一的量度,该量度可以量化随机环境中的干预有可能胜过自然选择的盲目作用。它是一个多变量和多层次的度量,可以从本质上量化不同人口类型与环境状况之间的互补变化量。类型在不同环境状态下的适应性越互补,战略合作相对于竞争选择的潜在收益就成比例地越大。通过互补的品种,保持不变的人口份额将始终优于自然选择和市场选择(包括对冲,投资组合管理和随机转换)。结果可用于确定学习健身景观细节的可接受成本,还可用于设计人口类型和环境状态的最大化人口增长的多级分类系统。探索了两个经验案例,一个来自不断发展的经济,另一个来自迁徙的鸟类。

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