Complexity Science Hub, Josefstædter Strasse 39, Vienna 1080, Austria.
Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
Proc Biol Sci. 2024 Oct;291(2033):20241606. doi: 10.1098/rspb.2024.1606. Epub 2024 Oct 30.
Organisms can learn in response to environmental inputs as well as actively modify their environments through niche construction on slower evolutionary time scales. How quickly should an organism respond to a changing environment, and when possible, should organisms adjust the time scale of environmental change? We formulate these questions using a model of learning costs that considers optimal time scales of both memory and environment. We derive a general, sublinear scaling law for optimal memory as a function of environmental persistence. This encapsulates a trade-off between remembering and forgetting. We place learning strategies within a niche construction dynamics in a game theoretic setting. Niche construction is found to reduce or stabilize environmental volatility when learned environmental resources can be monopolized. When learned resources are shared, niche destructors evolve to degrade the shared environment. We integrate these results into a metabolic scaling framework in order to derive learning strategies as a function of body size.
生物可以通过环境输入进行学习,也可以通过在较慢的进化时间尺度上的生态位构建来主动改变环境。生物应该多快对环境变化做出反应,以及在可能的情况下,生物应该调整环境变化的时间尺度吗?我们使用考虑记忆和环境时间尺度的最优性的学习成本模型来提出这些问题。我们推导出了一个作为环境持续性函数的最优记忆的亚线性标度定律。这包含了记忆和遗忘之间的权衡。我们在博弈论的情境下,将学习策略置于生态位构建动力学中。当可垄断的学习环境资源存在时,生态位构建被发现可以减少或稳定环境波动性。当学习资源被共享时,生态位破坏者会进化以破坏共享环境。我们将这些结果整合到代谢尺度框架中,以便推导出作为身体大小函数的学习策略。