Models of adaptive bet-hedging commonly adopt insights from Kelly’s famous work on optimal gambling strategies and the financial value of information. In particular, such models seek evolutionary solutions that maximize long-term average growth rate of lineages, even in the face of highly stochastic growth trajectories. Here, we argue for extensive departures from the standard approach to better account for evolutionary contingencies. Crucially, we incorporate considerations of volatility minimization, motivated by interim extinction risk in finite populations, within a finite time horizon approach to growth maximization. We find that a game-theoretic competitive optimality approach best captures these additional constraints and derive the equilibria solutions under straightforward fitness payoff functions and extinction risks. We show that for both maximal growth and minimal time relative payoffs, the log-optimal strategy is a unique pure strategy symmetric equilibrium, invariant with evolutionary time horizon and robust to low extinction risks.

中文翻译：

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重新审视自适应对冲：风险和时间范围的考虑

自适应投注对冲模型通常采用凯利关于最佳赌博策略和信息的财务价值的著名著作中的见解。特别是，即使面对高度随机的增长轨迹，此类模型也寻求使谱系的长期平均增长率最大化的进化解决方案。在这里，我们主张广泛偏离标准方法以更好地解释进化的偶然性。至关重要的是，我们将波动性最小化的考虑纳入到增长最大化的有限时间范围内，以有限种群中的临时灭绝风险为动机。我们发现博弈论的竞争最优方法最好地捕捉这些额外的约束，并在直接的适应度支付函数和灭绝风险下推导出均衡解。