Navigating uncertainty: Risk-averse versus risk-prone strategies in populations facing demographic and environmental stochasticity

• DOI: 10.1103/PhysRevE.111.024118
• ArXiv number: 2409.16127

Strategies aimed at reducing the negative effects of long-term uncertainty and risk are common in biology, game theory, and finance, even if they entail a cost in terms of mean benefit. Here, we focus on the single mutant's invasion of a finite resident population subject to fluctuating environmental conditions. Thus, the game-theoretical model we analyze integrates environmental and demographic randomness, i.e., the two leading sources of stochasticity and uncertainty. We use simulations and mathematical analysis to study if and when strategies that either increase or reduce payoff variance across environmental states can enhance the mutant fixation probability. Variance aversion implies that the mutant pays insurance in terms of mean payoff to avoid worst-case scenarios. Variance-prone or gambling strategies, on the other hand, entail specialization, allowing the mutant to capitalize on transient favorable conditions, leading to a series of “boom-and-bust” cycles. Our analyses elucidate how the rate of change of environmental conditions and the shape of the probability distribution of possible states affect the possible most convenient strategies. We discuss how our results relate to the bet-hedging theory, which aims to reduce fitness variance rather than payoff variance. We also describe the analogies and differences between these similar yet distinct approaches.

• Full paper

Additional files

Related research projects

MdM-IFISC-2

Maria de Maeztu 2023-2026

P.I.: Ernesto Estrada, Ingo Fischer, Emilio Hernández-García, Rosa Lopez, Claudio Mirasso, Jose Javier Ramasco, Raúl Toral, Roberta Zambrini

After 15 years of its existence, IFISC can point to a proven track record of impactful research. The previous 2018-2022 MdM award has significantly enhanced the institute's capabilities, as demonstrated by an …

APASOS - CSIC

A Physics approach to sociotechnical systems: from theory to data analysis.

P.I.: Tobias Galla

APASOS objective is to use mathematical and computational methods combined with data and physics thinking to model complex socio-technical systems. APASOS is organized into two workpackages (WP). WP 1 focuses on models …