How to Forage for a Mate?

Abstract

Foraging is a central decision-making behavior performed by all animals, essential to garnishing enough energy for an organism to survive. Similarly, mating is crucial for evolutionary continuity and offspring production. Mate choice is one of the central tenets of sexual selection, driving major evolutionary processes, and can be regarded as a decision-making process between potential mating partners. Often researchers have used coarse-grained models to describe macroscopic phenomenology pertaining to mate choice without detailed quantitative mechanisms of how animals use individual and environmental signals to guide their mating decisions. In this letter, we show that mate choice can be cast as a foraging problem, and we present an analytically tractable optimal foraging-inspired mechanistic theory of decision-making underlying mate choice. We begin from the premise that deciding upon which partner with which to mate is at its core a stochastic decision-making process. Agents adopt a variety of decision strategies, tuned by decision thresholds for leaving or committing to a mate. We find that sensitive leaving thresholds are favored independently of signal availability in the population. By contrast, optimal thresholds for committing to a mate depend upon signal availability in the population, with signal-rich populations generally favoring less eager strategies compared to signal-poor populations.

Figures (4)

• Figure 1: Schematic of mate choice model. The agent must choose between its current partner and the population (one-mate case, upper panel) or between its current partner and another potential mate (two-mate case, lower panel).
• Figure 2: Emergence of different strategies in the one - mate case, with differing average reward relative to population baseline ($\Delta R(\vec{\theta}) = V(\vec{\theta})-\langle R(\lambda)\rangle$, with $R(\lambda)= \frac{1}{\beta}\tanh(\beta\lambda)$). There is an optimal balanced strategy which performs distinctly better than the increasingly suboptimal eager, patient, and reserved strategies, which respectively correspond to excessively low staying thresholds, excessively high leaving thresholds, and excessively high staying thresholds. As information becomes less available ($\beta \sim 10$), the eager and patient strategies become equivalently suboptimal, and the balanced strategy becomes the only strategy to perform above random chance. Behavioral strategies are abbreviated as follows: Reserved (R), Balanced (B), Eager (E) and Patient (B) strategies
• Figure 3: Emergence of different strategies in the two - mate case. Strategies change with information rate in a similar manner to the one-mate case, with the notable exception that the region of optimal balanced strategy shifts in the intermediate information ($\beta$ = 1) case. Behavioral strategies are abbreviated as follows: Reserved (R), Balanced (B), Eager (E) and Patient (B) strategies
• Figure 4: Emergent scaling relationship. Power laws between $\Delta R$ and $\beta$, set by $\theta_\ell$. For small values of $\log(-\theta_\ell)$, we observe a power-law exponent $\alpha \approx-0.9$, consistent with our analytical estimation of a $\frac{1}{\beta}$ scaling.