Subekshya Bidari, Department of Applied Mathematics, University of Colorado Boulder
Evidence accumulation models of social foraging
Foraging is often modeled as a sequence of patch-leaving decisions. The distribution of food in the environment is idealized as being contained in discrete patches (e.g., trees), and animals must decide when to depart one patch to go forage at another. To account for the learning processes involved in determining resource availability within and across patches, we model foraging as an evidence accumulation process. These models associate evidence for leaving a patch with a deterministic drift term and the stochasticity of food encounters and memory with diffusive noise. Thus, the foraging decisions within a patch can be modeled as a drift diffusion process in which decisions are triggered when the process crosses a threshold.
I extend these individual evidence accumulation models to consider patch foraging decisions of multi-agent systems sharing information. Agents’ beliefs are tethered together via different types of coupling: diffusive coupling continuously pulls one forager’s belief about patch quality toward its neighbors whereas pulsatile coupling instantaneously updates a forager’s belief when another departs the patch. Using the patch residence time distribution for individuals and groups, we compare different forms of coupling to get insights into forms of information sharing that are most effective. Using data from capuchin and spider monkeys foraging in groups, we will parametrize our foraging drift diffusion models using Bayesian inference techniques to get insights into the most likely strategies animals use to make patch-leaving decisions in spatially cohesive groups.