Tuning Agent-Based Predator-Prey Models Toward Lotka-Volterra Dynamics
2026-06-11 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors explore how to make a computer simulation of wolves and sheep behave like classic predator-prey cycles, where populations rise and fall in a stable pattern. They use agents with simple brain-like controllers and try changing environmental settings to see if the populations keep cycling naturally without crashing or exploding. To do this, they optimize the parameters to keep populations balanced and oscillating for a long time. They run these simulations efficiently using a special software framework called ABMax that works well with powerful hardware.
agent-based modelpredator-prey systemLotka-Volterra cyclespopulation dynamicsrecurrent neural networkparameter optimizationenvironmental parametersdemographic parameterssimulationsABMax
Authors
Corinna Mandl, Siddharth Chaturvedi, Marcel van Gerven
Abstract
Recent growth in compute power has made it increasingly feasible to use large-scale agent-based models to simulate complex adaptive systems. A central difficulty is that such models contain many local rules and parameters, where small changes can lead to runaway behaviour, population collapse, or saturation at artificial bounds. We study this problem in a continuous predator-prey system where sheep and wolves are active agents with local sensing, internal energy, and recurrent neural network-based controllers. We ask whether environmental and demographic parameters can be tuned so that the resulting population dynamics resemble classical Lotka-Volterra cycles. We optimise these parameters with a feature-based loss that rewards sustained oscillations, phase lag, bounded populations, and long-term persistence, first for random controllers and then for evolved controllers in a more naturalistic setting. The model is implemented in ABMax, a JAX-based agent-based modelling framework that enables efficient batched simulation on hardware accelerators.