Dynamic analysis of cell interactions in biological environments under multiagent social learning framework

Abstract

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area are of great significance and can provide valuable insights to the understanding of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we consider a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), to model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. It can be used to predict the convergence of the system. At last, we experimentally verify the predictive power of our model using a number of representative games.