Multiagent reinforcement learning model for the emergence of common property and transhumance in Sub-Saharan Africa

Abstract

We consider social phenomena as challenges and measures for learning in multi-agent scenarios for the following reasons: (i) social phenomena emerge through complex learning processes of groups of people, (ii) a model of a phenomenon sheds light onto the strengths and weaknesses of the learning algorithm in the context of the model environment. In this paper we use tabular reinforcement learning to model the emergence of common property and transhumance in Sub-Saharan Africa. We find that the Markovian assumption is sufficient for the emergence of property sharing, when (a) the availability of resources fluctuates (b) the agents try to maximize their resource intake independently and (c) all agents learn simultaneously. © 2010 Springer.