Multiagent reinforcement learning model for the emergence of common property and transhumance in sub-saharan africa

Authors:
Balázs Pintér;Ákos Bontovics;András Lőrincz
Affiliations:
Eötvös Loránd University, Budapest, Hungary;Eötvös Loránd University, Budapest, Hungary;Eötvös Loránd University, Budapest, Hungary
Venue:
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Year:
2009

Citing 9
Cited 0

Growing artificial societies: social science from the bottom up

Growing artificial societies: social science from the bottom up
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

Machine Learning
Be there then: a modeling approach to settlement determinants and spatial efficiency among late ancestral pueblo populations of the Mesa Verde region, U.S. southwest

Dynamics in human and primate societies
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Introduction to Multiagent Systems

Introduction to Multiagent Systems
The Wealth of Nations

The Wealth of Nations
Factored value iteration converges

Acta Cybernetica
Factored temporal difference learning in the new ties environment

Acta Cybernetica
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.