A constrained multi-agent system compensating for self-lucrative behaviours in water resource sharing

This paper presents the development of a multi-agent model for simulating realistic water-sharing scenarios and a means of studying and assessing an ecosystem's viability by taking under consideration environmental and socio-economical parameters. To account for farmer self-lucrative behaviours, exhibited due to the economic pressure exerted on them, the proposed model imposes constraints such as the lack of inter-farmer communication and observation. Additionally, a self-adaptive learning algorithm is proposed that compensates for such self-lucrative behaviours, despite the imposed constraints. The proposed model was calibrated using data derived from the Lake Koronia ecosystem, and experimental results provided statistical and objective figures of merit for assessing typical irrigation policies under study. As it will be demonstrated, the developed model is a viable means for assessing irrigation policies, and the proposed self-adaptive learning method is a means of guiding behaviours towards the viability of both the resource and its users.

[1]  Panos Alexopoulos Resource sharing in multi-agent systems through multi-stage negotiation , 2007, AIAI.

[2]  François Bousquet,et al.  Multi-agent simulations and ecosystem management: a review , 2004 .

[3]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[4]  Bryan C. Pijanowski,et al.  Exploring complex dynamics in multi agent-based intelligent systems: theoretical and experimental approaches using the multi agent-based behavioral economic landscape (mabel) model , 2006 .

[5]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  M. Janssen,et al.  Learning, Signaling, and Social Preferences in Public-Good Games , 2006 .

[7]  Pericles A. Mitkas,et al.  A Hybrid Agent-Based Model for Estimating Residential Water Demand , 2005, Simul..

[8]  François Bousquet,et al.  SINUSE: a multi-agent model to negotiate water demand management on a free access water table , 2003, Environ. Model. Softw..

[9]  Elinor Ostrom,et al.  Managing Resources in the Global Commons , 2002 .

[10]  J. Anderies,et al.  Governance and the Capacity to Manage Resilience in Regional Social-Ecological Systems , 2006 .

[11]  Michael Monticino,et al.  Coupled human and natural systems: A multi-agent-based approach , 2007, Environ. Model. Softw..

[12]  L. Buşoniu,et al.  A comprehensive survey of multi-agent reinforcement learning , 2011 .

[13]  François Bousquet,et al.  SHADOC: a multi‐agent model to tackle viability of irrigated systems , 2000, Ann. Oper. Res..

[14]  Panagiotis Tzionas,et al.  A Hierarchical Fuzzy Decision Support System for the Environmental Rehabilitation of Lake Koronia, Greece , 2004, Environmental management.

[15]  Thomas Berger,et al.  Research, part of a Special Feature on Empirical based agent-based modeling Creating Agents and Landscapes for Multiagent Systems from Random Samples , 2006 .