Incorporating Bayesian learning in agent-based simulation of stakeholders' negotiation

abstract This paper describes the incorporation of a Bayesian learning algorithm into an agent-based modeldesigned to simulate stakeholders’ negotiation when evaluating scenarios of land development. Theobjective is to facilitate reaching an agreement at an earlier stage in the negotiation by providing theopportunity to the proposer agent to learn his opponents’ preferences. The modeling approach is testedin the Elbow River watershed, in southern Alberta, Canada, that is under considerable pressure for landdevelopment due to the proximity of the fast growing city of Calgary. Five agents are included in themodel respectively referred to as the Developer agent, the Planner agent, the Citizen agent, the Agriculture-Concerned agent, and the WaterConcerned agent. Two types of land development scenarios are evaluated;in the first case, only the geographical location is considered while in the second case, the internal land-use composition is also varied. The Developer agent that is equipped with the Bayesian learning capabilityattempts to approximate its opponents’ fuzzy evaluation functions based on the responses he receivesfrom them at each round of the negotiation. The results indicate that using this approach, an agreementcan be reached in fewer number of negotiation rounds than in the case where the Developer agent selectsthe subsequent offers based merely on its own utility. The model also indicates how the satisfaction ofeach agent evolves during the negotiation. This information is very useful for decision makers who wishto consider stakeholders’ perspectives when dealing with multiple objectives in a spatial context. 2014 Elsevier Ltd. All rights reserved.