Modeling agents and interactions in agricultural economics

When multiagent simulations are used for consensus building among stakeholders, it is important not only that the domain experts can deeply understand stakeholders' actual behavior but also that the stakeholders can feel the simulation result as their solution. To this end, we propose a modeling methodology which combines several techniques with the participatory method which takes stakeholders into the modeling process using role playing games (RPG). There are two types of model required to simulate a social system as a multiagent system: agents (internal models) and interactions. Hence, we considered a modeling method according to each character. In modeling an agent (e.g. decision making) which is implicit in human, the identification of the model greatly depends on the modeler's ability. Therefore we propose a modeling method wherein classification learning creates an alternative model from RPG log data for validating the domain experts' hypothesis. On the other hand, in modeling interactions (e.g. negotiation) which are emerged outside of human, it is rather important to show and capture continuously appeared interactions. Therefore we propose a modeling method with participatory simulation where a stakeholder participates as an avatar and agents act as the other stakeholders in order to deeply understand the stakeholders' interactions. Our methodology was effective to give the domain experts a deeper understanding through a real case study of agricultural economics in the northeast of Thailand [17].