An intelligent web-based spatial group decision support system to investigate the role of the opponents' modeling in urban land use planning. (September 2022) - Ghavami, Seyed Morsal; Taleai, Mohammad; Arentze, Theo
Abstract: Urban land-use planning decisions generally require negotiation between multiple stakeholders to reach an agreement on a specific plan. Computer-aided tools such as group decision support systems can facilitate the actors in this complicated process. In the context of these systems, using software agents enhance the effectiveness and efficiency of group decision support. The software agents can perform some computational and analytical tasks on behalf of the stakeholders. In more advanced cases, the agents can also learn stakeholders' preferences and behavior to help them make good decisions. This paper proposes an intelligent web-based spatial group decision support system to investigate the role of opponents modeling in urban land use planning by using a multi-agent system approach. For this purpose, two successive meetings are held in which the system is used: in the first meeting, the stakeholders revise the existing plans and respond to other stakeholders' requests. During the meeting, software agents attempt to model the behavior of the stakeholders they are associated with, based on a Bayesian learning method in combination with social value orientation theory to describe stakeholders' decision behavior in a group context. In the second meeting, the software agents help the stakeholders in the step of plan revision by providing the information obtained to the stakeholders. In an application, a comparison of the results of the meetings showed that the provided information about the opponents reduced the negotiation time and contributed to reaching a better spatial configuration of land-uses based on a criterion provided by social value orientation theory. Highlights: It provides a web-based intelligent Group Decision Support System for land use planning by using multi-agent systems. It investigates the influence of providing information about the other participants on the results of a group meeting. It utilizes Social Value Orientation (SVO) theory and Bayesian learning to model the participating stakeholders.