Urban geosimulations with the Logic Scoring of Preference method for agent-based decision-making

Abstract

The Logic Scoring of Preference (LSP) method is a generalized multicriteria evaluation (MCE) decision making approach with origins in soft computing. The method can model a wide range of aggregators to suit various evaluation objectives that are close to human reasoning. The LSP method can aggregate an unlimited number of inputs without loss of significance. The main objective of this study is to develop and implement an integrated approach using the LSP method to represent the decision-making process of actors influencing urban residential development represented within an agent-based model (ABM). Geospatial data for the Clayton-Cloverdale neighborhood of the City of Surrey, Canada, was used to incorporate the LSP agent-based model to simulate land-use change at the cadastral level. The geosimulations incorporated the decision-making process and interactions of agents as residents, developers, and city planners known as the main stakeholders with separate and sometimes conflicting priorities. The simulation results indicate a higher number of residents tend to choose mid-rise to high-rise buildings over single residential dwellings for a longer period of the time. This can be attributed to the lack affordability and developable land for housing in the future. The LSP method captured different agent decision-making reasoning that is closer to actual human logic which has resulted in the modeling outcomes of urban residential land-use to be in accordance to long term city plans.