Multi-agent system (MAS) models have been increasingly applied to the simulation of complex phenomena in different areas, providing successful and credible results. Citizens behavior related to a specific urban activity (i.e., recreation activities in a park, using bicycle for mobility purposes) can be modeled as an agent (actor) with several affinities and preferences which are dependent on aspects that affect the activity. A particular application of a MAS approach is in area of urban policy design, in which policies should be designed considering citizens needs, preferences and behavior. Once an open space in a city is available (i.e., an industry is moved to an industrial area), a land use policy should contribute to identify the new use for the urban space. There are different land use policies that can be applied depending on which services or facilities must be empowered in the city. It is important to identify the correct policy in order to satisfy present citizens needs but considering also the future needs in a social changing context. A socio-technological simulation model has been developed to allow citizens to get a better understanding of the urban problem, its dynamics and explore the sustainability of the different solutions., enhancing citizens to participate in the urban decisions through new technologies (i.e., e-participation). This paper illustrates an open space MAS simulation model for land use design policies in which citizens can check their opinion and get a better understanding of the different choices and its acceptability by the community considering not only present neighborhood profiles, but also future neighborhood configurations. It is the first step before the development of the final software including a user friendly interface to let citizens with different cultural profiles to perform simulations as an essential and neutral tool to reach consensus during the decision-making process in urban policy design.
Simulating agricultural land-use adaptation decisions under changing climate using multi-agent system model in the Upper East Region of Ghana
