The aim of this paper is to study the spatial consequences of applying different Attitude Utility Functions (AUFs), which reflect peoples’ simplified psychological frames, to investment plans in land-use decision making. For this purpose, we considered and implemented an agent-based model with new methods for searching landscapes, for selecting parcels to develop, and for allowing competitions among agents. Besides this, GIS (Geographic Information Systems) as a versatile and powerful medium of analyzing and representing spatial data is used. Our model is implemented on an artificial landscape in which land is being developed by agents. The agents are assumed to be mobile developers that are equipped with several land-related objectives. In this paper, agents mimic various risk-bearing attitudes and sometimes compete for developing the same parcel. The results reveal that patterns of land-use development are different in the two cases of regarding and disregarding AUFs. Therefore, it is considered here that using the attitudes of people towards risk helps the model to better simulate the decision making of land-use developers. The different attitudes toward risk used in this study can be attributed to different categories of developers based on sets of characteristics such as income, age, or education. 1. Introduction Land-Use/Cover Change (LUCC) is one of the most profound human-induced alterations of the earth’s system [1–3]. LUCC is a complex process caused by the interaction between natural and social systems at different spatial scales [4, 5]. The heterogeneity and contiguity of space creates many difficulties in spatial models of residential land-use development. Therefore, there is no simple, uniform way to analyze and explain the dynamics of land-use changes [6]. A group of models have recently emerged and gained popularity in the LUCC scientific community. These models are commonly referred to as agent-based models (ABMs) [7]. These models use the real actors of land-use changes (individuals or institutions) as objects of analysis and simulation and pay explicit attention to the interactions of the “agents” of change [7]. Numerous attempts have been made to define the concept of agents [8, 9]. In this paper, we adopted the definition of Maes [10]: “An agent is a system that tries to fulfill a set of goals in a complex, dynamic environment. An agent is situated in the environment: it can sense the environment through its sensors and act upon the environment using its actuators.” Agents can represent individuals, groups of individuals, and, if
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