Effects of Heterogeneity in Residential Preferences on an Agent-Based Model of Urban Sprawl

By Daniel G. Brown, D. Robinson. 2006

240 citations recorded by the source corpus.

The ability of agent-based models (ABMs) to represent heterogeneity in the characteristics and behaviors of actors enables analyses about the implications of this heterogeneity for system behavior. The importance of heterogeneity in the specification of ABMs, however, creates new demands for empirical support. An earlier analysis of a survey of residential preferences within southeastern Michigan revealed seven groups of residents with similar preferences on similar characteristics of location. In this paper, we present an ABM that represents the process of residential development within an urban system and run it for a hypothetical pattern of environmental variation. Residential locations are selected by residential agents, who evaluate locations on the basis of preference for nearness to urban services, including jobs, aesthetic quality of the landscape, and their similarity to their neighbors. We populate our ABM with a population of residential preferences drawn from the survey results in five different ways: (1) preferences drawn at random; (2) equal preferences based on the mean from the entire survey sample; (3) preferences drawn from a single distribution, whose mean and standard deviation are derived from the survey sample; (4) equal preferences within each of seven groups, based on the group means; and (5) preferences drawn from distributions for each of seven groups, defined by group means and standard deviations. Model sensitivity analysis, based on multiple runs of our model under each case, revealed that adding heterogeneity to agents has a significant effect on model outcomes, measured by aggregate patterns of development sprawl and clustering.

Subfields: Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/51ad259888cde098b2d81b65ed9a609482933be0

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In Global and Planetary Change

Citations in the corpus

NEW DIRECTIONS FOR URBAN ECONOMIC MODELS OF LAND USE CHANGE: INCORPORATING SPATIAL DYNAMICS AND HETEROGENEITY (2010, unknown): “Likewise, Brown and Robinson (2006) model agent decision making within a utility maximizing framework, but choices are unconstrained by land prices since they are omitted from the model.”
The Complexities of Agent-Based Modeling Output Analysis (2015, background): “…2010), economics (Kirman 1992; Tesfatsion & Judd 2006), health care (Effken et al. 2012), sociology (Macy & Willer 2002; Squazzoni 2012), geography (Brown & Robinson 2006), anthropology (Axelrod & Hammond 2003), archaeology (Axtell et al. 2002), bio-terrorism (Carley et al. 2006), business (North…”
Accommodating Spatial Correlation Across Choice Alternatives in Discrete Choice Models: Application to Modeling Residential Location Choice Behavior (2011, background): “, 2007), ecology (Brown and Robinson, 2006), human genetics (Whitfield et al.”
A Third Wave in the Economics of Climate Change (2015, background): “2006; Epstein 2009), urban planning (Brown and Robinson 2006; Ligtenberg et al. 2001; Batty 2009), electricity markets (Weidlich and Veit 2008; Sun and Tesfatsion 2007), emissions trading markets (Zhang et al.”
Large language models empowered agent-based modeling and simulation: a survey and perspectives (2023, unknown): “Prompt engineering: When simulating speci ﬁ c agents, we can provide task instructions, background knowledge, generation patterns, and task examples speci ﬁ c to certain domains or scenarios, thereby aligning LLMs ’ output with human knowledge and values when deployed.”
Comparison of empirical methods for building agent-based models in land use science (2007, methodology): “One uses Monte Carlo techniques to generate heterogeneous populations of unique agents (e.g. Berger and Schreinemachers 2006, Brown and Robinson 2006).”
Modeling human decisions in coupled human and natural systems: Review of agent-based models (2012, background): “Examples include the 281 land use agents who choose sites for various land use purposes (Brown and Robinson, 2006; Brown et al., 282 2005), and land buyers in a coastal township who search for the location that maximizes their utility 284 function constrained by their budget (Filatova et al., 2011).”