10 positive and negative effects of heterogeneity in urban setting.
Answers
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.
Agent-based modeling (ABM) is a contemporary approach that can be used to represent agents that are heterogeneous, adaptive, and interactive (Holland and Miller 1991, Hong and Page 2004), important characteristics of complex systems that create tractability problems for analytical models. ABM is distinguished from statistical modeling approaches in its focus on the ways in which macro-scale spatial patterns, e.g., urban settlement patterns, result from processes and behaviors of microscale actors, e.g., households and firms, and by its ability to represent nonlinear interactions (Epstein and Axtell 1996, Axelrod 1997, Gilbert and Troitzsch 1999, Gimblett 2002, Parker et al. 2002, 2003). Though cellular automata (CA) have also been used to represent these dynamics, ABM permits mechanisms to be assigned to objects other than locations on the landscape, whereas CA rules refer to locations (Benenson and Torrens 2004). Agent-based models have been used to provide “proofs of existence” (Waldrop 1990) of spatial patterns resulting from the actions of individual agents with very simple behavioral rules. For example, Parker and Meretsky (2004) and Sasaki and Box (2003) demonstrated how spatial patterns like those described by Von Thünen can result from economically rational agent behaviors, and Schelling (1969, 1978) demonstrated how patterns of residential segregation can result when individuals have only a small preference to be near people like themselves.
Important issues in the use of ABMs are how to appropriately represent the heterogeneity of agents and their environment as software objects in ways that accurately reflect the actual heterogeneity of ”real-world” objects, and what effects heterogeneity has on the outcomes of the models. In individual-based models of ecological systems, the degree of heterogeneity among and within species (Lomnicki and Sedziwy 1989, Uchmanski 2000) has clear effects on the viability and behavior of populations. Many abstract models of land-use dynamics (Sanders et al. 1997, Otter et al. 2001, Cioffi-Revilla and Gotts 2003, Parker et al. 2003, Brown et al. 2004) have demonstrated how complex interactions can give rise to observed land-use patterns without data on actor-level heterogeneity. Models of land-use that use data on agent characteristics have tended to (1) use aggregate data about agents or their environment (e.g., ILUTE, Miller et al. 2004), or (2) use ranges of acceptable values as defined in the literature and randomly assign values within those ranges (e.g., LUCITA, Deadman et al. 2004).
refer more at ECOLOGY & SOCIETY WEBSITE
hope this helps uuuuuuuuuuuuuuuu!!!!!!!!!!!!
With regards,
Spideygeek