1.2.6 Agent-Based Models
Agent-based models (ABM), or the multi-agent system models (MAS), are developed based upon the assumption that “agent” is the major driver of a system (e.g.,
Parker et al. 2003; Batty 2005; Torrens and Benenson 2005; Xie et al. 2007). ABMs
are similar to CA models which are both spatial transition models built on a bottomup perspective for the simulation of emergent properties of complex adaptive
systems (Couclelis 2001). The three primary components of an ABM are the agents,
landscape and their interactions. Within the modeling structure, the agents can
interact with each other as well as the environment across multiple scales. Agents
could employ high degree of rationality and information-processing ability in
decision making which will influence the behavior of the systems (Miller and
Page 2007). A number of ABMs apply the utility function to represent agents’
decision-making on location choices (e.g., Brown and Robinson 2006; Xie
et al. 2007; Ligmann-Zielinska 2009). Usually, an agent will select a location that
can maximize the utility or profit. Although traditional ABM is built on the bottomup perspective, researchers in geographic and ecological modeling have proposed
that ABM should not be restricted to the bottom-up simulation (Xie et al. 2007). In
the paper by Xie et al. (2007), the author considers both macro level and micro level
spatiotemporal urban dynamics. The macro level model is based on a stepwise
regression model to project the aggregated rate of change at township level. The
micro level model is to allocate the changes at the cellular level. The interaction
among the two levels is also modeled through incorporating township competition
in the utility function.
The structure of ABM is promising for land change research in that it explicitly
represents human-nature interactions and feedbacks which are essential components for simulating land changes as coupled human-environmental systems. However, given its complexity in model design and implementation, much effort needs
to be done to examine its operability for simulating real world processes and to fully
realize the potential of ABM. Moreover, the advancement of ABM is challenged by
the lack of detailed data to represent and validate complex human decision-making
processes and interactions among agents at the micro level.
1.3 Major Issues in Land Change Modeling
The usefulness and complexity of land change modeling lie in the necessity to treat
land changes as coupled human-environmental systems with complex interactions
and feedbacks at multiple spatiotemporal scales (Turner et al. 2007). This section
discusses several important theoretical and methodological issues in land change
modeling: (i) coupling of human decision-making and environmental conditions,
(ii) scale dependency and multilevel interactions, and (iii) temporal dynamics and
complexity. These proposed issues are important for developing a comprehensive
1 Land Change Modeling: Status and Challenges
9
Agent-based models (ABM), or the multi-agent system models (MAS), are developed based upon the assumption that “agent” is the major driver of a system (e.g.,
Parker et al. 2003; Batty 2005; Torrens and Benenson 2005; Xie et al. 2007). ABMs
are similar to CA models which are both spatial transition models built on a bottomup perspective for the simulation of emergent properties of complex adaptive
systems (Couclelis 2001). The three primary components of an ABM are the agents,
landscape and their interactions. Within the modeling structure, the agents can
interact with each other as well as the environment across multiple scales. Agents
could employ high degree of rationality and information-processing ability in
decision making which will influence the behavior of the systems (Miller and
Page 2007). A number of ABMs apply the utility function to represent agents’
decision-making on location choices (e.g., Brown and Robinson 2006; Xie
et al. 2007; Ligmann-Zielinska 2009). Usually, an agent will select a location that
can maximize the utility or profit. Although traditional ABM is built on the bottomup perspective, researchers in geographic and ecological modeling have proposed
that ABM should not be restricted to the bottom-up simulation (Xie et al. 2007). In
the paper by Xie et al. (2007), the author considers both macro level and micro level
spatiotemporal urban dynamics. The macro level model is based on a stepwise
regression model to project the aggregated rate of change at township level. The
micro level model is to allocate the changes at the cellular level. The interaction
among the two levels is also modeled through incorporating township competition
in the utility function.
The structure of ABM is promising for land change research in that it explicitly
represents human-nature interactions and feedbacks which are essential components for simulating land changes as coupled human-environmental systems. However, given its complexity in model design and implementation, much effort needs
to be done to examine its operability for simulating real world processes and to fully
realize the potential of ABM. Moreover, the advancement of ABM is challenged by
the lack of detailed data to represent and validate complex human decision-making
processes and interactions among agents at the micro level.
1.3 Major Issues in Land Change Modeling
The usefulness and complexity of land change modeling lie in the necessity to treat
land changes as coupled human-environmental systems with complex interactions
and feedbacks at multiple spatiotemporal scales (Turner et al. 2007). This section
discusses several important theoretical and methodological issues in land change
modeling: (i) coupling of human decision-making and environmental conditions,
(ii) scale dependency and multilevel interactions, and (iii) temporal dynamics and
complexity. These proposed issues are important for developing a comprehensive
1 Land Change Modeling: Status and Challenges
9