The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.

The ecosystem service concept has emphasized the role of people within socio-ecological systems (SESs). In this paper, we review and discuss alternative ways of representing people, their behaviour and decision-making processes in SES models using an agent-based modelling (ABM) approach. We also explore how ABM can be empirically grounded using information from social survey. The capacity for ABM to be generalized beyond case studies represents a crucial next step in modelling SESs, although this comes with considerable intellectual challenges. We propose the notion of human functional types, as an analogy of plant functional types, to support the expansion (scaling) of ABM to larger areas. The expansion of scope also implies the need to represent institutional agents in SES models in order to account for alternative governance structures and policy feedbacks. Further development in the coupling of human-environment systems would contribute considerably to better application and use of the ecosystem service concept.

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

An agent-based model was developed as a tool designed to explore our understanding of spatial, social, and environmental issues related to land-use/cover change. The model focuses on a study site in a region of the Amazon frontier, characterized by the development of family farms on 100-ha lots arranged along the Transamazon highway and a series of side roads, west of Altamira, Brazil. The model simulates the land-use behaviour of farming households on the basis of a heuristic decisionmaking strategy that utilizes burn quality, subsistence requirements, household characteristics, and soil quality as key factors in the decisionmaking process. Farming households interact through a local labour pool. The effects of the land-use decisions made by households affect the land cover of their plots and ultimately that of the region. This paper describes this model, referred to as LUCITA, and presents preliminary results showing land-cover changes that compare well with observed land-use and land-cover changes in the region.