Dawn C. Parker

Multi-agent systems for the simulation of land-use and land-cover change: A review

Abstract This article presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decision making, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

Comparison of empirical methods for building agent-based models in land use science

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.

Spatial agent-based models for socio-ecological systems

Departing from the comprehensive reviews carried out in the field, we identify the key challenges that agent-based methodology faces when modeling coupled socio-ecological systems. Focusing primarily on the papers presented in this thematic issue, we review progress in spatial agent-based models along the lines of four methodological challenges: (1) design and parameterizing of agent decision models, (2) verification, validation and sensitivity analysis, (3) integration of socio-demographic, ecological, and biophysical models, and (4) spatial representation. Based on this we critically reflect on the future work that is required to make agent-based modeling widely accepted as a tool to support the real world policy. Progress of agent-based methodology in modeling coupled socio-ecological systems.Key methodological challenges for ABM.Societal issues and critical reflection on the prospects of ABM.

A conceptual design for a bilateral agent-based land market with heterogeneous economic agents

This paper presents a conceptual design for an agent-based bilateral residential land market. The design includes interactions between multiple buyers and sellers (household agents, developers, and rural land owners) and two local feedbacks to land value—price expectation formation based on local neighborhoods and spatial externalities. To address the methodological challenges inherent in the transition from equilibrium-based analytical models to agent-based simulation, we combine traditional deductive optimization models of behavior at the agent level with inductive models of price expectation formation. Relative to previous models, our proposed model is more closely linked to urban economics; contains a wider range of drivers of land use (LU); and addresses alternative models of division of gains from trade and determination of transaction prices, including models of bid and ask price formation. Our proposed approach is also closely linked to geographic cellular LU models, potentially uniting the strengths of these two disciplinary perspectives.

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

Case studies, cross-site comparisons, and the challenge of generalization: Comparing agent-based models of land-use change in frontier regions

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.

Effects of land markets and land management on ecosystem function: A framework for modelling exurban land-change

This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux. To demonstrate the framework, we implement a simple integration of a new agent-based model of exurban residential development and land-management decisions with the ecosystem process model BIOME-BGC. Using a stylized scenario, we evaluate the influence of different exurban residential-land-management strategies on carbon storage at the parcel level over a 48-year period from 1958 to 2005, simulating stocks of carbon in soil, litter, vegetation, and net primary productivity. Results show 1) residential parcels with management practices that only provided additions in the form of fertilizer and irrigation to turfgrass stored slightly more carbon than parcels that did not include management practices, 2) conducting no land-management strategy stored more carbon than implementing a strategy that included removals in the form of removing coarse woody debris from dense tree cover and litter from turfgrass, and 3) the removal practices modelled had a larger impact on total parcel carbon storage than our modelled additions. The degree of variation within the evaluated land-management practices was approximately 42,104?kg?C storage on a 1.62?ha plot after 48 years, demonstrating the substantial effect that residential land-management practices can have on carbon storage. Highlights? The new framework integrates agent-based and ecosystem models to link management and the C cycle. ? Fertilization and irrigation of residential turfgrass stored more carbon than no management. ? No management stored more carbon than managements removing woody debris and grass clippings. ? Removal practices had a larger impact on total parcel carbon storage than modeled additions. ? Residential land-management practices can have a substantial effect on carbon storage.

Agent-Based Modeling Simulation of Social Adaptation and Long-Term Change in Inner Asia

We present a new international project to develop temporally and spatially calibrated agent-based models of the rise and fall of polities in Inner Asia (Central Eurasia) in the past 5,000 years. Gaps in theory, data, and computational models for explaining long-term sociopolitical change—both growth and decay—motivate this project. We expect three contributions: (1) new theoreticallygrounded simulation models validated and calibrated by the best available data; (2) a new long-term cross-cultural database with several data sets; and (3) new conceptual, theoretical, and methodological contributions for understanding social complexity and long-term change and adaptation in real and artificial societies. Our theoretical framework is based on explaining sociopolitical evolution by the process of “canonical variation”.

A Qualitative Study of the Impact of HIV/AIDS on Agricultural Households in Southeastern Uganda

The HIV/AIDS pandemic threatens economic, social, and environmental sustainability throughout sub-Saharan Africa. This paper reports on a qualitative study exploring interrelationships between HIV/AIDS, labor availability, agricultural productivity, household resources, food consumption, and health status in rural southeastern Uganda. Respondents reported an increase in widow-and-orphan-headed households; labor shortages due to illness and caretaking; degradation of household resources from health-related expenses; loss of land tenure and assets following deaths, especially for widows and orphans; and changes in agricultural practices and productivity. Our study highlights a potential downward spiral of livelihood degradation for vulnerable households and suggests targeted interventions to improve sustainability.

Do Land Markets Matter? A Modeling Ontology and Experimental Design to Test the Effects of Land Markets for an Agent-Based Model of Ex-Urban Residential Land-Use Change

Urban sprawl is shaped by various geographical, ecological and social factors under the influence of land market forces. When modeling this process, geographers and economists tend to prioritize factors most relevant to their own domain. Still, there are very few structured systematic comparisons exploring how the extent of process representation affects the models’ ability to generate extent and pattern of change. This chapter aims to explore the question of how the degree of representation of land market processes affects simulated spatial outcomes. We identify four distinct elements of land markets: resource constraints, competitive bidding, strategic behavior, and endogenous supply decisions. Many land-use-change models include one or more of these elements; thus, the progression that we designed should facilitate analysis of our results in relation to a broad range of existing land-use-change models, from purely geographic to purely economic and from reduced form to highly structural models. The description of the new agent-based model, in which each of the four levels of market representation can be gradually activated, is presented. The behavior of suppliers and acquirers of land, and the agents’ interactions at land exchange are discussed in the presence of each of the four land-market mechanisms.