Steven M. Manson

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.

Simplifying complexity: a review of complexity theory

Abstract Complexity theory has captured the attention of the scientific community to the extent where its proponents tout it as a dominant scientific trend. Geographers, and environmental, human, and regional planners have applied complexity theory to topics ranging from cultural transmission and economic growth to the braiding of rivers. While such a wide array of applications is heartening because it speaks to the utility of complexity theory, it is necessary to move beyond the hyperbole and critically examine the nature of complexity research. The author therefore provides an overview of the evolution of complexity research, establishes a preliminary typology of complexity approaches with their advantages and drawbacks, and identifies areas of further research.

Deforestation in the southern Yucatan peninsular region: an integrative approach

Abstract The tensions between development and preservation of tropical forests heighten the need for integrated assessments of deforestation processes and for models that address the fine-tuned location of change. As Mexico’s last tropical forest frontier, the southern Yucatan peninsular region witnesses these tensions, giving rise to a “hot spot” of tropical deforestation. These forests register the imprint of ancient Maya uses and selective logging in the recent past, but significant modern conversion of them for agriculture began in the 1960s. Subsequently, as much as 10% of the region’s forests have been disturbed anthropogenically. The precise rates of conversion and length of successional growth in both upland and wetland forests are tied to policy and political economic conditions. Pressures on upland forests are exacerbated by the development of infrastructure for El Mundo Maya, an archaeological and ecological activity predicated on forest maintenance, and by increased subsistence and market cultivation, including lands on the edge of Mexico’s largest tropical forest biosphere reserve. In this complex setting, the southern Yucatan peninsular region project seeks to unite research in the ecological, social, and remote sensing sciences to provide a firm understanding of the dynamics of deforestation and to work towards spatially explicit assessments and models that can be used to monitor and project forest change under different assumptions.

Projecting Global Land-Use Change and Its Effect on Ecosystem Service Provision and Biodiversity with Simple Models

Background As the global human population grows and its consumption patterns change, additional land will be needed for living space and agricultural production. A critical question facing global society is how to meet growing human demands for living space, food, fuel, and other materials while sustaining ecosystem services and biodiversity [1]. Methodology/Principal Findings We spatially allocate two scenarios of 2000 to 2015 global areal change in urban land and cropland at the grid cell-level and measure the impact of this change on the provision of ecosystem services and biodiversity. The models and techniques used to spatially allocate land-use/land-cover (LULC) change and evaluate its impact on ecosystems are relatively simple and transparent [2]. The difference in the magnitude and pattern of cropland expansion across the two scenarios engenders different tradeoffs among crop production, provision of species habitat, and other important ecosystem services such as biomass carbon storage. For example, in one scenario, 5.2 grams of carbon stored in biomass is released for every additional calorie of crop produced across the globe; under the other scenario this tradeoff rate is 13.7. By comparing scenarios and their impacts we can begin to identify the global pattern of cropland and irrigation development that is significant enough to meet future food needs but has less of an impact on ecosystem service and habitat provision. Conclusions/Significance Urban area and croplands will expand in the future to meet human needs for living space, livelihoods, and food. In order to jointly provide desired levels of urban land, food production, and ecosystem service and species habitat provision the global society will have to become much more strategic in its allocation of intensively managed land uses. Here we illustrate a method for quickly and transparently evaluating the performance of potential global futures.

Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States.

We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatán of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general.

Complexity Theory in the Study of Space and Place

Researchers across disciplines apply complexity theory to issues ranging from economic development to earthquake prediction. The breadth of applications speaks to the promise of complexity theory, but there remain a number of challenges to be met, particularly those related to its ontological and epistemological dimensions. We identify a number of key issues by asking three questions. Does complexity theory operate at too general a level to enhance understanding? What are the ontological and epistemological implications of complexity? What are the challenges in modeling complexity? In answering these questions, we argue that, although complexity offers much to the study of place and space, research in these areas has a number of strengths that enhance complexity research.

Complex systems models and the management of error and uncertainty

For the complex systems modeller, uncertainty is ever-present. While uncertainty cannot be eliminated, we suggest that formally incorporating an assessment of uncertainty into our models can provide great benefits. Sources of uncertainty arise from the model itself, theoretical flaws, design flaws, and logical errors. Management of uncertainty and error in complex systems models calls for a structure for uncertainty identification and a clarification of terminology. In this paper, we define complex systems and place complex systems models into a common typology leading to the introduction of complex systems specific issues of error and uncertainty. We provide examples of complex system models of land use change with foci on errors and uncertainty and finally discuss the role of data in building complex systems models.

Bounded rationality in agent‐based models: experiments with evolutionary programs

This paper examines the use of evolutionary programming in agent‐based modelling to implement the theory of bounded rationality. Evolutionary programming, which draws on Darwinian analogues of computing to create software programs, is a readily accepted means for solving complex computational problems. Evolutionary programming is also increasingly used to develop problem‐solving strategies in accordance with bounded rationality, which addresses features of human decision‐making such as cognitive limits, learning, and innovation. There remain many unanswered methodological and conceptual questions about the linkages between bounded rationality and evolutionary programming. This paper reports on how changing parameters in one variant of evolutionary programming, genetic programming, affects the representation of bounded rationality in software agents. Of particular interest are: the ability of agents to solve problems; limits to the complexity of agent strategies; the computational resources with which agents create, maintain, or expand strategies; and the extent to which agents balance exploration of new strategies and exploitation of old strategies.

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.

Modeling Land Use and Land Cover Change

Models are used in a variety of fields, including land change science, to better understand the dynamics of systems, to develop hypotheses that can be tested empirically, and to make predictions and/or evaluate scenarios for use in assessment activities. Modeling is an important component of each of the three foci outlined in the science plan of the Land use and cover change (LUCC) project (Turner et al., 1995) of the International Geosphere-Biosphere Program (IGBP) and the International Human Dimensions Program (IHDP). In Focus 1, on comparative land use dynamics, models are used to help improve our understanding of the dynamics of land use that arise from human decision-making at all levels, households to nations. These models are supported by surveys and interviews of decision makers. Focus 2 emphasizes development of empirical diagnostic models based on aerial and satellite observations of spatial and temporal land cover dynamics. Finally, Focus 3 focuses specifically on the development of models of land use and cover change (LUCC) that can be used for prediction and scenario generation in the context of integrative assessments of global change.A