The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the increasing use of data and dynamics in ABMs has further enhanced the complexity of their outputs. In this article, we offer an overview of the state-of-the-art approaches in analyzing and reporting ABM outputs highlighting challenges and outstanding issues. In particular, we examine issues surrounding variance stability (in connection with determination of appropriate number of runs and hypothesis testing), sensitivity analysis, spatio-temporal analysis, visualization, and effective communication of all these to non-technical audiences, such as various stakeholders.@en

Urban land-use modeling methods have experienced substantial improvements in the last several decades. With the advancement of urban land-use change theories and modeling techniques, a considerable number of models have been developed. The relatively young approach, agent-based modeling, provides urban land-use models with some new features and can help address the challenges faced by traditional models. Applications of agent-based models to study urban dynamics have increased steadily over the last twenty years. To offer a retrospective on the developments in agent-based models (ABMs) of urban residential choices, we review fifty-one relevant models that fall into three general categories: (i) urban land-use models based on classical theories; (ii) different stages of the urbanization process; and (iii) integrated agent-based and microsimulation models. We summarize and compare the main features of these fifty-one models within each category. This review focuses on three fundamental new features introduced byABMs. The first is agent heterogeneity with particular attention to the method of introducing heterogeneity in agents' attributes and behaviors. The second is the representation of land-market processes, namely preferences, resources constraints, competitive bidding, and endogenous relocation. The third is the method of measuring the extensive model outputs. In addition, we outline accompanying challenges to, and open questions for, incorporating these new features. We conclude that, by modeling agent heterogeneity and land markets, and by exploiting a much broader dimension of output, we will enhance our understanding of urban land-use change and are hopefully able to improve model fitness and robustness.@en

Land-use change in a market economy, particularly at the urban-rural fringe in North America, is shaped through land and housing markets. Although market activities are at the core of economic studies of land-use change, many market elements are neglected by coupled human-environment models. We scrutinized the effects of the level of detail of market representation using an abstract, agent-based model of land-use change. This model includes agents representing land buyers and sellers and their respective market-based decision-making behaviors. Our results show that although incorporating key market elements, particularly budget constraints and competitive bidding, in land-use models generally alters projected land-use patterns, their impacts differ significantly depending on the level of detail of market representation. Consistent with theories of land change, our research confirms that budget constraints can considerably reduce the projected quantity of land-use change. The effects of competitive bidding, however, are more complex and depend on buyers' budgets, their relative preferences for proximity versus open-space amenities, and the size of neighborhoods. Market competition might reduce or increase the quantity of land-use change and the degree of sprawl in the simulated landscapes. Because of the strong effects of market elements on resulting patterns, adequate representation of the structure of markets is important for capturing and characterizing the complexity inherent in coupled human-environment systems.@en

Representing agent heterogeneity is one of the main reasons that agent-based models become increasingly popular in simulating the emergence of land-use, land-cover change and socioeconomic phenomena. However, the relationship between heterogeneous economic agents and the resultant landscape patterns and socioeconomic dynamics has not been systematically explored. In this paper, we present a stylized agent-based land market model, Land Use in eXurban Environments (LUXE), to study the effects of multidimensional agents' heterogeneity on the spatial and socioeconomic patterns of urban land use change under various market representations. We examined two sources of agent heterogeneity: budget heterogeneity, which imposes constraints on the affordability of land, and preference heterogeneity, which determines location choice. The effects of the two dimensions of agents' heterogeneity are systematically explored across different market representations by three experiments. Agents' heterogeneity exhibits a complex interplay with various forms of market institutions as indicated by macro-measures (landscape metrics, segregation index, and socioeconomic metrics). In general, budget heterogeneity has pronounced effect on socioeconomic results, while preference heterogeneity is highly pertinent to spatial outcomes. The relationship between agent heterogeneity and macro-measures becomes more complex when more land market mechanisms are represented. In other words, appropriately simulating agent heterogeneity plays an important role in guaranteeing the fidelity of replicating empirical land use change process.@en

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.@en

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.@en

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.@en

Dutch coastal land markets are characterized by high amenity values but are threatened by potential coastal hazards, leading to high potential damage costs from flooding. Yet, Dutch residents generally perceive low or no flood risk. Using an agent-based land market model and Dutch survey data on risk perceptions and location preferences, this paper explores the patterns of land development and land rents produced by buyers with low, highly skewed risk perceptions. We find that, compared to representative agent and uniform risk perception models, the skewed risk perception distribution produces substantially more, high-valued development in risky coastal zones, potentially creating economically significant risks triggered by the current Dutch flood protection policy.@en

We present the conceptual design of a new land-change modelling framework that builds on previous land-change research and models (i.e. ALMA, SOME, DEED). The design integrates agents of land change, land-market mechanisms, land-management behaviour and its ecosystem impacts, and land-policy scenarios into a single framework that can be used to address questions about land-change processes in exurban environments. The framework is implemented in Java, built using the Repast Simphony agent-based libraries within the Eclipse integrated development environment. The framework serves as a platform for integrating human and natural processes, as well as data that include social surveys of residential landscape and neighbourhood preferences as well as landmanagement behaviours, ecological field measurements of biomass in residential property parcels, interpretations of historical air photographs, and economic and household data acquired from local governments in Southeastern Michigan. The purpose of the framework is to provide an overarching design that can be extended into specific model implementations that evaluate, among other questions, how policy, land-management preferences, and land-market dynamics affect land-use and land-cover change patterns and subsequent carbon storage and flux.@en

We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller sides; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Thünen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a 'sellers' market' to a 'buyers' market'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion.@en

Heterogeneity in both the spatial environment and economic agents is a crucial driver of land market dynamics. We present an agent-based land market model where land from agriculture use is transferred into urban. The model combines the microeconomic demand, supply, and bidding foundations of spatial economics models with the spatial heterogeneity of spatial econometric models in a single methodological platform. Heterogeneous agents exchange heterogeneous spatial goods via simulated bilateral market interactions. We model a coastal city where both coastal amenities and flooding or erosion disamenities drive land market outcomes, facilitating separate analysis of the effects of each driver on land rents and land development patterns. We also analyze the implications of homogeneous versus heterogeneous but unbiased flood risk perceptions. Since buyers with low risk perceptions drive market outcomes, spatial development under heterogeneous risk perceptions differs qualitatively, with more expansion into risky areas. Our results highlight the shortcomings of policy models based on representative agent assumptions and the importance of including agent-level data in empirical modeling.@en

This paper aims to understand the effects of biases in individual flood risk perception on aggregated land use patterns and their implications for macro policy. We develop a spatially explicit land market model and param-eterize individual risk perceptions with data from a survey held in the Nether-lands in 2008. Two sets of experiments are presented. A model with heteroge-neous agents produces qualitatively different results compared to a model with homogeneous agents. Individuals with low flood risk perception drive urban developments into the economically inefficient zone and leading to the increas-ing potential damage.@en

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.@en

We construct a spatially explicit agent-based model of a bilateral land market. Heterogeneous agents form their bid and ask prices for land based on the utility that they obtain from a certain location (house/land) and based on the state of the market (an excess of demand or supply). We underline the distinction between bid /ask price and individual willingness to pay/to accept and show that variations between them that reflect market conditions can influence land prices. Agents sort among locations with respect to distance from the city center and environmental spatial externalities. Aggregated outcomes such as land patterns and land prices are produced by the model. The basic model of buyers and sellers trading land in the urban area produces results identical to the monocentric urban model. However, more complex dynamics appears when environmental amenities and market-adjustment variable influence the formation of land prices.@en