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.

The complexities of agent-based modeling output analysis

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.

A review of urban residential choice models using agent-based modeling

Climate change mitigation and adaptation are human-mediated processes but global modelling tools currently take no account of human responses to environmental change. In this Perspective the authors propose the agent functional type approach to advance the representation of these processes. Understanding the links between land-use change (LUC) and climate change is vital in developing effective land-based climate mitigation policies and adaptation measures. Although mitigation and adaptation are human-mediated processes, current global-scale modelling tools do not account for societal learning and other human responses to environmental change. We propose the agent functional type (AFT) method to advance the representation of these processes, by combining socio-economics (agent-based modelling) with natural sciences (dynamic global vegetation models). Initial AFT-based simulations show the emergence of realistic LUC patterns that reflect known LUC processes, demonstrating the potential of the method to enhance our understanding of the role of people in the Earth system.

Global models of human decision-making for land-based mitigation and adaptation assessment

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances - in terms of model complexity, model evaluation, and model structure - can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from 'yet another model' to doing better science with models.

/pdf/strategic-directions-for-agent-based-modeling-avoiding-the-4jfzrslq91.pdf

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome.

Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development. Social networks composed of weak ties and strong ties affect adoption of multifunctional agriculture.Network effectiveness is contingent on social and spatial factors.Programs targeting network structure and function could facilitate expansion of alternative systems.

/pdf/modeling-the-effect-of-social-networks-on-adoption-of-4o0wobai8u.pdf

Modeling the effect of social networks on adoption of multifunctional agriculture

A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations

We consider the problem of estimating parameters of stochastic differential equations (SDEs) with discrete-time observations that are either completely or partially observed. The transition density between two observations is generally unknown. We propose an importance sampling approach with an auxiliary parameter when the transition density is unknown. We embed the auxiliary importance sampler in a penalized maximum likelihood framework which produces more accurate and computationally efficient parameter estimates. Simulation studies in three different models illustrate promising improvements of the new penalized simulated maximum likelihood method. The new procedure is designed for the challenging case when some state variables are unobserved and moreover, observed states are sparse over time, which commonly arises in ecological studies. We apply this new approach to two epidemics of chronic wasting disease in mule deer.

Penalized importance sampling for parameter estimation in stochastic differential equations

Agent-based models have been recognized as computational laboratories furnishing spatial scientists with a plausible exploratory apparatus for learning about land use dynamics through an explicit representation of human behavior. At the same time research suggests that the utility of agent-based modeling has been hampered by a limited understanding of the decision processes involving a wide array of stakeholders with different perceptions and preferences. Therefore, it is critically important to offer new tools for a more comprehensive inspection of uncertainties related to the interrelationships between individual choices and land development patterns. In this paper, we propose a new approach to evaluating agent behavioral uncertainty using time dependent variance-based global sensitivity analysis. The method produces time series of first order sensitivity indices that allocate the variance of development patterning to two heterogeneous behavioral features: risk perceptions, quantified through attitude utility functions, and land preferences, in the form of weights assigned to different decision criteria. We experiment with three ABM scenarios that emphasize the various decision components. The scenarios utilize a fixed number of parameters with changing distributions reflecting the behavioral characteristic under consideration. Outcome maps for each time step are summarized using the aggregation index, which is further employed in sensitivity computation. The resulting sensitivity indices are plotted against time to track the impact of input conditions on land use compactness. The comparisons of the plots reveal varying sensitivity trajectories that depend on the modified decision rule.

Time dependent variance-based sensitivity analysis of development aggregation generated by heterogeneous land use agents

The growing body of knowledge on modeling land use systems points to epistemic uncertainty as one of the challenging obstacles in development and application of agent-based models (ABMs). To decrease outcome uncertainty, sensitivity analysis (SA) is performed as part of model verification and validation. Oftentimes, however, it is inadequately addressed, partly because of the lack of tools and techniques that focus on an explicit evaluation of ABM dynamics. The nonlinear processes, inherent in such models, necessitate longitudinal SA with time path investigation of input-output relationships of endogenous variables. In response to the outlined deficiencies, this study investigates the potential of time-dependent global sensitivity analysis (time-GSA) in examining the dynamics of outcome uncertainty of a simple ABM of land use change. Specifically, we apply first and total order sensitivity indices to decompose variance of output landscape fragmentation, apportioned to model inputs for multiple time steps and multiple realizations of the ABM. We focus the analysis on selected complex systems characteristics including preference uncertainty, path dependence, access to information, and magnitude of interactions and feedbacks. We conclude that the factor sensitivity measures vary significantly during model execution. Consequently, a static snapshot of ABM sensitivity, taken at the end of the simulation, is inadequate when deciding on factor prioritization and reduction. Assuming that ABM dynamics is a result of factor interaction, we observe a distinct time lag of nonlinearity, which unfolds after the formation of the seeds of development. Therefore, we argue for further application of time-GSA in ABM as one of the visual quantitative techniques contributing to evaluation of ABM nonlinearity.

Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change

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A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations

Penalized importance sampling for parameter estimation in stochastic differential equations

Time dependent variance-based sensitivity analysis of development aggregation generated by heterogeneous land use agents

Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change