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

Predicting the spatial expansion of an animal population with presence-only data

Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision-making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence-only distribution data and minimal demographic information to predict the spread of roe deer (Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home-range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional-scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence-only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much-needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management.

Data-driven agent-based modelling of incentives for carbon sequestration: The case of sown biodiverse pastures in Portugal

Sown biodiverse permanent pastures rich in legumes (SBP) offset animal farming emissions due to their potential to sequester carbon. From 2009 to 2014 Portugal implemented a programme that provided payments to incentivize the adoption of SBP. However, no proper evaluation of its outcome was conducted. To address this gap, we develop an agent-based model (ABM) at the municipality level to study the adoption of SBP in Portugal and assess the outcome of the programme. We applied the first pure data-driven approach in agricultural land-use ABM, which relies on machine learning algorithms to define the agents' behavioural rules and capture their interaction with biophysical conditions. The ABM confirms that the program effectively expanded the adoption of SBP. However, our estimates indicate that the adoption rate in the absence of payments would have been higher than originally predicted. Furthermore, the existence of the program decreased the adoption rate after its conclusion. These findings underscore the importance of using reliable models and considering residual effects to properly design land use policies. The ABM developed in this study provides a basis for future research aimed at supporting the development of new policies to further promote the adoption of SBP.

An integrated socio-economic agent-based modeling framework towards assessing farmers' decision making under water scarcity and varying utility functions

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers' behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.

An agent-based transmission model of COVID-19 for re-opening policy design

The global pandemic caused by the coronavirus (COVID-19) disease has collapsed the worldwide economy. Elements such as non-obligatory vaccination, new strain variants and lack of discipline to follow social distancing measures suggest the possibility that COVID-19 may continue to exist, exhibiting the behavior of a seasonal disease. As the socio-economic crisis has become unsustainable, all countries are planning strategies to gradually restart their economic and social activities. Initially, several containment measures have been adopted involving social distancing, infection detection tests, and ventilation systems. Despite the implementation of such policies, there exists a lack of evaluation of their performance to reduce the contagion index. This means there are no appropriate indicators to decide which intervention or set of interventions present the most effective result. Under these conditions, the development of models that provide useful information in the design and evaluation of containment measures and re-opening policies is of prime concern. In this paper, a novel approach to model the transmission process of COVID-19 in closed environments is proposed. The proposed model can simulate the effects that result from the complex interaction among individuals when they follow a particular containment measure or re-opening policy. With the proposed model, different hypothetical re-opening policies, that are otherwise impossible to analyze in real conditions, can be tested. Computer experiments demonstrate that the proposed model provides suitable information and realistic predictions, which are appropriate for designing strategies that allow a safe return to economic activities.

An Agent-Based Model-Driven Decision Support System for Assessment of Agricultural Vulnerability of Sugarcane Facing Climatic Change

In recent years, there have been significant changes in weather patterns, mainly caused by sharp increases in temperature, increases in carbon dioxide, and fluctuations in precipitation levels, negatively impacting agricultural production. Agricultural systems are characterized by being vulnerable to the variation of biophysical and socioeconomic factors involved in the development of agricultural activities. Agent-based models (ABMs) enable the study, analysis, and management of ecosystems through their ability to represent networks and their spatial nature. In this research, an ABM is developed to evaluate the behavior and determine the vulnerability in the sugarcane agricultural system; allowing the capitalization of knowledge through characteristics such as social ability and autonomy of the modeled agents through fuzzy logic and system dynamics. The methodology used includes information networks for a dynamic assessment of agricultural risk modeled by time series, system dynamics, uncertain parameters, and experience; which are developed in three stages: vulnerability indicators, crop vulnerability, and total system vulnerability. The development of ABM, a greater impact on the environmental contingency is noted due to the increase in greenhouse gas emissions and the exponential increase in extreme meteorological phenomena threatening the cultivation of sugarcane, making the agricultural sector more vulnerable and reducing the yield of the harvest.

Future Developments in Geographical Agent-Based Models: Challenges and Opportunities

Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent-based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual-level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state-of-the-art, and the outlook for the field over the next decade. We argue that although agent-based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems.

Evaluating environmental change and behavioral decision-making for sustainability policy using an agent-based model: A case study for the Smoky Hill River Watershed, Kansas

Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.

Exploring sustainable land use in forested tropical social-ecological systems: A case-study in the Wet Tropics

Tropical countries lie at the nexus of three pressing issues for global sustainability: agricultural production, climate change mitigation and biodiversity conservation. The forces that drive forest protection do not necessarily oppose those that drive forest clearance for development. This decoupling, enhanced by the stronger economic forces compared to conservation, is detrimental for the social-ecological sustainability of forested tropical landscapes. This paper presents an integrated, and spatially-explicit, Agent-Based Model that examines the future impacts of land-use change scenarios on the sustainability of the Wet Tropics region of tropical Queensland, Australia. In particular, the model integrates Bayesian Belief Networks, Geographical Information Systems, empirical data and expert knowledge, under a land-sharing/land-sparing analysis, to study the impact of different landscape configurations on trade-offs and synergies among biodiversity and two ecosystem services (sugarcane production and carbon sequestration). Contrary to most tropical regions, model simulations show that Business As Usual is helping to reconcile these contrasting goals in the forested landscape of the Wet Tropics. The paper analyses which combination of governance and socio-economic factors is causing these positive results. This is an outstanding achievement for a tropical region, considering that most tropical areas are characterized for having stronger economic-land clearing forces compared to conservation forces, which reduce important ecosystem services for human wellbeing and the health of ecosystems.

Developing agent-based models of complex health behaviour

Managing non-communicable diseases requires policy makers to adopt a whole systems perspective that adequately represents the complex causal architecture of human behaviour. Agent-based modelling is a computational method to understand the behaviour of complex systems by simulating the actions of entities within the system, including the way these individuals influence and are influenced by their physical and social environment. The potential benefits of this method have led to several calls for greater use in public health research. We discuss three challenges facing potential modellers: model specification, obtaining required data, and developing good practices. We also present steps to assist researchers to meet these challenges and implement their agent-based model.

Using focus groups to design systems science models that promote oral health equity

BACKGROUND

While the US population overall has experienced improvements in oral health over the past 60 years, oral diseases remain among the most common chronic conditions across the life course. Further, lack of access to oral health care contributes to profound and enduring oral health inequities worldwide. Vulnerable and underserved populations who commonly lack access to oral health care include racial/ethnic minority older adults living in urban environments. The aim of this study was to use a systematic approach to explicate cause and effect relationships in creating a causal map, a type of concept map in which the links between nodes represent causality or influence.

METHODS

To improve our mental models of the real world and devise strategies to promote oral health equity, methods including system dynamics, agent-based modeling, geographic information science, and social network simulation have been leveraged by the research team. The practice of systems science modeling is situated amidst an ongoing modeling process of observing the real world, formulating mental models of how it works, setting decision rules to guide behavior, and from these heuristics, making decisions that in turn affect the state of the real world. Qualitative data were obtained from focus groups conducted with community-dwelling older adults who self-identify as African American, Dominican, or Puerto Rican to elicit their lived experiences in accessing oral health care in their northern Manhattan neighborhoods.

RESULTS

The findings of this study support the multi-dimensional and multi-level perspective of access to oral health care and affirm a theorized discrepancy in fit between available dental providers and patients. The lack of information about oral health at the community level may be compromising the use and quality of oral health care among racial/ethnic minority older adults.

CONCLUSIONS

Well-informed community members may fill critical roles in oral health promotion, as they are viewed as highly credible sources of information and recommendations for dental providers. The next phase of this research will involve incorporating the knowledge gained from this study into simulation models that will be used to explore alternative paths toward improving oral health and health care for racial/ethnic minority older adults.