The SMASH model is an agent-based model of rural smallholder households. It models households’ evolving income and wealth, which they earn through crop sales. Wealth is carried in the form of livestock, which are grazed on an external rangeland (exogenous) and can be bought/sold as investment/coping mechanisms. The model includes a stylized representation of soil nutrient dynamics, modeling the inflows and outflows of organic and inorganic nitrogen from each household’s field.

The model has been applied to assess the resilience-enhancing effects of two different farm-level adaptation strategies: legume cover cropping and crop insurance. These two strategies interact with the model through different mechanims - legume cover cropping through ecological mechanisms and crop insurance through financial mechanisms. The model can be used to investigate the short- and long-term effects of these strategies, as well as how they may differently benefit different types of household.

After a little work experience, we realize that different kinds of people prefer different work environments: some enjoy a fast-paced challenge; some want to get by; and, others want to show off.

From that experience, we also realize that different kinds of people affect their work environments differently: some increase the pace; some slow it down; and, others make it about themselves.

This model concerns how three different kinds of people affect their work environment and how that work environment affects them in return. The model explores how this circular relation between people’s preferences and their environment creates patterns of association and performance over time.

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The purpose of the model is to explore the influence of the design of circular business models (CBMs) on CBM viability. The model represents an Industrial Symbiosis Network (ISN) in which a processor uses the organic waste from suppliers to produce biogas and nutrient rich digestate for local reuse. CBM viability is expressed as value captured (e.g., cash flow/tonne waste/agent) and the survival of the network over time (shown in the interface).

In the model, the value captured is calculated relative to the initial state, using incineration costs as a benchmark. Moderating variables are interactions with the waste incinerator and actor behaviour factors. Actors may leave the network when the waste supply for local production is too low, or when personal economic benefits are too low. When the processor decides to leave, the network fails. Theory of planned behaviour can be used to include agent behaviour in the simulations.

This project combines game theory and genetic algorithms in a simulation model for evolutionary learning and strategic behavior. It is often observed in the real world that strategic scenarios change over time, and deciding agents need to adapt to new information and environmental structures. Yet, game theory models often focus on static games, even for dynamic and temporal analyses. This simulation model introduces a heuristic procedure that enables these changes in strategic scenarios with Genetic Algorithms. Using normalized 2x2 strategic-form games as input, computational agents can interact and make decisions using three pre-defined decision rules: Nash Equilibrium, Hurwicz Rule, and Random. The games then are allowed to change over time as a function of the agent’s behavior through crossover and mutation. As a result, strategic behavior can be modeled in several simulated scenarios, and their impacts and outcomes can be analyzed, potentially transforming conflictual situations into harmony.

This study employs a hierarchical cross-departmental ABM to explore the question: How and to what extent are the land use policies enforced when assessed against the real-world land use pattern? Specifically, two sub-questions are of interest: How can real-world policy interactions be abstracted into the behavior across hierarchical governmental departments in the model? How can the level of enforcement for each land use policy be quantified under these interactions? We build three hierarchical agents—the central level, the local level that incorporates three departments, and the village collective level—with simplified but plausible processes of land use change, with levels of enforcement of different land use policies as key parameters. We calibrate the model using a genetic algorithm to determine those parameters and answer our research question. We further applied the model to simulate potential land use changes and investigate the implications of different policy options. The results are expected to provide insights into the intricate relationships shaping land use processes, contributing to evidence-based decision-making in urban planning and sustainable land use management.

Municipal waste management (MWM) is essential for urban development. Efficient waste management is essential for providing a healthy and clean environment, for reducing GHGs and for increasing the amount of material recycled. Waste separation at source is perceived as an effective MWM strategy that relays on the behaviour of citizens to separate their waste in different fractions. The strategy is straightforward, and many cities have adopted the strategy or are working to implement it. However, the success of such strategy depends on adequate understanding of the drivers of the behaviour of proper waste sorting. The Theory of Planned Behaviour (TPB) has been extensively applied to explain the behaviour of waste sorting and contributes to determining the importance of different psychological constructs. Although, evidence shows its validity in different contexts, without exploring how urban policies and the built environment affect the TPB, its application to urban challenges remains unlocked. To date, limited research has focused in exposing how different urban situations such as: distance to waste bins, conditions of recycling facilities or information campaigns affect the planned behaviour of waste separation. To fill this gap, an agent-based model (ABM) of residents capable of planning the behaviour of waste separation is developed. The study is a proof of concept that shows how the TPB can be combined with simulations to provide useful insights to evaluate different urban planning situations. In this paper we depart from a survey to capture TPB constructs, then Structural Equation Modelling (SEM) is used to validate the TPB hypothesis and extract the drivers of the behaviour of waste sorting. Finally, the development of the ABM is detailed and the drivers of the TPB are used to determine how the residents behave. A low-density and a high-density urban scenario are used to extract policy insights. In conclusion, the integration between the TPB into ABMs can help to bridge the knowledge gap between can provide a useful insight to analysing and evaluating waste management scenarios in urban areas. By better understanding individual waste sorting behaviour, we can develop more effective policies and interventions to promote sustainable waste management practices.

This generic individual-based model of a bird colony shows how the influence neighbour’s stress levels synchronize the laying date of neighbours and also of large colonies. The model has been used to demonstrate how this form of simulation model can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions.

This model grows land use patterns that emerge as a result of land-use compatibilities stablished in urban development plans, land topography, and street networks. It contains urban brushes to paint streets and land uses as a way to learn about urban pattern emergence through free experimentation.

An ABM of changes in individuals’ lifestyles which considers their
evolving behavioural choices. Individuals have a set of environmental behavioural traits that spread through a fixed Watts–Strogatz graph via social interactions with their neighbours. These exchanges are mediated by transmission biases informing from whom an individual learns and
how much attention is paid. The influence of individuals on each other is a function of their similarity in environmental identity, where we represent environmental identity computationally by aggregating past agent attitudes towards multiple environmentally related behaviours. To perform a behaviour, agents must both have
a sufficiently positive attitude toward a behaviour and overcome a corresponding threshold. This threshold
structure, where the desire to perform a behaviour does not equal its enactment, allows for a lack of coherence
between attitudes and actual emissions. This leads to a disconnect between what people believe and what
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Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives.