This is an extension of the basic Suceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in two spaces, one a treatment, and one a control. Through the modeling options, one can explore how changing assumptions about the number of susceptible people, starting number of infected people, the disease’s infection probability, and average duration impacts the outcome. In addition, this version allows users to explore how public health interventions like social distancing, masking, and isolation can affect the number of people infected. The model shows that the interactions of agents, and the interventions can drastically affect the results of the model.

We used the model in our course about COVID-19: https://www.csats.psu.edu/science-of-covid19

The emergence of cooperation in human societies is often linked to environmental constraints, yet the specific conditions that promote cooperative behavior remain an open question. This study examines how resource unpredictability and spatial dispersion influence the evolution of cooperation using an agent-based model (ABM). Our simulations test the effects of rainfall variability and resource distribution on the survival of cooperative and non-cooperative strategies. The results show that cooperation is most likely to emerge when resources are patchy, widely spaced, and rainfall is unpredictable. In these environments, non-cooperators rapidly deplete local resources and face high mortality when forced to migrate between distant patches. In contrast, cooperators—who store and share resources—can better endure extended droughts and irregular resource availability. While rainfall stochasticity alone does not directly select for cooperation, its interaction with resource patchiness and spatial constraints creates conditions where cooperative strategies provide a survival advantage. These findings offer broader insights into how environmental uncertainty shapes social organization in resource-limited settings. By integrating ecological constraints into computational modeling, this study contributes to a deeper understanding of the conditions that drive cooperation across diverse human and animal systems.

The MML is a hybrid modeling environment that couples an agent-based model of small-holder agropastoral households and a cellular landscape evolution model that simulates changes in erosion/deposition, soils, and vegetation.

This model is represents an effort to replicate one of the first attempts (van der Vaart 2006) to develop an agent based model of agricultural origins using principles and equations drawn from human behavioral ecology. We have taken one theory of habitat choice (Ideal Free Distribution) and applied it to human behavioral adaptations to differences in resource quality of different habitats.

MUSA is an ABM that simulates the commuting sector in USA. A multilevel validation was implemented. Social network with a social-circle structure included. Two types of policies have been tested: market-based and preference-change.

The model represents an archetypical fishery in a co-evolutionary social-ecological environment, capturing different dimensions of trust between fishers and fish buyers for the establishment and persistence of self-governance arrangements.

This model examines the potential impact of market collapse on the economy and demography of fishing households in the Logone Floodplain, Cameroon.

This model describes and analyses the outcomes of the confrontation of interests, some conflicting, some common, about the management of a small river in SW France

The CHIME ABM explores information distribution networks and agents’ protective decision making in the context of hurricane landfall.

This code can be used to analyze the sensitivity of the Deffuant model to different measurement errors. Specifically to:
- Intrinsic stochastic error
- Binning of the measurement scale
- Random measurement noise
- Psychometric distortions

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