The purpose of this model is to better understand the dynamics of a multihost pathogen in two host system comprising of high densities of domestic hosts and sympatric wildlife hosts susceptible to the pathogen.

An agent-based model designed as a tool to assess and plan free-ranging dog population management programs that implement Animal Birth Control (ABC). The time, effort, financial resources and conditions needed to successfully control dog populations and achieve rabies control can be determined by performing virtual experiments using DogPopDy.

An agent-based model simulating West Nile Virus dynamics in a one host (American robin)-one vector (Culex spp. mosquito) system. ODD improved and code cleaned.

We compare three model estimates for the time and treatment requirements to eliminate HCV among HIV-positive MSM in Victoria, Australia: a compartmental model; an ABM parametrized by surveillance data; and an ABM with a more heterogeneous population.

The SAFIRe model (Simulation of Agents for Fertility, Integrated Energy, Food Security, and Reforestation) is an agent-based model co-developed with rural communities in Senegal’s Groundnut Basin. Its purpose is to explore how local farming and pastoral practices affect the regeneration of Faidherbia albida trees, which are essential for maintaining soil fertility and supporting food security through improved millet production. The model supports collective reflection on how different social and ecological factors interact, particularly around firewood demand, livestock pressure, and agricultural intensification.

The model simulates a 100-hectare agricultural landscape where agents (farmers, shepherds, woodcutters, and supervisors) interact with trees, land parcels, and each other. It incorporates seasonality, crop rotation, tree growth and cutting, livestock feeding behaviors, and farmers’ engagement in sapling protection through Assisted Natural Regeneration (ANR). Two types of surveillance strategies are compared: community-led monitoring and delegated surveillance by forestry authorities. Farmer engagement evolves over time based on peer influence, meeting participation, and the success of visible tree regeneration efforts.

SAFIRe integrates participatory modeling (ComMod and ComExp) and a backcasting approach (ACARDI) to co-produce scenarios rooted in local aspirations. It was explored using the OpenMole platform, allowing stakeholders to test a wide range of future trajectories and analyze the sensitivity of key parameters (e.g., discussion frequency, time in fields). The model’s outcomes not only revealed unexpected insights—such as the hidden role of farmers in tree loss—but also led to real-world actions, including community nursery creation and behavioral shifts toward tree care. SAFIRe illustrates how agent-based modeling can become a tool for social learning and collective action in socio-ecological systems.

MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.

MOOvPOP is designed to simulate population dynamics (abundance, sex-age composition and distribution in the landscape) of white-tailed deer (Odocoileus virginianus) for a selected sampling region.

This model illustrates actor interaction in the construction sector, according to information gathered in NL. It offers a simple frame to represent diverse interests, interdependencies and effects on the number of built sustainable houses.

This NetLogo model is an implementation of the mostly verbal (and graphic) model in Jarret Walker’s Human Transit: How Clearer Thinking about Public Transit Can Enrich Our Communities and Our Lives (2011). Walker’s discussion is in the chapter “Connections or Complexity?”. See especially figure 12-2, which is on page 151.

In “Connections or Complexity?”, Walker frames the matter as involving a choice between two conflicting goals. The first goal is to minimize connections, the need to make transfers, in a transit system. People naturally prefer direct routes. The second goal is to minimize complexity. Why? Well, read the chapter, but as a general proposition we want to avoid unnecessary complexity with its attendant operating characteristics (confusing route plans in the case of transit) and management and maintenance challenges. With complexity general comes degraded robustness and resilience.

How do we, how can we, choose between these conflicting goals? The grand suggestion here is that we only choose indirectly, implicitly. In the present example of connections versus complexity we model various alternatives and compare them on measures of performance (MoP) other than complexity or connections per se. The suggestion is that connections and complexity are indicators of, heuristics for, other MoPs that are more fundamental, such as cost, robustness, energy use, etc., and it is these that we at bottom care most about. (Alternatively, and not inconsistently, we can view connections and complexity as two of many MoPs, with the larger issue to be resolve in light of many MoPs, including but not limited to complexity and connections.) We employ modeling to get a handle on these MoPs. Typically, there will be several, taking us thus to a multiple criteria decision making (MCDM) situation. That’s the big picture.

This is an agent-based model coded in NetLogo. The model simulates population dynamics of bighorn sheep population in the Hell’s Canyon region of Idaho and will be used to develop a better understanding of pneumonia dynamics in bighorn sheep populations. The overarching objective is to provide a decision-making context for effective management of pneumonia in wild populations of bighorn sheep.