Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.

This model simulates the dynamics of agricultural land use change, specifically the transition between agricultural and non-agricultural land use in a spatial context. It explores the influence of various factors such as agricultural profitability, path dependency, and neighborhood effects on land use decisions.

The model operates on a grid of patches representing land parcels. Each patch can be in one of two states: exploited (green, representing agricultural land) or unexploited (brown, representing non-agricultural land). Agents (patches) transition between these states based on probabilistic rules. The main factors affecting these transitions are agricultural profitability, path dependency, and neighborhood effects.
-Agricultural Profitability: This factor is determined by the prob-agri function, which calculates the probability of a non-agricultural patch converting to agricultural based on income differences between agriculture and other sectors. -Path Dependency: Represented by the path-dependency parameter, it influences the likelihood of patches changing their state based on their current state. It’s a measure of inertia or resistance to change. -Neighborhood Effects: The neighborhood function calculates the number of exploited (agricultural) neighbors of a patch. This influences the decision of a patch to convert to agricultural land, representing the influence of surrounding land use on the decision-making process.

Scilab version of an agent-based model of societal well-being, based on the factors of: overvaluation of conspicuous prosperity; tradeoff rate between inconspicuous/conspicuous well-being factors; turnover probability; and individual variation.

This Bicycle encounter model builds on the Salzburg Bicycle model (Wallentin & Loidl, 2015). It simulates cyclist flows and encounters, which are locations of potential accidents between cyclists.

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 includes an innovation search environment. Agents search and can share their findings. It’s implemented in Netlogo-Hubnet & a parallel Netlogo model. This allows for validation of search strategies against experimental findings.

This program simulates a group of hunter-gatherer (households) moving randomly over an artificial landscapoe pulated with resources randomly distributed (a Gaussian distribution). To survive, agents hunt and gather using their own labor resources and available technology. When labor and technology is not enough to compensate the resource difficulty of access, they need to cooperate. The purpose of the model is to analyze the consequences of cooperation on cultural diversity: the more the agents cooperate, the more their culture (a 10 componenet vector) is updated to imitate the culture of cooperative agents. The less the agent cooperates, the more different its culture becomes.

FNNR-ABM is an agent-based model that simulates human activity, Guizhou snub-nosed monkey movement, and GTGP-enrolled land parcel conversion in the Fanjingshan National Nature Reserve in Guizhou, China.

Quick-start guide:
1. Install Python and set environmental path variables.
2. Install the mesa, matplotlib (optional), and pyshp (optional) Python libraries.
3. Configure fnnr_config_file.py.
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This model takes into consideration Peer Reviewing under the influence of Impact Factor (PRIF) and it has the purpose to explore whether the infamous metric affects assessment of papers under review. The idea is to consider to types of reviewers, those who are agnostic towards IF (IU1) and those that believe that it is a measure of journal (and article) quality (IU2). This perception is somehow reflected in the evaluation, because the perceived scientific value of a paper becomes a function of the journal in which an article has been submitted. Various mechanisms to update reviewer preferences are also implemented.