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.

Negotiation plays a fundamental role in shaping human societies, underpinning conflict resolution, institutional design, and economic coordination. This article introduces E³-MAN, a novel multi-agent model for negotiation that integrates individual utility maximization with fairness and institutional legitimacy. Unlike classical approaches grounded solely in game theory, our model incorporates Bayesian opponent modeling, transfer learning from past negotiation domains, and fallback institutional rules to resolve deadlocks. Agents interact in dynamic environments characterized by strategic heterogeneity and asymmetric information, negotiating over multidimensional issues under time constraints. Through extensive simulation experiments, we compare E³-MAN against the Nash bargaining solution and equal-split baselines using key performance metrics: utilitarian efficiency, Nash social welfare, Jain fairness index, Gini coefficient, and institutional compliance. Results show that E³-MAN achieves near-optimal efficiency while significantly improving distributive equity and agreement stability. A legal application simulating multilateral labor arbitration demonstrates that institutional default rules foster more balanced outcomes and increase negotiation success rates from 58% to 98%. By combining computational intelligence with normative constraints, this work contributes to the growing field of socially aware autonomous agents. It offers a virtual laboratory for exploring how simple institutional interventions can enhance justice, cooperation, and robustness in complex socio-legal systems.

The agent-based simulation is set to work on information that is either (a) functional, (b) pseudo-functional, (c) dysfunctional, or (d) irrelevant. The idea is that a judgment on whether information falls into one of the four categories is based on the agent and its network. In other words, it is the agents who interprets a particular information as being (a), (b), (c), or (d). It is a decision based on an exchange with co-workers. This makes the judgment a socially-grounded cognitive exercise. The uFUNK 1.0.2 Model is set on an organization where agent-employee work on agent-tasks.

Presented here is a socioeconomic agent-based model (ABM) to examine the Hollywood labor system as a network within a simulated movie labor market based on preferential attachment and compare the findings with 50 co-production ego networks during the 2015 movie year. Using the ABM, I test the role slight individual preference for racial and ethnic similarity within one’s own network at the microlevel and find that it is insufficient to explain the phenomena of racial and ethnic underrepresentation at the macrolevel. The ABM also includes the ability to test alternative explanations, such as overt opportunity loss as a possible explanation.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

The core algorithm is an agent-based model, which simulates travel patterns on a network based on microscopic decision-making by each traveler.

SONG is a simulator designed for simulating the process of transportation network growth.

We built a model using R,polr package, to assess 55 published case studies from developing countries to determine what factors influence the level of compliance of local communities with protected area regulations.

This model investigates the link between prescribed growth in body size, population dynamics and density dependence through population feedback on available resources.