This model extends the bounded confidence model of Deffuant and Weisbuch. It introduces online contexts in which a person can deliver his or her opinion to several other persons. There are 2 additional parameters accessibility and connectivity.

The Li-BIM model aims at simulating the behavior of occupants in a building. It is structured around the numerical modeling of the building (IFC format) and a BDI cognitive architecture. The model has been implemented under the GAMA platform.

WeDiG Sim- Weighted Directed Graph Simulator - is an open source application that serves to simulate complex systems. WeDiG Sim reflects the behaviors of those complex systems that put stress on scale-free, weightedness, and directedness. It has been implemented based on “WeDiG model” that is newly presented in this domain. The WeDiG model can be seen as a generalized version of “Barabási-Albert (BA) model”. WeDiG not only deals with weighed directed systems, but also it can handle the […]

The SIM-VOLATILE model is a technology adoption model at the population level. The technology, in this model, is called Volatile Fatty Acid Platform (VFAP) and it is in the frame of the circular economy. The technology is considered an emerging technology and it is in the optimization phase. Through the adoption of VFAP, waste-treatment plants will be able to convert organic waste into high-end products rather than focusing on the production of biogas. Moreover, there are three adoption/investment scenarios as the technology enables the production of polyhydroxyalkanoates (PHA), single-cell oils (SCO), and polyunsaturated fatty acids (PUFA). However, due to differences in the processing related to the products, waste-treatment plants need to choose one adoption scenario.

In this simulation, there are several parameters and variables. Agents are heterogeneous waste-treatment plants that face the problem of circular economy technology adoption. Since the technology is emerging, the adoption decision is associated with high risks. In this regard, first, agents evaluate the economic feasibility of the emerging technology for each product (investment scenarios). Second, they will check on the trend of adoption in their social environment (i.e. local pressure for each scenario). Third, they combine these two economic and social assessments with an environmental assessment which is their environmental decision-value (i.e. their status on green technology). This combination gives the agent an overall adaptability fitness value (detailed for each scenario). If this value is above a certain threshold, agents may decide to adopt the emerging technology, which is ultimately depending on their predominant adoption probabilities and market gaps.

MASTOC-LLM extends the classic Multi-Agent System Tragedy of the Commons (MASTOC) model by replacing hard-coded behavioral rules with autonomous decision-making powered by large language models (LLMs). Three heterogeneous agents manage herds of cows on a shared grassland commons. Each tick, an agent receives a structured prompt describing current resource levels, its own herd size, peer behavior, and — optionally — a rolling memory of recent rounds and messages from neighboring agents. The LLM returns a stocking decision (add, remove, or hold cows) together with a natural-language rationale and, when communication is enabled, a short message to broadcast to peers.

The model is designed to test whether LLM agents spontaneously develop Ostrom-style common-pool resource governance (mutual monitoring, graduated sanctions, graduated rule revision) or instead fall into identifiable failure modes. Preliminary experiments with Claude Haiku 4.5, GPT-5.4-mini, and DeepSeek R1:32b have revealed four recurring collapse patterns — Cooperative Paralysis, Defection Cascade, Overshoot-Panic, and Hybrid Architecture Failure — whose onset timing is sensitive to memory length, inter-agent communication, and the post-training alignment approach of the underlying model.

MASTOC-LLM is intended as a laboratory for generative agent-based modelling (GABM) methodology: it provides a clean, well-understood commons baseline against which LLM behavioral hypotheses can be systematically tested and compared across models, parameter sweeps, and alignment regimes.

This model was design to test parameters that affects the number of people shot during mass shooting. This basic formulation places a gunman in a crowd and allows the users to manipulate parameters of the gunman.

The model integrates major theories of political judgment and behavior within the classical cognitive paradigm embedded in the ACT-R cognitive architecture. It models preferences and beliefs of political candidates, parties, and groups.

This is an agent-based model constructed in Netlogo v6.2.2 which seeks to provide a simple but flexible tool for researchers and dog-population managers to help inform management decisions.

It replicates the basic demographic processes including:
* reproduction
* natural death
* dispersal
…

FIBE represents a simple fishery model. Fish that reproduce and fisher with different fishing styles that fish as their main source of income. The aim of the model is to reflect the different fishing behaviours as described and observed in the (Swedish) Baltic Sea fishery and explore the consequences of different approximations of human/fisher behaviour in under different environmental and managerial scenarios.

The overarching aim is to advance the incorporation and understanding of human behaviour (diversity) in fisheries research and management. In particular focusing on insights from social (fishery) science of fisher behaviour.

Chicago’s demographic, neighborhood, sex risk behaviors, sexual network data, and HIV prevention and treatment cascade information from 2015 were integrated as input to a new agent-based model (ABM) called the Levers-of-HIV-Model (LHM). This LHM, written in NetLogo, forms patterns of sexual relations among Men who have Sex with Men (MSM) based on static traits (race/ethnicity, and age) and dynamic states (sexual relations and practices) that are found in Chicago. LHM’s five modules simulate and count new infections at the two marker years of 2023 and 2030 for a wide range of distinct scenarios or levers, in which the levels of PrEP and ART linkage to care, retention, and adherence or viral load are increased over time from the 2015 baseline levels.