The model is an experimental ground to study the impact of network structure on diffusion. It allows to construct a social network that already has some measurable level of homophily, and simulate a diffusion process over this social network.

This NetLogo model simulates the spread of climate change beliefs within a population of individuals. Each believer has an initial belief level, which changes over time due to interactions with other individuals and exposure to media. The aim of the model is to identify possible methods for reducing climate change denial.

The model is designed to analyse the effects of mitigation measures on the European brown hare (Lepus europaeus), which is directly affected by ongoing land use change and has experienced widespread decline throughout Europe since the 1960s. As an input, we use two 4×4 km large model landscapes, which were generated by a landscape generator based on real field sizes and crop proportions and differed in average field size and crop composition. The crops grown annually are evaluated in terms of forage suitability, breeding suitability and crop richness for the hare. Six mitigation scenarios are implemented, defined by a 10 % increase in: (1) mixed silphie, (2) miscanthus, (3) grass-clover ley, (4) alfalfa, (5) set-aside, and (6) general crop richness. The model shows that that both landscape configuration and composition have a significant effect on hare population development, which responds particularly strongly to compositional changes.

This research aims to uncover the micro-mechanisms that drive the macro-level relationship between cultural tolerance and innovation. We focus on the indirect influence of minorities—specifically, workers with diverse domain expertise—within collaboration networks. We propose that minority influence from individuals with different expertise can serve as a key driver of organizational innovation, particularly in dynamic market environments, and that cultural tolerance is critical for enabling such minority-induced innovation. Our model demonstrates that seemingly conflicting empirical patterns between cultural tightness/looseness and innovation can emerge from the same underlying micro-mechanisms, depending on parameter values. A systematic simulation experiment revealed an optimal cultural configuration: a medium level of tolerance (t = 0.6) combined with low consistency (κ = 0.05) produced the fastest adaptation to abrupt market changes. These findings provide evidence that indirect minority influence is a core micro-mechanism linking cultural tolerance to innovation.

This generic individual-based model of a bird colony shows how the influence neighbour’s stress levels synchronize the laying date of neighbours and also of large colonies. The model has been used to demonstrate how this form of simulation model can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions.

This model presents an autonomous, two-lane driving environment with a single lane-closure that can be toggled. The four driving scenarios - two baseline cases (based on the real-world) and two experimental setups - are as follows:

• Baseline-1 is where cars are not informed of the lane closure.
• Baseline-2 is where a Red Zone is marked wherein cars are informed of the lane closure ahead.
• Strategy-1 is where cars use a co-operative driving strategy - FAS. ^[1]
• Strategy-2 is a variant of Strategy-1 and uses comfortable deceleration values instead of the vehicle’s limit.
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This project was developed during the Santa Fe course Introduction to Agent-Based Modeling 2022. The origin is a Cellular Automata (CA) model to simulate human interactions that happen in the real world, from Rubens and Oliveira (2009). These authors used a market research with real people in two different times: one at time zero and the second at time zero plus 4 months (longitudinal market research). They developed an agent-based model whose initial condition was inherited from the results of the first market research response values and evolve it to simulate human interactions with Agent-Based Modeling that led to the values of the second market research, without explicitly imposing rules. Then, compared results of the model with the second market research. The model reached 73.80% accuracy.
In the same way, this project is an Exploratory ABM project that models individuals in a closed society whose behavior depends upon the result of interaction with two neighbors within a radius of interaction, one on the relative “right” and other one on the relative “left”. According to the states (colors) of neighbors, a given cellular automata rule is applied, according to the value set in Chooser. Five states were used here and are defined as levels of quality perception, where red (states 0 and 1) means unhappy, state 3 is neutral and green (states 3 and 4) means happy.
There is also a message passing algorithm in the social network, to analyze the flow and spread of information among nodes. Both the cellular automaton and the message passing algorithms were developed using the Python extension. The model also uses extensions csv and arduino.

This ABM simulates opinions on a topic (originally contested infrastructures) through the interactions between paired agents and based on the sociopsychological assumptions of social judgment theory (SJT; Sherif & Hovland, 1961).

This model is a spatial evolutionary game theory model in which animals with inherited red or blue phenotypes move, interact locally, gain or lose energy based on payoff structure, and reproduce across generations. Plants provide a stationary green component of the environment. The model can be used to examine how game payoffs, inheritance, and survival shape population and evolutionary dynamics over time.

The model provides instruments for the simulation of interbank network evolution. There are tools for dynamic network analysis, allowing to evaluate graph topological invariants, thermodynamic network features and combinational node-based features.