This is model that simulates how multiple kinds of peer effects shape the diffusion of innovations through different types of social relationships.

Agent-based model of intergroup conflict in divided communities.

A group of agents share a resource and agents will become sufficiently motivated to adopt a rule to constraint their freedom if they experience resource scarcity and developed mutual trust relationships.

The program simulate the functioning of an italian health and social public information office (SPUN) on the basis of the real data collected in the first five years of functioning.

The integrated and spatially-explicit ABM, called DIReC (Demography, Industry and Residential Choice), has been developed for Aberdeen City and the surrounding Aberdeenshire (Ge, Polhill, Craig, & Liu, 2018). The model includes demographic (individual and household) models, housing infrastructure and occupancy, neighbourhood quality and evolution, employment and labour market, business relocation, industrial structure, income distribution and macroeconomic indicators. DIReC includes a detailed spatial housing model, basing preference models on house attributes and multi-dimensional neighbourhood qualities (education, crime, employment etc.).
The dynamic ABM simulates the interactions between individuals, households, the labour market, businesses and services, neighbourhoods and economic structures. It is empirically grounded using multiple data sources, such as income and gender-age distribution across industries, neighbourhood attributes, business locations, and housing transactions. It has been used to study the impact of economic shocks and structural changes, such as the crash of oil price in 2014 (the Aberdeen economy heavily relies on the gas and oil sector) and the city’s transition from resource-based to a green economy (Ge, Polhill, Craig, & Liu, 2018).

The code shared here accompanies the paper at https://doi.org/10.1371/journal.pone.0208451. It simulates the effects of various economic trade scenarios on the phenomenon of the ‘disappearing middle’ in the Scottish beef and dairy farming industries. The ‘disappearing middle’ is a situation in which there is a simultaneous observed decline in medium-sized enterprises and rise in the number of small and large-scale enterprises.

The model’s purpose is to provide a potential explanation for the emergence, sustenance and decline of unpopular norms based on pluralistic ignorance on a social network.

LogoClim is a NetLogo model for simulating and visualizing global climate conditions. It allows researchers to integrate high-resolution climate data into agent-based models, supporting reproducible research in ecology, agriculture, environmental sciences, and other fields that rely on climate data.

The model utilizes raster data to represent climate variables such as temperature and precipitation over time. It incorporates historical data (1951-2024) and future climate projections (2021-2100) derived from global climate models under various Shared Socioeconomic Pathways (SSPs, O’Neill et al., 2017). All climate inputs come from WorldClim 2.1, a widely used source of high-resolution, interpolated climate datasets based on weather station observations worldwide (Fick & Hijmans, 2017).

LogoClim follows the FAIR Principles for Research Software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub. See the Logônia model for an example of its integration into a full NetLogo simulation.

Our model shows how disinformation spreads on a random network of individuals. The network is weighted and directed. We are looking at how different factors affect how fast, or how many people get “infected” with the misinformation. One of the main factors that we were curious about was perceived trustworthiness. This is because we want to see if people of power, or a high degree of perceived trustworthiness, were able to push misinformation to more people and convert more people to believe the information.

The agent-based simulation of innovation diffusion is based on the idea of the Bass model (1969).

The adoption of an agent is driven two parameters: its innovativess p and its prospensity to conform with others. The model is designed for a computational experiment building up on the following four model variations:

(i) the agent population it fully connected and all agents share the same parameter values for p and q
(ii) the agent population it fully connected and agents are heterogeneous, i.e. individual parameter values are drawn from a normal distribution
(iii) the agents population is embeded in a social network and all agents share the same parameter values for p and q
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