A model to show the effects of flood risk on a housing market; the role of flood protection for risk reduction; the working of the existing public-private flood insurance partnership in the UK, and the proposed scheme ‘Flood Re’.

The purpose of this model is the simulation of social care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. The model simulates care provision as a negotiation process conducted between agents across their kinship networks, with agents with stronger familial relationships to the recipient being more likely to attempt to allocate time to care provision. The model also simulates demographic change, the impact of socioeconomic status, and allows agents to relocate and change jobs or reduce working hours in order to provide care.
Despite the relative lack of empirical data in this model, the model is able to reproduce plausible patterns of social care provision. The inclusion of detailed economic and behavioural mechanisms allows this model to serve as a useful policy development tool; complex behavioural interventions can be implemented in simulation and tested on a virtual population before applying them in real-world contexts.

This model implements a coupled opinion-mobility agent-based framework in NetLogo, extending Attraction-Repulsion Model (ARM) dynamics with endogenous migration in continuous 2D space.

Each agent has an opinion s in [0,1] and a spatial position (x,y). Agents interact locally within an interaction radius, with exposure-controlled interaction probability. Opinion updates follow ARM rules: attraction for small opinion distance and repulsion for large distance (tolerance threshold T). After social interaction, agents move according to a social-force mechanism that balances attraction to similar neighbors and avoidance of dissimilar neighbors, controlled by orientation bias (approaching goods vs leaving bads). The model also includes an optional exposure-mobility coupling setting.

Main outputs include polarization (P), spatial assortativity (Moran’s I), mixed-neighbor fraction (f_mix), and good-component count (N_g). The model is designed to study phase behavior of polarization and segregation under mobility and tolerance heterogeneity.

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This model illustrates actor interaction in the construction sector, according to information gathered in NL. It offers a simple frame to represent diverse interests, interdependencies and effects on the number of built sustainable houses.

The model is an extension of: Carley K. (1991) “A theory of group stability”, American Sociological Review, vol. 56, pp. 331-354.

The original model from Carley (1991) works as follows:
- Agents know or ignore a series of knowledge facts;
- At each time step, each agent i choose a partner j to interact with at random, with a probability of choice proportional to the degree of knowledge facts they have in common.
- Agents interact synchronously. As such, interaction happens only if the partnert j is not already busy interacting with someone else.
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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.

This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.

The goal of the AG-Innovation agent-based model is to explore and compare the effects of two alternative mechanisms of innovation development and diffusion (exogenous, linear and endogenous, non-linear) on emergent properties of food and income distribution and adoption rates of different innovations. The model also assesses the range of conditions under which these two alternative mechanisms would be effective in improving food security and income inequality outcomes. Our modelling questions were: i) How do cross-scalar social-ecological interactions within agricultural innovation systems affect system outcomes of food security and income inequality? ii) Do foreign aid-driven exogenous innovation perpetuate income inequality and food insecurity and if so, under which conditions? iii) Do community-driven endogenous innovations improve food security and income inequality and if so, under which conditions? The Ag-Innovation model is intended to serve as a thinking tool for for the development and testing of hypotheses, generating an understanding of the behavior of agricultural innovation systems, and identifying conditions under which alternated innovation mechanisms would improve food security and income inequality outcomes.

This agent-based model represents a stylized inter-organizational innovation network where firms collaborate with each other in order to generate novel organizational knowledge.

The model proposes a translation of some Luhmann’s concepts (social sub-system, perturbation, dissipation, social communication and power) into a model using a stylized spatial-society as a metaphor of a Luhmann’s social subsystem. The model has been used to improve the social theory understanding and to evaluate the effect of different parameterization in the global stabilization and individual/social power distribution.