Ge, J., & Polhill, G. (2016). Exploring the Combined Impact of Factors Influencing Commuting Patterns and CO2 Emission in Aberdeen Using an Agent-Based Model. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/11.html
We develop an agent-based transport model using a realistic GIS-enabled road network and the car following method. The model can be used to study the impact of social interventions such as flexi-time and workplace sharing, as well as large infrastructure such as the construction of a bypass or highway. The model is developed in Netlogo version 5 and requires road network data in GIS format to run.

CEDSS is an agent-based model of domestic energy demand at the level of a small community.

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 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).

Protein 2.0 is a systems model of the Norwegian protein sector designed to explore the potential impacts of carbon taxation and the emergence of cultivated meat and dairy technologies. The model simulates production, pricing, and consumption dynamics across conventional and cultivated protein sources, accounting for emissions intensity, technological learning, economies of scale, and agent behaviour. It assesses how carbon pricing could alter the competitiveness of conventional beef, lamb, pork, chicken, milk, and egg production relative to emerging cultivated alternatives, and evaluates the implications for domestic production, emissions, and food system resilience. The model provides a flexible platform for exploring policy scenarios and transition pathways in protein supply. Further details can be found in the associated publication.

An agent-based model simulates emergence of in-group favoritism. Agents adopt friend selection strategies using an invariable tag and reputations meaning how cooperative others are to a group. The reputation can be seen as a kind of public opinion.

This is a coupled conceptual model of agricultural land decision-making and incentivisation and species metacommunities.

The provided source code is the result of our efforts in replicating Epstein’s Demographic Prisoner’s Dilemma. The simulation model is written in Repast/J 3.1.

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.
…

Reconstruction of the original code M. Cohen, J. March, and J. Olsen garbage can model, realized by means of Microsoft Office Excel 2010