The model explores how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect ant colony level foraging in a variable enviroment.

The model studies the dynamics of risk-sharing cooperatives among heterogeneous farmers. Based on their knowledge on their risk exposure and the performance of the cooperative farmers choose whether or not to remain in the risk-sharing agreement.

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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MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.

In our model, individual agents are distributed over a two-dimensional square lattice. The agents play the prisoner’s dilemma game with their neighbors, imitate the highest strategy, and then migrate to empty sites based on their tag preference.

This model explores a social mechanism that links the reversal of the gender gap in education with changing patterns in relative divorce risks in 12 European countries.

The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure

This model examines how financial and social top-down interventions interplay with the internal self-organizing dynamics of a fishing community. The aim is to transform from hierarchical fishbuyer-fisher relationship into fishing cooperatives.

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 three-day participatory workshop organized by the TISSS Lab had 20 participants who were academics in different career stages ranging from university student to professor. For each of the five games, the participants had to move between tables according to some pre-specified rules. After the workshop both the participant’s perception of the games’ complexities and the participants’ satisfaction with the games were recorded.

In order to obtain additional objective measures for the games’ complexities, these games were also simulated using this simulation model here. Therefore, the simulation model is an as-accurate-as-possible reproduction of the workshop games: it has 20 participants moving between 5 different tables. The rules that specify who moves when vary from game to game. Just to get an idea, Game 3 has the rule: “move if you’re sitting next to someone who is waring white or no socks”.

An exact description of the workshop games and the associated simulation models can be found in the paper “The relation between perceived complexity and happiness with decision situations: searching for objective measures in social simulation games”.