WatASit is an agent-based model implemented in the CORMAS plateform. The model is developped to simulate irrigation situations at the operational level during a collective irrigation campaign.

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

We employ an agent-based model (ABM) to simulate the policy implementation for shaping specific land use patterns. Detailed information will be presented after the journal paper is published.

The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.

The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).

This model implements two types of network diffusion from an initial group of activated nodes. In complex contagion, a node is activated if the proportion of neighbour nodes that are already activated exceeds a given threshold. This is intended to represented the spread of health behaviours. In simple contagion, an activated node has a given probability of activating its inactive neighbours and re-tests each time step until all of the neighbours are activated. This is intended to represent information spread.

A range of networks are included with the model from secondary school friendship networks. The proportion of nodes initially activated and the method of selecting those nodes are controlled by the user.

This model is an abstract simulation of the COVID-19 virus in the United States population. It demonstrates how different masks of different types affect the progress of the virus.

Using data from the British Social Attitude Survey, we develop an agent-based model to study the effect of social influence on the spread of meat-eating behaviour in the British population.

The purpose of the model is to provide an analogy for how the Schwartz values may influence the aggregated economic performance, as measured by: public goods provision, private goods provision and leisure time.

How natural population ageing affects UK household spending patterns.

The purpose of the study is to unpack and explore a potentially beneficial role of sharing metacognitive information within a group when making repeated decisions about common pool resource (CPR) use.

We explore the explanatory power of sharing metacognition by varying (a) the individual errors in judgement (myside-bias); (b) the ways of reaching a collective judgement (metacognition-dependent), (c) individual knowledge updating (metacognition- dependent) and d) the decision making context.

The model (AgentEx-Meta) represents an extension to an existing and validated model reflecting behavioural CPR laboratory experiments (Schill, Lindahl & Crépin, 2015; Lindahl, Crépin & Schill, 2016). AgentEx-Meta allows us to systematically vary the extent to which metacognitive information is available to agents, and to explore the boundary conditions of group benefits of metacognitive information.