The model simulates agents behaviour in wine market parallel trading systems: auctions, OTC and Liv-ex. Models are written in JAVA and use MASON framework. To run a simulation download source files with additional src folder with sobol.csv file. In WineSimulation.java set RESULTS_FOLDER parameter. Uses following external libraries mason19..jar, opencsv.jar, commons-lang3-3.5.jar and commons-math3-3.6.1.jar.

This model simulates a group of farmers that have encounters with individuals of a wildlife population. Each farmer owns a set of cells that represent their farm. Each farmer must decide what cells inside their farm will be used to produce an agricultural good that is self in an external market at a given price. The farmer must decide to protect the farm from potential encounters with individuals of the wildlife population. This decision in the model is called “fencing”. Each time that a cell is fenced, the chances of a wildlife individual to move to that cell is reduced. Each encounter reduces the productive outcome obtained of the affected cell. Farmers, therefore, can reduce the risk of encounters by exclusion. The decision of excluding wildlife is made considering the perception of risk of encounters. In the model, the perception of risk is subjective, as it depends on past encounters and on the perception of risk from other farmers in the community. The community of farmers passes information about this risk perception through a social network. The user (observer) of the model can control the importance of the social network on the individual perception of risk.

I added a discounting rate to the equation for expected values of defective / collaborative strategies.
The discounting rate was set to 0.956, the annual average from 1980 to 2015, using the Consumer Price Index (CPI) of Statistics Korea.

The current rate of production and consumption of meat poses a problem both to peoples’ health and to the environment. This work aims to develop a simulation of peoples’ meat consumption behaviour in Britain using agent-based modelling. The agents represent individual consumers. The key variables that characterise agents include sex, age, monthly income, perception of the living cost, and concerns about the impact of meat on the environment, health, and animal welfare. A process of peer influence is modelled with respect to the agents’ concerns. Influence spreads across two eating networks (i.e. co-workers and household members) depending on the time of day, day of the week, and agents’ employment status. Data from a representative sample of British consumers is used to empirically ground the model. Different experiments are run simulating interventions of application of social marketing campaigns and a rise in price of meat. The main outcome is the average weekly consumption of meat per consumer. A secondary outcome is the likelihood of eating meat.

RHEA aims to provide a methodological platform to simulate the aggregated impact of households’ residential location choice and dynamic risk perceptions in response to flooding on urban land markets. It integrates adaptive behaviour into the spatial landscape using behavioural theories and empirical data sources. The platform can be used to assess: how changes in households’ preferences or risk perceptions capitalize in property values, how price dynamics in the housing market affect spatial demographics in hazard-prone urban areas, how structural non-marginal shifts in land markets emerge from the bottom up, and how economic land use systems react to climate change. RHEA allows direct modelling of interactions of many heterogeneous agents in a land market over a heterogeneous spatial landscape. As other ABMs of markets it helps to understand how aggregated patterns and economic indices result from many individual interactions of economic agents.
The model could be used by scientists to explore the impact of climate change and increased flood risk on urban resilience, and the effect of various behavioural assumptions on the choices that people make in response to flood risk. It can be used by policy-makers to explore the aggregated impact of climate adaptation policies aimed at minimizing flood damages and the social costs of flood risk.

The model is a representation of a liberalised electricity market designed as an energy-only market and consists of large scale investors and their power generation assets in the electricity market.

This model simulate product diffusion on different social network structures.

The purpose of the presented ABM is to explore how system resilience is affected by external disturbances and internal dynamics by using the stylized model of an agricultural land use system.

We explore land system resilience with a stylized land use model in which agents’ land use activities are affected by external shocks, agent interactions, and endogenous feedbacks. External shocks are designed as yield loss in crops, which is ubiquitous in almost every land use system where perturbations can occur due to e.g. extreme weather conditions or diseases. Agent interactions are designed as the transfer of buffer capacity from farmers who can and are willing to provide help to other farmers within their social network. For endogenous feedbacks, we consider land use as an economic activity which is regulated by markets — an increase in crop production results in lower price (a negative feedback) and an agglomeration of a land use results in lower production costs for the land use type (a positive feedback).

This model explores a price Q-learning mechanism for perishable products that considers uncertain demand and customer preferences in a competitive multi-agent retailer market (a model-free environment).

The modeling includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The model is spatial and dynamic.