The Friendship Field model aims at modelling friendship formation based on three factors: Extraversion, Resemblance and Status, where social interaction is motivated by the Social Battery. Social Battery is one’s energy and motivation to engage in social contact. Since social contact is crucial for friendship formation, the model included Social Battery to affect social interactions. To our best knowledge, Social Battery is a yet unintroduced concept in research while it is a dynamic factor influencing the social interaction besides one’s characteristics. Extraverts’ Social Batteries charge while interacting and exhaust while being alone. Introverts’ Social Batteries charge while being alone and exhaust while interacting. The aim of the model is to illustrate the concept of Social Battery. Moreover, the Friendship Field shows patterns regarding Extraversion, Resemblance and Status including the mere-exposure effect and friendship by similarity. For the implementation of Status, Kemper’s status-power theory is used. The concept of Social Battery is also linked to Kemper’s theory on the organism as reference group. By running the model for a year (3 interactions moments per day), the friendship dynamics over time can be studied.

We presented the model at the Social Simulation Conference 2022.

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.

Municipal waste management (MWM) is essential for urban development. Efficient waste management is essential for providing a healthy and clean environment, for reducing GHGs and for increasing the amount of material recycled. Waste separation at source is perceived as an effective MWM strategy that relays on the behaviour of citizens to separate their waste in different fractions. The strategy is straightforward, and many cities have adopted the strategy or are working to implement it. However, the success of such strategy depends on adequate understanding of the drivers of the behaviour of proper waste sorting. The Theory of Planned Behaviour (TPB) has been extensively applied to explain the behaviour of waste sorting and contributes to determining the importance of different psychological constructs. Although, evidence shows its validity in different contexts, without exploring how urban policies and the built environment affect the TPB, its application to urban challenges remains unlocked. To date, limited research has focused in exposing how different urban situations such as: distance to waste bins, conditions of recycling facilities or information campaigns affect the planned behaviour of waste separation. To fill this gap, an agent-based model (ABM) of residents capable of planning the behaviour of waste separation is developed. The study is a proof of concept that shows how the TPB can be combined with simulations to provide useful insights to evaluate different urban planning situations. In this paper we depart from a survey to capture TPB constructs, then Structural Equation Modelling (SEM) is used to validate the TPB hypothesis and extract the drivers of the behaviour of waste sorting. Finally, the development of the ABM is detailed and the drivers of the TPB are used to determine how the residents behave. A low-density and a high-density urban scenario are used to extract policy insights. In conclusion, the integration between the TPB into ABMs can help to bridge the knowledge gap between can provide a useful insight to analysing and evaluating waste management scenarios in urban areas. By better understanding individual waste sorting behaviour, we can develop more effective policies and interventions to promote sustainable waste management practices.

This is a model of coherency based belief within a dynamic network of individuals. Described in an invited talk on workshop on Coherence, Berlin, 9th July 2016.

Signaling chains are a special case of Lewis’ signaling games on networks. In a signaling chain, a sender tries to send a single unit of information to a receiver through a chain of players that do not share a common signaling system.

This is a simulation model of an intelligent agent that has the objective to learn sustainable management of a renewable resource, such as a fish stock.

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.

Agriculture is the largest water-consuming sector worldwide, responsible for almost 70% of the world’s total freshwater consumption. Agricultural water reuse is one of the most sustainable and reliable methods to alleviate water shortages worldwide. However, the dynamics of agricultural water reuse adoption by farmers and its impacts on local water resources are still unknown to the scientific community, according to the literature. Therefore, the primary purpose of the WRAF model is to investigate the micro-level dynamics of agricultural water reuse adoption by farmers and its impacts on local water resources. The WRAF was developed using agent-based modeling as an exploratory tool for scenario analysis. The model was specifically designed for researchers and water resources decision-makers, especially those interested in natural resources management and water reuse.
WRAF simulates a virtual agricultural area in which several autonomous farms operate. It also simulates these farms’ water consumption dynamics. The developed model includes two types of agents: farmers and wastewater treatment plants. In general, farmer agents are the main water-consuming agents, and wastewater treatment plant agents are recycled water providers in the WRAF model. Dynamic simulation of agricultural water supply and demand in the area allows the user to observe the results of various irrigation water management scenarios, including recycled water. The models also enable the user to apply multiple climate change scenarios, including normal, moderate drought, severe drought, and wet weather conditions.

The basic idea behind developing MIXTRUST was to represent a network of agricultural stakeholders composed of farmers and a cooperative in a mixed landscape to test its performances in response to risks. A mixed landscape here is a landscape where crop and livestock systems interact by the intermediary of material flows of agricultural products. It can be within mixed farms, or between farms, often specialized, (e.g. straw-manure).

This is an initial exploratory exercise done for the class @ http://thiagomarzagao.com/teaching/ipea/ Text available here: https://arxiv.org/abs/1712.04429v1
The program:
Reads output from an ABM model and its parameters’ configuration
Creates a socioeconomic optimal output based on two ABM results of the modelers choice
Organizes the data as X and Y matrices
Trains some Machine Learning algorithms
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