The “Descriptive Norm and Fraud Dynamics” model demonstrates how fraudulent behavior can either proliferate or be contained within non-hierarchical organizations, such as peer networks, through social influence taking the form of a descriptive norm. This model expands on the fraud triangle theory, which posits that an individual must concurrently possess a financial motive, perceive an opportunity, and hold a pro-fraud attitude to engage in fraudulent activities (red agent). In the absence of any of these elements, the individual will act honestly (green agent).

The model explores variations in a descriptive norm mechanism, ranging from local distorted knowledge to global perfect knowledge. In the case of local distorted knowledge, agents primarily rely on information from their first-degree colleagues. This knowledge is often distorted because agents are slow to update their empirical expectations, which are only partially revised after one-to-one interactions. On the other end of the spectrum, local perfect knowledge is achieved by incorporating a secondary source of information into the agents’ decision-making process. Here, accurate information provided by an observer is used to update empirical expectations.

The model shows that the same variation of the descriptive norm mechanism could lead to varying aggregate fraud levels across different fraud categories. Two empirically measured norm sensitivity distributions associated with different fraud categories can be selected into the model to see the different aggregate outcomes.

The model is a microsimulation, where the agents don’t Interact with each other. It simulates income distribution, unemployment dynamics, education, and Family grant in Brazil, focusing on the impact on social inequality. It tracks the indicators Gini index, Lorenz curve, and Palma ratio. The objective is to explore how these factors influence wealth distribution and social inequality over time.
This work was developed in partnership with the Graduate Program in Computational Modeling, in the Universidade Federal do Rio Grande - FURG, in Brazil.

AGENTS model is an agent-based computational framework designed to explore the socio-ecological and economic dynamics of agricultural production in the Byzantine Negev Highlands, with a focus on viticulture. It integrates historical, environmental, and social factors to simulate settlement sustainability, crop yields, and the impacts of varying climate conditions. The model is built in NetLogo and incorporates GIS-based topographical and hydrological data. Key features include the ability to assess climate impacts on crop profitability and settlement strategies, evaluate economic outputs of ancient vineyards, and simulate agent decision-making processes under diverse scenarios.

The AGENTS model is highly flexible, enabling users to simulate agricultural regimes with any two crops: one cash crop (a crop grown for profit, e.g., grapevines) and one staple crop (a crop grown for subsistence, e.g., wheat). While the default setup models viticulture and wheat cultivation in the Byzantine Negev Highlands, users can adapt the model to different environmental and socio-ecological contexts worldwide—both past and present.

Users can load external files to customize precipitation, evaporation, topography, and labor costs (measured as man-days per 0.1ha, converted to kg of wheat per model patch size area), and can also edit key parameters related to yield calculations. This includes modifying crop-specific yield formulas, soil and runoff indices, and any factors influencing crop performance. The model inherently simulates cash crops grown in floodplain regions and staple crops cultivated along riverbanks, providing a powerful tool to investigate societal resilience and responses to climate stressors across diverse environments.

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For deep decarbonisation, the design of climate policy needs to account for consumption choices being influenced not only by pricing but also by social learning. This involves changes that pertain to the whole spectrum of consumption, possibly involving shifts in lifestyles. In this regard, it is crucial to consider not just short-term social learning processes but also slower, longer-term, cultural change. Against this background, we analyse the interaction between climate policy and cultural change, focusing on carbon taxation. We extend the notion of “social multiplier” of environmental policy derived in an earlier study to the context of multiple consumer needs while allowing for behavioural spillovers between these, giving rise to a “cultural multiplier”. We develop a model to assess how this cultural multiplier contributes to the effectiveness of carbon taxation. Our results show that the cultural multiplier stimulates greater low-carbon consumption compared to fixed preferences. The model results are of particular relevance for policy acceptance due to the cultural multiplier being most effective at low-carbon tax values, relative to a counter-case of short-term social interactions. Notably, at high carbon tax levels, the distinction between social and cultural multiplier effects diminishes, as the strong price signal drives even resistant individuals toward low-carbon consumption. By varying socio-economic conditions, such as substitutability between low- and high-carbon goods, social network structure, proximity of like-minded individuals and the richness of consumption lifestyles, the model provides insight into how cultural change can be leveraged to induce maximum effectiveness of climate policy.

Educational attainment and student retention in higher education are two of the main focuses of higher education research. Institutions in the U.S. are constantly looking for ways to identify areas of improvement across different aspects of the student experience on university campuses. This paper combines Department of Education data, U.S. Census data, and higher education theory on student retention, to build an agent-based model of student behavior.

Reducing packaging waste is a critical challenge that requires organizations to collaborate within circular ecosystems, considering social, economic, and technical variables like decision-making behavior, material prices, and available technologies. Agent-Based Modeling (ABM) offers a valuable methodology for understanding these complex dynamics. In our research, we have developed an ABM to explore circular ecosystems’ potential in reducing packaging waste, using a case study of the Dutch food packaging ecosystem. The model incorporates three types of agents—beverage producers, packaging producers, and waste treaters—who can form closed-loop recycling systems.

Beverage Producer Agents: These agents represent the beverage company divided into five types based on packaging formats: cans, PET bottles, glass bottles, cartons, and bag-in-boxes. Each producer has specific packaging demands based on product volume, type, weight, and reuse potential. They select packaging suppliers annually, guided by deterministic decision styles: bargaining (seeking the lowest price) or problem-solving (prioritizing high recycled content).

Packaging Producer Agents: These agents are responsible for creating packaging using either recycled or virgin materials. The model assumes a mix of monopolistic and competitive market situations, with agents calculating annual material needs. Decision styles influence their choices: bargaining agents compare recycled and virgin material costs, while problem-solving agents prioritize maximum recycled content. They calculate recycled content in packaging and set prices accordingly, ensuring all produced packaging is sold within or outside the model.

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Hybrid attacks coordinate the exploitation of vulnerabilities across domains to undermine trust in authorities and cause social unrest. Whilst such attacks have primarily been seen in active conflict zones, there is growing concern about the potential harm that can be caused by hybrid attacks more generally and a desire to discover how better to identify and react to them. In addressing such threats, it is important to be able to identify and understand an adversary’s behaviour. Game theory is the approach predominantly used in security and defence literature for this purpose. However, the underlying rationality assumption, the equilibrium concept of game theory, as well as the need to make simplifying assumptions can limit its use in the study of emerging threats. To study hybrid threats, we present a novel agent-based model in which, for the first time, agents use reinforcement learning to inform their decisions. This model allows us to investigate the behavioural strategies of threat agents with hybrid attack capabilities as well as their broader impact on the behaviours and opinions of other agents.

The Non-Deterministic model of affordable housing Negotiations (NoD-Neg) is designed for generating hypotheses about the possible outcomes of negotiating affordable housing obligations in new developments in England. By outcomes we mean, the probabilities of failing the negotiation and/or the different possibilities of agreement.
The model focuses on two negotiations which are key in the provision of affordable housing. The first is between a developer (DEV) who is submitting a planning application for approval and the relevant Local Planning Authority (LPA) who is responsible for reviewing the application and enforcing the affordable housing obligations. The second negotiation is between the developer and a Registered Social Landlord (RSL) who buys the affordable units from the developer and rents them out. They can negotiate the price of selling the affordable units to the RSL.
The model runs the two negotiations on the same development project several times to enable agents representing stakeholders to apply different negotiation tactics (different agendas and concession-making tactics), hence, explore the different possibilities of outcomes.
The model produces three types of outputs: (i) histograms showing the distribution of the negotiation outcomes in all the simulation runs and the probability of each outcome; (ii) a data file with the exact values shown in the histograms; and (iii) a conversation log detailing the exchange of messages between agents in each simulation run.

Model of influence of access to social information spread via social network on decisions in a two-person game.

The model is designed to simulate the behavior and decision-making processes of individuals (agents) in a social network. It aims to represent the changes in individual probability to take any action based on changes in attributes. The action is anything that can be reasonably influenced by the three influencing methods implemented in this model: peer pressure, social media, and state campaigns, and for which the user has a decision-making model. The model is implemented in the multi-agent programmable environment NetLogo 6.3.0.