This model aims to explore how gambling-like behavior can emerge in loot box spending within gaming communities. A loot box is a purchasable mystery box that randomly awards the player a series of in-game items. Since the contents of the box are largely up to chance, many players can fall into a compulsion loop of purchasing, as the fear of missing out and belief in the gambler’s fallacy allow one to rationalize repeated purchases, especially when one compares their own luck to others. To simulate this behavior, this model generates players in different network structures to observe how factors such as network connectivity, a player’s internal decision making strategy, or even common manipulations games use these days may influence a player’s transactions.

We use an agent-based 3D model to reveal the behavioral dynamics of real-world cases. The target of the simulation is the Peshawar massacre. The previous 2-D model has three main problems which can be solved by our 3-D model. Under the key action rules, our model matches the real target case exactly. Based on the optimal solution, we precisely match the results of the real cases, such as the number of deaths and injuries. We also explore the importance of adding height (constructed as a 3D model) to the model.

The goal of the paper is to propose an abstract but formalised model of how Schwartz higher order values may influence individual decisions on sharing an individual effort among alternative economic activities. Subsequently, individual decisions are aggregated into the total (collective) economic output, taking into account interactions between the agents. In particular, we explore the relationship between individual higher order values: Self–Enhancement, Self–Transcendence, Openness to Change, and Conservation – measured according to Schwartz’s universal human values theory – and individual and collective economic performance, by means of a theoretical agent based model. Furthermore, based on empirical observations, Openness to Change (measured by the population average in the case of collective output) is positively associated with individual and collective output. These relations are negative for Conservation. Self-Enhancement is positively associated with individual output but negatively with collective output. In case of Self–Transcendence, this effect is opposite. The model provides the potential explanations, in terms of individual and population differences in: propensity for management, willingness to change, and skills (measured by an educational level) for the empirically observed relations between Schwartz higher order values and individual and collective output. We directly calibrate the micro–level of the model using data from the ninth round of the European Social Survey (ESS9) and present the results of numerical simulations.

The SAFIRe model (Simulation of Agents for Fertility, Integrated Energy, Food Security, and Reforestation) is an agent-based model co-developed with rural communities in Senegal’s Groundnut Basin. Its purpose is to explore how local farming and pastoral practices affect the regeneration of Faidherbia albida trees, which are essential for maintaining soil fertility and supporting food security through improved millet production. The model supports collective reflection on how different social and ecological factors interact, particularly around firewood demand, livestock pressure, and agricultural intensification.

The model simulates a 100-hectare agricultural landscape where agents (farmers, shepherds, woodcutters, and supervisors) interact with trees, land parcels, and each other. It incorporates seasonality, crop rotation, tree growth and cutting, livestock feeding behaviors, and farmers’ engagement in sapling protection through Assisted Natural Regeneration (ANR). Two types of surveillance strategies are compared: community-led monitoring and delegated surveillance by forestry authorities. Farmer engagement evolves over time based on peer influence, meeting participation, and the success of visible tree regeneration efforts.

SAFIRe integrates participatory modeling (ComMod and ComExp) and a backcasting approach (ACARDI) to co-produce scenarios rooted in local aspirations. It was explored using the OpenMole platform, allowing stakeholders to test a wide range of future trajectories and analyze the sensitivity of key parameters (e.g., discussion frequency, time in fields). The model’s outcomes not only revealed unexpected insights—such as the hidden role of farmers in tree loss—but also led to real-world actions, including community nursery creation and behavioral shifts toward tree care. SAFIRe illustrates how agent-based modeling can become a tool for social learning and collective action in socio-ecological systems.

The objective of this study is to create a framework to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products.

The model explores the emergence of inequality in cognitive and socio-emotional skills at the societal level within and across generations that results from differences in parental investment behavior during childhood and adolescence.

The Regional Security Game is a iterated public goods game with punishement based on based on life sciences work by Boyd et al. (2003 ) and Hintze & Adami (2015 ), with modifications appropriate for an international relations setting. The game models a closed regional system in which states compete over the distribution of common security benefits. Drawing on recent work applying cultural evolutionary paradigms in the social sciences, states learn through imitation of successful strategies rather than making instrumentally rational choices. The model includes the option to fit empirical data to the model, with two case studies included: Europe in 1933 on the verge of war and south-east Asia in 2013.

Confirmation Bias is usually seen as a flaw of the human mind. However, in some tasks, it may also increase performance. Here, agents are confronted with a number of binary Signals (A, or B). They have a base detection rate, e.g. 50%, and after they detected one signal, they get biased towards this type of signal. This means, that they observe that kind of signal a bit better, and the other signal a bit worse. This is moderated by a variable called “bias_effect”, e.g. 10%. So an agent who detects A first, gets biased towards A and then improves its chance to detect A-signals by 10%. Thus, this agent detects A-Signals with the probability of 50%+10% = 60% and detects B-Signals with the probability of 50%-10% = 40%.
Given such a framework, agents that have the ability to be biased have better results in most of the scenarios.

This model simulates economic and epidemiological interaction between citrus production and the disease Huanglongbing (HLB), which is vectored by the Asian citrus psyllid. The model is used to evaluate area-wide coordinated spraying when free-riding is possible given individuals’ beliefs in other grower participation in area-wide spraying and in the information provided by extension on the threat as HLB spread.

A simple model is constructed using C# in order to to capture key features of market dynamics, while also producing reasonable results for the individual insurers. A replication of Taylor’s model is also constructed in order to compare results with the new premium setting mechanism. To enable the comparison of the two premium mechanisms, the rest of the model set-up is maintained as in the Taylor model. As in the Taylor example, homogeneous customers represented as a total market exposure which is allocated amongst the insurers.

In each time period, the model undergoes the following steps:
1. Insurers set competitive premiums per exposure unit
2. Losses are generated based on each insurer’s share of the market exposure
3. Accounting results are calculated for each insurer
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