The model reproduces the spread of environmental awareness among agents and the impact of awareness level of the agents on the consumption of a resource, like energy. An agent is a household with a set of available advanced smart metering functions.

The model combines the two elements of disorganization and motivation to explore their impact on teams. Effects of disorganization on team task performance (problem solving)

The model aims to investigate the role of Microfinance Institutes (MFIs) in strengthening the coping capacity of slum-dwellers (residents) in case of frequent disasters. The main purpose of the model is system understanding. It aids in understanding the following research question: Are the microcredits provided by MFI to start a small business helpful in increasing coping capacity of a slum dweller for recovering from frequent and intense disasters?

DARTS simulates food systems in which agents produce, consume and trade food. Here, food is a summary item that roughly corresponds to commodity food types (e.g. rice). No other food types are taken into account. Each food system (World) consists of its own distribution of agents, regions and connections between agents. Agents differ in their ability to produce food, earn off-farm income and trade food. The agents aim to satisfy their food requirements (which are fixed and equal across agents) by either their own food production or by food purchases. Each simulation step represents one month, in which agents can produce (if they have productive capacity and it is a harvest month for their region), earn off-farm income, trade food (both buy and sell) and consume food. We evaluate the performance of the food system by averaging the agents’ food satisfaction, which is defined as the ratio of the food consumed by each agent at the end of each month divided by her food requirement. At each step, any of the abovementioned attributes related to the agents’ ability to satisfy their food requirement can (temporarily) be shocked. These shocks include reducing the amount of food they produce, removing their ability to trade locally or internationally and reducing their cash savings. Food satisfaction is quantified (both immediately after the shock and in the year following the shock) to evaluate food security of a particular food system, both at the level of agent types (e.g. the urban poor and the rural poor) and at the systems level. Thus, the effects of shocks on food security can be related to the food system’s structure.

The main function of this simulation model is to simulate the onset of individual panic in the context of a public health event, and in particular to simulate how an individual’s panic develops and dies out in the context of a dual information contact network of online social media information and offline in-person perception information. In this model, eight different scenarios are set up by adjusting key parameters according to the difference in the amount and nature of information circulating in the dual information network, in order to observe how the agent’s panic behavior will change under different information exposure situations.

An empirically calibrated agent-based model of cooperation among 14 EU member states. Adaptive state-agents update their cooperation propensity through behavioural inertia, influence along the observed intra-EU trade network (IMF bilateral flows), and repeated-game payoff indicators built from verified Eurostat, Eurobarometer and IMF data (2021-2024). An anchored logistic mapping makes the observed configuration stationary in the absence of shocks, so outcomes read as deviations from the empirical baseline. The model stress-tests European cooperation to 2040 under five scenarios of increasing severity, from a baseline to a Taiwan Strait crisis counterfactual, with 1,000 Monte Carlo replications and a full sensitivity suite (one-factor-at-a-time, joint parameter sampling, breaking-point analysis, alternative functional form). Documented with the ODD protocol; self-testing and fully reproducible under fixed seeds.

Agent-based model of cooperation among fourteen EU member states under geopolitical shocks, 2024-2040. States are adaptive agents on the observed intra-EU trade network, updating a cooperation propensity through inertia, network influence, and a repeated-game payoff structure built from verified Eurostat, Eurobarometer and IMF data. An anchoring device makes the observed 2024 configuration exactly stationary absent shocks, so results are read as deviations from an empirical baseline. Five scenarios of increasing severity are simulated with Monte Carlo replication, up to an extreme counterfactual crisis in the Taiwan Strait. Includes six robustness batteries and a full ODD description.

The Oussa Pañn model is an agent-based representation of the social-ecological system embedded in the Oussa Pañn role-playing game. It represents shellfish harvesters making individual livelihood decisions between harvesting renewable shellfish resources from mudflats and engaging in alternative income-generating activities in a village. The model was developed to (i) compare simulated and observed game trajectories, (ii) verify the internal consistency of recorded game data, and (iii) provide a baseline against which observed player behaviour can be compared, notably through random decision-making scenarios.

This model tests whether local housing supply elasticity governs crash severity inside a single metropolitan housing market. Saiz (2010) established that across US metros, regions constrained by geography and regulation experience deeper boom-bust cycles than flexible ones. That finding is routinely applied downward to neighborhoods and ZIP codes as though the mechanism scaled without qualification.

The empirical record for the Washington DC and Northern Virginia region says it does not. Across 84 ZIP codes, measured supply elasticity ranges from 0.35 to 4.95 with a median of 1.21. The worst single-year price decline between 2007 and 2012 averaged 9.6 percent in constrained ZIP codes and 9.0 percent in flexible ones, a gap that cannot be distinguished from noise. Wide variation in the proposed cause, no meaningful separation in the proposed effect.

The model embeds households, houses and a metro-wide credit condition in the real ZIP geography of the region using three GIS layers and an empirical price panel. Local elasticity governs construction, exactly as theory predicts. Prices are driven by a shared macro drift schedule and, under the credit-amplified mode, by a leverage cycle with a financial accelerator and a deviation penalty. The design question is whether those shared forces are sufficient to override local supply differences at the sub-metropolitan scale.

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This model builds on inquisitiveness as a key individual disposition to expand the bounds of their rationality. It represents a system where teams are formed around problems and inquisitive agents integrate competencies to find ‘emergent’ solutions.