What policy measures are effective in redistributing essential resources during crisis situations such as climate change impacts? We model a collective action institution with different rules for designing and organizing it, and make our analysis specific to various societal contexts.

Our model captures a generic societal context of unequal vulnerability and climate change impact in a stylized form. We represent a community of people who harvest and consume an essential resource to maintain their well-being. However, their ability to harvest the resource is not equal; people are characterized by a ‘resource access’ attribute whose values are uniformly distributed from 0 to 1 in the population. A person’s resource access value determines the amount of resource units they are able to harvest, and therefore the welfare levels they are able to attain. People travel to the centralized resource region and derive well-being or welfare, represented as an energy gain, by harvesting and consuming resource units.

The community is subject to a climate change impact event that occurs with a certain periodicity and over a certain duration. The capacity of resource units to regenerate diminishes during the impact events. Unequal capacities to access the essential resource results in unequal vulnerability among people with regards to their ability to maintain a sufficient welfare level, especially during impact events.

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Why do career outcomes in organizations follow highly skewed distributions when the individual traits presumed to drive success — competence, effort, and social skill — are approximately normally distributed in the population? This agent-based model investigates the relative contributions of individual attributes versus random events (“luck”) to career outcomes in hierarchically structured organizations.

The model places 500 agents in a five-level pyramid-shaped organization over 80 six-month evaluation periods (40 simulated years). Each agent is characterized by four attributes drawn from normal distributions at initialization: competence, effort, social skill, and adaptability. In each period, agents may encounter stochastic career events (good or bad projects, supportive or poor managers, market booms or busts), which modify their performance and visibility scores. Promotion decisions are made competitively when vacancies arise at higher levels, using a weighted combination of recent performance, accumulated visibility, network position, and tenure. Social connections form and decay through a proximity-based model influenced by agents’ social skills.

The model extends the talent-versus-luck framework of Pluchino et al. (2018) in four substantive directions: (1) it embeds agents in a hierarchical organization with finite positions at each level, making promotion a zero-sum competition rather than an abstract encounter with random events; (2) it represents individual differences along four independent attribute dimensions rather than a single talent score; (3) it incorporates dynamic social network formation that mediates access to career-relevant opportunities; and (4) it implements the Peter Principle mechanism through which promoted agents must acquire competence appropriate to their new role.

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This model allows for analyzing the most efficient levers for enhancing the use of recycled construction materials, and the role of empirically based decision parameters.

The targeted subsidies plan model is based on the economic concept of targeted subsidies.

The targeted subsidies plan model simulates the distribution of subsidies among households in a community over several years. The model assumes that the government allocates a fixed amount of money each year for the purpose of distributing cash subsidies to eligible households. The eligible households are identified by dividing families into 10 groups based on their income, property, and wealth. The subsidy is distributed to the first four groups, with the first group receiving the highest subsidy amount. The model simulates the impact of the subsidy distribution process on the income and property of households in the community over time.

The model simulates a community of 230 households, each with a household income and wealth that follows a power-law distribution. The number of household members is modeled by a normal distribution. The model allocates a fixed amount of money each year for the purpose of distributing cash subsidies among eligible households. The eligible households are identified by dividing families into 10 groups based on their income, property, and wealth. The subsidy is distributed to the first four groups, with the first group receiving the highest subsidy amount.
The model runs for a period of 10 years, with the subsidy distribution process occurring every month. The subsidy received by each household is assumed to be spent, and a small portion may be saved and added to the household’s property. At the end of each year, the grouping of households based on income and assets is redone, and a number of families may be moved from one group to another based on changes in their income and property.
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STiMUS (Stigmergic–Mutualistic IMOI Model) is an agent-based model of teamwork in socio-technical systems where contributors collaborate through shared digital artefacts — wiki pages, code files, issue tickets, project cards, Scratch projects — represented as patches in a NetLogo world. The model integrates two coordination mechanisms. Stigmergy is indirect coordination through traces left in a shared environment: each edit deposits a pheromone that diffuses to neighbouring patches and evaporates over time, so recent activity attracts further contributions. Mutualism is a reciprocal benefit loop in which valuable, well-maintained artefacts raise contributor motivation and shared understanding, while motivated contributors improve artefacts.

Contributors (turtles of the contributor breed) carry individual state: skill, motivation, shared-mental-model, specialty, benefit-gain, and an explicit-mode flag. At each tick every contributor selects a target artefact with an ant-colony-optimization-style rule weighing the artefact’s pheromone, incompleteness (1 - completeness), resource-value, and topic match between specialty and the artefact’s topic-tag; with probability p-explicit it instead takes the patch with the highest maintenance-need, modelling explicit task assignment. Each edit increases pheromone, quality, completeness and reuse-count, raises resource-value, lowers maintenance-need, and appends the editor to the artefact’s edit-authors list. When the previous last-editor-id differs from the current editor, the Edit Succession Ratio rises, the editor’s shared-mental-model grows, and a co-editing link is created — operationalising the idea that repeated cross-author succession on the same artefact builds shared understanding. Contributors’ motivation is updated from the benefit drawn from the visited artefact.

Each patch maintains a stigmergic layer (pheromone, quality, completeness, recentness, last-editor-id, edit-count, edit-authors) and a mutualistic layer (resource-value, reuse-count, maintenance-need, topic-tag), plus task flags (is-task?, task-complexity). Global monitors report the Edit Succession Ratio (ESR = cross-author-edits / total-edits, and an alternative esr-value = share of edited patches with more than one distinct author), mean-quality, mean-resource-value, a mutualism-index averaging contributor benefit and resource value, coediting-density (network density of the co-editing graph), active-pages-share, and task-completion-rate. The model logs every edit as a bipartite edge (tick, author_id, pageid, specialty, topic_tag, quality), exportable to CSV.

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Perpetual Motion Machine - A simple economy that operates at both a biophysical and economic level, and is sustainable. The goal: to determine the necessary and sufficient conditions of sustainability, and the attendant necessary trade-offs.

Overview

Purpose

Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..
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This model is an agent-based simulation designed to explore how climate-induced environmental degradation can contribute to the emergence of social violence in coastal communities that depend heavily on ecosystem services for their livelihoods. The model represents a coupled social–ecological system in which environmental shocks—such as sea level rise and marine ecosystem decline—affect local economic conditions, food security, and community stability.

Agents in the model represent individuals whose livelihoods depend on coastal ecosystems. Environmental degradation reduces ecosystem productivity and increases economic hardship, which can lead to the formation of grievances among agents. The model incorporates behavioral thresholds that determine how individuals respond to hardship and perceived injustice. Under certain conditions—particularly when institutional capacity and law enforcement effectiveness are limited—these grievances may escalate into violent behavior.

The simulation allows users to explore how different climate scenarios, levels of ecosystem degradation, livelihood dependence, and institutional responses influence the probability of social instability and violence. By modeling the interactions between environmental stress, socio-economic vulnerability, and governance capacity, the model provides a computational framework for examining potential pathways linking climate change and conflict in coastal social–ecological systems.

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