This computational model is an agent-based model (ABM) developed to investigate how repeated failures of emerging niches accumulate and influence the trajectory of socio-technical transitions. Built in AnyLogic 8.7.11, the model simulates the dynamic interactions between a dominant regime and sequential niche entrants within a two-dimensional practice space. It models alignment, movement, and competition based on technological maturity and market penetration. The model utilizes a reinforcing feedback structure linking consumer support, output, resource accumulation, and capacity development (Physical and Institutional Capacity). A complete model specification following the ODD+D (Overview, Design concepts, Details, and Decision) protocol is included in the documentation.

This model is a minimal agent-based model (ABM) of green consumption and market tipping dynamics in a stylised two-firm economy. It is designed as an existence proof to illustrate how weak individual preferences, when combined with habit formation, social influence, and firm price adaptation, can generate non-linear transitions (tipping points) in market outcomes.

The economy consists of:
1) Two firms, each supplying a differentiated consumption bundle that differs in its fixed green share (one relatively greener, one less green).
2) Many households, each consuming a unit mass per period and allocating consumption between the two firms.

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In his 2003 book, Historical Dynamics (ch. 4), Turchin describes and briefly analyzes a spatial ABM of his metaethnic frontier theory, which is essentially a formalization of a theory by Ibn Khaldun in the 14th century. In the model, polities compete with neighboring polities and can absorb them into an empire. Groups possess “asabiya”, a measure of social solidarity and a sense of shared purpose. Regions that share borders with other groups will have increased asabiya do to salient us vs. them competition. High asabiya enhances the ability to grow, work together, and hence wage war on neighboring groups and assimilate them into an empire. The larger the frontier, the higher the empire’s asabiya.
As an empire expands, (1) increased access to resources drives further growth; (2) internal conflict decreases asabiya among those who live far from the frontier; and (3) expanded size of the frontier decreases ability to wage war along all frontiers. When an empire’s asabiya decreases too much, it collapses.  Another group with more compelling asabiya eventually helps establish a new empire.

Project Selene ABM Suite

Agent-Based Scenario Exploration for Consortium Cooperation Dynamics

Version: 2.1 (Revised)
Date: January 2026
Status: Exploratory Analysis Tool

HUMLAND Fire-in-the-Hole is a conceptual agent-based model (ABM) designed to explore the ecological and behavioral consequences of fire-driven hunting strategies employed by hunter-gatherers, specifically Neanderthals, during the Last Interglacial period around the Neumark-Nord (Germany) archaeological site.

This model builds on and specializes the HUMLAND 1.0.0 model (Nikulina et al. 2024), integrating anthropogenic fires, elephant group behavior, and landscape response to simulate interactions between humans, megafauna, and vegetation over time.

An Agent Based Model that explores the deployment of hydrogen among a regional industrial cluster in the Netherlands, consisting of 15 companies. The companies seek to decarbonize by replacing their natural gas by hydrogen.
The model integrates technical characteristics as well as company motivations to transition to hydrogen. The baseline model only considers individual investments where company can locally produce hydrogen. If they reach the backbone threshold, companies can also consider buying hydrogen through a connection to the national hydrogen network. The second scenario considers that companies can participate in a joint investment to get an electrolyzer to locally produce the hydrogen.
Two experiments look at the impact of the sectoral configuration and at the impact of subsidy conditions on the region’s hydrogen transition

This agent-based model investigates scapegoating as a social mechanism of crisis management. Inspired by René Girard’s mimetic theory, it simulates how individual tension accumulates and spreads across a small-world network. When tension exceeds certain thresholds, leaders emerge and accuse marginalized agents, who may attempt to transfer blame to substitutes. If scapegoating occurs, collective tension decreases, but victims become isolated while leaders consolidate temporary authority. This simulation provides a conceptual and methodological framework for exploring how collective blame, crisis contagion, and leadership paradoxes emerge in complex networks. It can also be extended with empirical data, such as social media dynamics of online harassment and virtual lynching, offering potential applications for both theoretical research and practical crisis monitoring.

This project is an interactive agent-based model simulating consumption of a shared, renewable resource using a game-theoretic framework with environmental feedback. The primary function of this model was to test how resource-use among AI and human agents degrades the environment, and to explore the socio-environmental feedback loops that lead to complex emergent system dynamics. We implemented a classic game theoretic matrix which decides agents´ strategies, and added a feedback loop which switches between strategies in pristine vs degraded environments. This leads to cooperation in bad environments, and defection in good ones.

Despite this use, it can be applicable for a variety of other scenarios including simulating climate disasters, environmental sensitivity to resource consumption, or influence of environmental degradation to agent behaviour.
The ABM was inspired by the Weitz et. al. (2016, https://pubmed.ncbi.nlm.nih.gov/27830651/) use of environmental feedback in their paper, as well as the Demographic Prisoner’s Dilemma on a Grid model (https://mesa.readthedocs.io/stable/examples/advanced/pd_grid.html#demographic-prisoner-s-dilemma-on-a-grid). The main innovation is the added environmental feedback with local resource replenishment.

Beyond its theoretical insights into coevolutionary dynamics, it serves as a versatile tool with several practical applications. For urban planners and policymakers, the model can function as a ”digital sandbox” for testing the impacts of locating high-consumption industrial agents, such as data centers, in proximity to residential communities. It allows for the exploration of different urban densities, and the evaluation of policy interventions—such as taxes on defection or subsidies for cooperation—by directly modifying the agents’ resource consumptions to observe effects on resource health. Furthermore, the model provides a framework for assessing the resilience of such socio-environmental systems to external shocks.
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BESTMAP-ABM-DE is an agent-based model to determine the adoption and spatial allocation of selected agri-environmental schemes (AES) by individual farmers in the Mulde River Basin located in Western Saxony, Germany. The selected AES are buffer areas, cover crops, maintaining permanent grassland and conversion of arable land to permanent grassland. While the first three schemes have already been offered in the case study area, the latter scheme is a hypothetical scheme designed to test the impact of potential policy changes. For the first model analyses, only the currently offered schemes are considered. With the model, the effect of different scenarios of policy design on patterns of adoption can be investigated. In particular, the model can be used to study the social-ecological consequences of agricultural policies at different spatial and temporal scales and, in combination with biophysical models, test the ecological implications of different designs of the EU’s Common Agricultural Policy. The model was developed in the BESTMAP project.

This agent-based model (ABM), developed in NetLogo and available on the COMSES repository, simulates a stylized, competitive electricity market to explore the effects of carbon pricing policies under conditions of technological innovation. Unlike traditional models that treat innovation as exogenous, this ABM incorporates endogenous innovation dynamics, allowing clean technology costs to evolve based on cumulative deployment (Wright’s Law) or time (Moore’s Law). Electricity generation companies act as agents, making investment decisions across coal, gas, wind, and solar PV technologies based on expected returns and market conditions. The model evaluates three policy scenarios—No Policy, Emissions Trading System (ETS), and Carbon Tax—within a merit-order market framework. It is partially empirically grounded, using real-world data for technology costs and emissions caps. By capturing emergent system behavior, this model offers a flexible and transparent tool for analyzing the transition to low-carbon electricity systems.