An agent-based microsimulation of insecticide-treated net (ITN) distribution and adoption in Kenya (2003–2024), integrating the Theory of Planned Behaviour, Rogers diffusion, Weibull net decay, and a GPS-based two-layer social network. 8,561 household agents calibrated via Approximate Bayesian Computation to six DHS/MIS survey waves, achieving 2.42 pp mean absolute error on Kenya-level ownership. The analysis chain supports mechanism counterfactuals and policy experiments on equity outcomes of ITN distribution strategies.

This is an agent-based model, simulating wolf (Canis Lupus) reappearance in the Netherlands. The model’s purpose is to allow researchers to investigate the reappearance of wolves in the Netherlands and the possible effect of human interference. Wolf behaviour is modelled according to the literature. The suitability of the Dutch landscape for wolf settlement has been determined by Lelieveld (2012) [1] and is transformed into a colour-coded map of the Netherlands. The colour-coding is the main determinant of wolf settlement. Human involvement is modelled through the public opinion, which varies according to the size, composition and behaviour of the wolf population.

[1] Lelieveld, G.: Room for wolf comeback in the Netherlands, (2012).

An Agent-Based Model to simulate agent reactions to threatening information based on the anxiety-to-approach framework of Jonas et al. (2014).
The model showcases the framework of BIS/BAS (inhibitory and approach motivated behavior) for the case of climate information, including parameters for anxiety, environmental awareness, climate scepticism and pro-environmental behavior intention.

Agents receive external information according to threat-level and information frequency. The population dynamic is based on the learning from that information as well as social contagion mechanisms through a scale-free network topology.

The model uses Netlogo 6.2 and the network extension.
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SERDUX-MARCIM simulates the propagation of a cyberattack over the computational network of an organization in the maritime sector, at the strategic level of decision-making. It is the instantiation of ABM-MARCIM, the agent-based model of the MARCIM framework for the modeling and simulation of maritime cyberdefense.

Every computational asset of the target organization – servers, endpoints, routers, gateways, vessel systems, radars – is an agent that occupies one of six states at each time step: Susceptible, Exposed, Resistant, Degraded, Unavailable or Destroyed, the initials of which give the model its name. The states Degraded, Unavailable and Destroyed are associated with the D5 cyberattack effects (disrupt, degrade, deny, destroy, deceive) as a function of the degree and the duration of the attack. Thirteen transitions between states are admissible.

Unlike a conventional agent-based model, the local update function is not an individual behavioral rule. It is a system of six ordinary differential equations with eight time-dependent transition rates – propagation, cyberattack (degraded), cyberattack (unavailable), cyberattack (destroyed), recovery, sanitation, loss of resistance, and unavailability by other causes. The values of those rates derive from the capabilities of the target organization, the capabilities of the attacker, and the degree and duration of the cyberattack, computed through a cyber risk approach aligned with the OWASP Risk Rating Methodology, the ISACA categorization of security controls and the IMO Guidelines on Maritime Cyber Risk Management.

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This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.

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.

Comparing 7 alternative models of human behavior and assess their performance on a high resolution dataset based on individual behavior performance in laboratory experiments.

This research article presents an agent-based simulation hereinafter called COMMONSIM. It builds on COMMONISM, i.e. a large-scale commons-based vision for a utopian society. In this society, production and distribution of means are not coordinated via markets, exchange, and money, or a central polity, but via bottom-up signalling and polycentric networks, i.e. ex-ante coordination via needs. Heterogeneous agents care for each other in life groups and produce in different groups care, environmental as well as intermediate and final means to satisfy sensual-vital needs. Productive needs decide on the magnitude of activity in groups for a common interest, e.g. the production of means in a multi-sectoral artificial economy. Agents share cultural traits identified by different behaviour: a propensity for egoism, leisure, environmentalism, and productivity. The narrative of this utopian society follows principles of critical psychology and sociology, complexity and evolution, the theory of commons, and critical political economy. The article presents the utopia and an agent-based study of it, with emphasis on culture-dependent allocation mechanisms and their social and economic implications for agents and groups.

At the heart of a study of Social-Ecological Systems, this model is built by coupling together two independently developed models of social and ecological phenomena. The social component of the model is an abstract model of interactions of a governing agent and several user agents, where the governing agent aims to promote a particular behavior among the user agents. The ecological model is a spatial model of spread of the Mountain Pine Beetle in the forests of British Columbia, Canada. The coupled model allowed us to simulate various hypothetical management scenarios in a context of forest insect infestations. The social and ecological components of this model are developed in two different environments. In order to establish the connection between those components, this model is equipped with a ‘FlipFlop’ - a structure of storage directories and communication protocols which allows each of the models to process its inputs, send an output message to the other, and/or wait for an input message from the other, when necessary. To see the publications associated with the social and ecological components of this coupled model please see the References section.

Meridian is a deterministic artificial world in which autonomous LLM scientist agents run
experiments on instruments, exchange letters, and maintain explicit probabilistic beliefs, while
a physical constant changes covertly mid-run. The true state is known only to the simulator, so
whether anyone notices becomes a measurement rather than an interpretation.

The platform separates three things that are usually confounded: what could have been known from
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