patch_choice_enhanced.nlogox is an agent-based model (ABM) implementing the patch choice model from optimal foraging theory (OFT) in a multi-agent simulation environment. It extends the classical single-forager, equation-based Marginal Value Theorem (MVT) formulation to support multiple competing foragers, demographic processes, bounded memory, and stochastic resource dynamics on a spatially heterogeneous landscape.

Norms@Risk ABM aim is to develop an explanation for the conditions under which people jointly take action to prevent disasters from happening, e.g. impacts from climate crisis, pandemic. The aim of the Norms@Risk work is to explore understudied dynamics between risk, social norms and cooperative behaviour and their role in overcoming collective threats. More specifically, Norms@Risk seeks to refine existing theory on cooperation by unpacking the role of collective risk interacting with social norms in ‘Collective Risk Social Dilemmas’*.

This simulation study takes on the next scientific iteration after a series of behavioural experiments. With the model we target to refine existing theory by capturing the heterogeneity in contributions that existing theories cannot fully explain as the behavioural responses are assumed to be more homogenous, particularly diversity regarding sensitivity to risk and norms.

*Collective Risk Social Dilemmas reflect a special type of social dilemma, unlike typical public goods, the collective risk dilemma involves individual contribution not to realise a gain, but to avoid a collective loss.

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We built a model of knowledges diffusion in networks. The particularity is that knowledges to be passed on are cumulative : rely on previous acquisition of specific knowledges of a lower level to be passed from an agent to another. We test for different types of networks, different selection rules for the knowledge to be passed and the possibility to introduce a turnover among agents.
We concentrate on the learning speed and the convergence level of the learning process.

This NetLogo model simulates the movement and foraging behavior of 20 bird species (classified as urban avoiders or utilizers) across a 20×20m grid landscape representing southern Bogotá, Colombia. Agents follow an optimal foraging strategy (90% of the time) or move randomly (10%), gaining energy from land-cover-dependent food sources and dying if energy falls below their basal metabolic rate. The model compares two landscape scenarios — current land cover (2016) and a proposed scenario incorporating the ‘Media Luna del Sur’ ecological corridor from Bogotá’s 2022–2035 Land Use Plan (POT) — to evaluate whether the proposed green infrastructure improves functional connectivity for avian biodiversity across the urban matrix. Outputs include cumulative patch visitation counts, exported as raster files, used to generate connectivity heatmaps.

The model simulates the evolution of the labor market in the context of the PNRR (National Recovery and Resilience Plan). It tracks how two types of agents (angajati and neangajati) develop their professional competencies to match the requirements of seven distinct job categories.
The simulation focuses on the gap between current skill levels and market demands, specifically modeling how a sudden “Market Shift” (the introduction of a 13th competency) impacts the workforce’s readiness.

HousingABM_Japan is a NetLogo agent-based model of the residential market of Tokyo’s 23 wards. It evaluates whether a single parameter configuration can jointly reproduce key features of prices, rents, yields, and market turnover across distinct market regimes from 2001 to 2025, with particular attention to demand- and supply-side trend-following during the 2021–2025 price surge.

The model builds on the Bank of England housing-market lineage (Baptista et al. 2016; Carro et al. 2023) and introduces four extensions: (1) dynamic linkages between the sale and rental markets through vacancy, rents, and yields; (2) heterogeneous demand-side trend-following; (3) supply-side trend-following through construction-cost trend anchoring and a momentum-dependent dynamic premium; and (4) housing-equity borrowing that converts unrealized equity into additional borrowing capacity.

Twenty parameters are calibrated using 2001–2015 data and held fixed for post-calibration evaluation over 2016–2020 and 2021–2025, while annual exogenous inputs follow observed historical paths. The model reproduces the shift from moderate price growth to the 2021–2025 surge, as well as rent acceleration, surge-period yield compression, and persistently low market turnover, although it understates the intermediate acceleration of 2016–2020.

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Organizations operate under conditions of imperfect performance in which human error is inevitable, yet errors are rarely examined as the triggers for the managerial interactions that shape organizational culture over time. The present research introduces an agent-based simulation of a work team completing a fixed sequence of tasks under varying degrees of managerial oversight and response policy. The model isolates the mechanical loss of throughput caused by errors from the psychological and cultural consequences of managerial reactions, which are categorized into ignoring, correcting or punishing. Furthermore, the model incorporates an autonomous self-notice mechanism, allowing workers to correct themselves in the absence of managerial intervention. By tracking the accumulation of worker resentment and the transient enhancements in learning, the simulation acts as a dynamic laboratory for observing delayed consequences, nonlinear tipping points and systemic organizational collapse. The results reveal a central paradox of organizational control. Highly monitored punitive environments generate high short-term throughput, yet they simultaneously accumulate interactional injustice and resentment that engineer a rapid cascading turnover and the highest probability of systemic collapse. Conversely, corrective policies combined with active monitoring achieve equivalent throughput while sustaining workforce viability. A comprehensive sensitivity analysis establishes that error accumulation is primarily determined by structural factors, namely agent-level mistake propensity and task difficulty, while resignation dynamics, resentment accumulation and collapse timing remain predominantly governed by managerial policy, a hierarchy independently corroborated by a surrogate model and shown to be stable across independent seeds and across stakeholder weighting scenarios. The study bridges the operational mechanics of task completion and the social dynamics of workplace mistreatment, illustrating how short-term punitive success often masks long-term structural fragility.

This NetLogo model simulates the impact of mowing frequency, mower type and the percentage of refuge strips on the abundance of arthropods in managed grasslands. It represents five functional arthropod groups: holometabolous plant-dwelling arthropods (HoP, represented by butterflies); holometabolous ground-dwelling arthropods (HoG, represented by ants); nesting pollinators (NP, represented by bees); hemimetabolous plant-dwelling arthropods (HeP, represented by grasshoppers); and ground-resident arthropods (GR, represented by spiders). Three mowing frequencies are simulated: intensive (four mowings per year), intermediate (two mowings per year) and extensive (one mowing per year). Bar and disc mowers differ in their effects on arthropod mortality and vegetation height. Refuge strip coverage can be set to 0%, 10% or 20%. Arthropod abundance changes over time through movement, reproduction, natural mortality, mower-related mortality and vegetation regrowth.

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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A simple theoretical agent-based model intending to represent anxiety in students is presented. In this, agents make two decisions each simulation tick, whether to: socialise and go to school (on school days). Circular causation can result in some agents entering a negative loop of high-anxiety and avoidance, even whilst most improve their skills and develop lower levels of anxiety. We compare three different mechanisms that may lie behind decision-making under anxiety: expect the worst, avoid uncertainty and avoid anxiety. Results indicate that, although these are often indistinguishable in terms of some obvious aggregate measures on the outcomes, they do exhibit different underlying dynamics.