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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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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MOSAIC is an agent-based NetLogo model of decentralized mission coordination among heterogeneous robots operating under partial observability, limited energy, spatially variable risk, dynamic communication, and individual and cooperative task requirements. Robots discover tasks locally, exchange task information through temporary communication links, submit capability-, energy-, deadline-, and risk-aware bids, compete for individual contracts, and form temporary coalitions for cooperative tasks.

The model integrates decentralized auctions, greedy capability-based coalition formation, contract release and reassignment, four reward regimes, reputation, adaptive bidding strategies, failure traceability, and mission-, network-, information-, inequality-, and coalition-level metrics. It operates without a centralized mission planner or global combinatorial assignment solver.

Seven paired-seed BehaviorSpace experiments comprising 690 official simulation runs evaluate baseline mission viability, reward regimes, communication structure, capability heterogeneity, cooperative-task demand, reputation and adaptive strategies, and mission-incentive strength. The results indicate that structural coordination capacity—particularly information reach, capability compatibility, and feasible coalition construction—has a stronger effect on mission completion than increasing incentive intensity within the tested architecture and parameter ranges.

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An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.

Subjective biases and errors systematically affect market equilibria, whether at the population level or in bilateral trading. Here, we consider the possibility that an agent engaged in bilateral trading is mistaken about her own valuation of the good she expects to trade, that has not been explicitly incorporated into the existing bilateral trade literature. Although it may sound paradoxical that a subjective private valuation is something an agent can be mistaken about, as it is up to her to fix it, we consider the case in which that agent, seller or buyer, consciously or not, given the structure of a market, a type of good, and a temporary lack of information, may arrive at an erroneous valuation. The typical context through which this possibility may arise is in relation with so-called experience goods, which are sold while all their intrinsic qualities are still unknown (such as untasted bottled fine wines). We model this “private misvaluation” phenomenon in our study. The agents may also be mistaken about how their exchange counterparties are themselves mistaken. Formally, they attribute a certain margin of error to the other agent, which can differ from the actual way that another agent misvalues the good under consideration. This can constitute the source of a second-order misvaluation. We model different attitudes and situations in which agents face unexpected signals from their counterparties and the manner and extent to which they revise their initial beliefs. We analyse and simulate numerically the consequences of first-order and second-order misvaluation on market equilibria.

This agent-based model simulates how new immigrant households choose where to live in Metro Vancouver under the origins diversity scenario. The model begins with 16,000 household agents, reflecting an expected annual population increase of about 42,500 people based on an average household size of 2.56. Each agent is assigned four characteristics: one of ten origin categories, income level (adjusted using NOC data and recent immigrant earnings), likelihood of having children, and preferred mode of commuting. The ten origin groups are drawn from Census patterns, including six subgroups within the broader Asian category (China, India, the Philippines, Iran, South Korea, and Other Asian countries) and two categories for immigrants from the Americas. This refined classification better captures the diversity of newcomers arriving in the region.

This Agent-Based Model is designed to simulate how similarity-based partner selection (homophily) shapes the formation of co-offending networks and the diffusion of skills within those networks. Its purpose is to isolate and test the effects of offenders’ preference for similar partners on network structure and information flow, under controlled conditions.

In the model, offenders are represented as agents with an individual attribute and a set of skills. At each time step, agents attempt to select partners based on similarity preference. When two agents mutually select each other, they commit a co-offense, forming a tie and exchanging a skill. The model tracks the evolution of network properties (e.g., density, clustering, and tie strength) as well as the spread of skills over time.

This simple and theoretical model does not aim to produce precise empirical predictions but rather to generate insights and test hypotheses about the trade-off between partnership stability and information diffusion. It provides a flexible framework for exploring how changes in partner selection preferences may lead to differences in criminal network dynamics. Although the model was developed to simulate offenders’ interactions, in principle, it could be applied to other social processes involving social learning and skills exchange.

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Negotiation plays a fundamental role in shaping human societies, underpinning conflict resolution, institutional design, and economic coordination. This article introduces E³-MAN, a novel multi-agent model for negotiation that integrates individual utility maximization with fairness and institutional legitimacy. Unlike classical approaches grounded solely in game theory, our model incorporates Bayesian opponent modeling, transfer learning from past negotiation domains, and fallback institutional rules to resolve deadlocks. Agents interact in dynamic environments characterized by strategic heterogeneity and asymmetric information, negotiating over multidimensional issues under time constraints. Through extensive simulation experiments, we compare E³-MAN against the Nash bargaining solution and equal-split baselines using key performance metrics: utilitarian efficiency, Nash social welfare, Jain fairness index, Gini coefficient, and institutional compliance. Results show that E³-MAN achieves near-optimal efficiency while significantly improving distributive equity and agreement stability. A legal application simulating multilateral labor arbitration demonstrates that institutional default rules foster more balanced outcomes and increase negotiation success rates from 58% to 98%. By combining computational intelligence with normative constraints, this work contributes to the growing field of socially aware autonomous agents. It offers a virtual laboratory for exploring how simple institutional interventions can enhance justice, cooperation, and robustness in complex socio-legal systems.

The model represents urban commuters’ transport mode choices among cars, public transit, and motorcycles—a mode highly prevalent in developing countries. Using an agent-based modeling approach, it simulates transport dynamics and serves as a testbed for evaluating policies aimed at improving mobility.

The model simulates an ecosystem of human agents who decide, at each time step, which mode of transportation to use for commuting to work. Their decision is based on a combination of personal satisfaction with their most recent journey—evaluated across a vector of individual needs—the information they crowdsource from their social network, and their personal uncertainty regarding trying new transport options.

Agents are assigned demographic attributes such as sex, age, and income level, and are distributed across city neighborhoods according to their socioeconomic status. To represent social influence in decision-making, agents are connected via a scale-free social network topology, where connections are more likely among agents within the same socioeconomic group, reflecting the tendency of individuals to form social ties with similar others.

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An unintended consequence of low cost maritime travel may be hyperconnectedness, creating social situations where information can be readily passed before it is verified- an issue not limited to modern digitally connected societies. In traditional Coast Salish societies, the peoples of what is now Western Washington and Southwestern British Columbia, oral traditions were vertified through a process called witnessing. Witnesses would be trained to recount and verify oral history and traditional teachings at high fidelity. Here, a simple model based on dual inheritance approaches to genes and culture, is used to compare this specific form of verifying socially important information compared to modern mass communication. The model suggests that witnessing is a high fidelity form of transmitting knowledge with a low error rate, more in line with modern apprenticeships than mass communication. Social mechanisms such as witnessing provide solutions to issues faced in contemporary discourse where the validity of information and even fact checking mechanisms may be biased or counterfactual. This effort also demonstrates the utillity of using modeling approaches to highlight how specific, historically contingent institutions such as witnesses can be drawn upon to model potential solutions to contemporary issues solved in the past in traditional Coast Salish practice.