This ABM re-implements and extends the simulation model of peer review described in Squazzoni & Gandelli (Squazzoni & Gandelli, 2013 - doi:10.18564/jasss.2128) (hereafter: ‘SG’). The SG model was originally developed for NetLogo and is also available in CoMSES at this link.
The purpose of the original SG model was to explore how different author and reviewer strategies would impact the outcome of a journal peer review system on an array of dimensions including peer review efficacy, efficiency and equality. In SG, reviewer evaluation consists of a continuous variable in the range [0,1], and this evaluation scale is the same for all reviewers. Our present extension to the SG model allows to explore the consequences of two more realistic assumptions on reviewer evaluation: (1) that the evaluation scale is discrete (e.g. like in a Likert scale); (2) that there may be differences among their interpretation of the grades of the evaluation scale (i.e. that the grade language is heterogeneous).

This model represents individuals from two different groups interacting and sharing attitudes toward both groups. Through these interactions, individuals adjust their attitudes, which can lead to different outcomes in opinion dynamics. The model can study both intra-group and inter-group opinion dynamics. Under certain conditions, agents with initially moderate attitudes might enter a negative spiral, leading to increasingly extreme intergroup attitudes. Alternatively, these moderate attitudes can foster consensus between groups, resulting in agreement on intergroup attitudes. This model serves as a tool for exploring opinion polarization, particularly in contexts where attitudes evolve through interaction rather than remaining static.

By adjusting different parameters, one can examine the effects of variations in attitude distributions; group size and relative group size; contributions of xenophobia, opinion distance, and individual attitudes towards the outgroup; as well as ratio between xenophobia and attitudinal bias to the overall intergroup bias.

The model accompanies the paper When contact backfires, and when it does not: A computational model of the opinion dynamics of intergroup attitudes.

This model is a replication of Torsten Hägerstrand’s 1965 model–one of the earliest known calibrated and validated simulations with implicit “agent based” methodology.

We provide an agent-based model of collective action, informed by Granovetter (1978) and its replication model by Siegel (2009). We use the model to examine the role of ICTs in collective action under different cultural and political contexts.

An agent-based model of rule-governed (religious) communities in which networked agents observe one another’s conduct, sanction deviation,
and decide whether to remain. The model separates two structural conditions for durable capture. First, whether an enforcement apparatus
activates: agents punish deviation only where conduct is observable (the code-geometry parameter sigma) and where sanctioning is
institutionally rewarded (pi), a joint threshold in which neither condition alone suffices. Second, whether members’ exit is foreclosed: an
outside-option degradation (delta) that can be imposed exogenously or allowed to drift with enforcement activity. Punishment concentrates in
a small, endogenously recomputed enforcer cadre, but that concentration is produced by an institutional privilege bundle (a punishment
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This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.

AncientS-ABM is an agent-based model for simulating and evaluating the potential social organization of an artificial past society, configured by available archaeological data. Unlike most existing agent-based models used in archaeology, our ABM framework includes completely autonomous, utility-based agents. It also incorporates different social organization paradigms, different decision-making processes, and also different cultivation technologies used in ancient societies. Equipped with such paradigms, the model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics; and to assess the influence of social organization on agents’ population growth, agent community numbers, sizes and distribution.

AncientS-ABM also blends ideas from evolutionary game theory with multi-agent systems’ self-organization. We model the evolution of social behaviours in a population of strategically interacting agents in repeated games where they exchange resources (utility) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the progressive adoption of the most successful agent strategies. Agent population is not fixed, but fluctuates over time, while agents in stage games also receive non-static payoffs, in contrast to most games studied in the literature. To tackle this, we defined a novel formulation of the evolutionary dynamics via assessing agents’ rather than strategies’ fitness.

As a case study, we employ AncientS-ABM to evaluate the impact of the implemented social organization paradigms on an artificial Bronze Age “Minoan” society, located at different geographical parts of the island of Crete, Greece. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate better sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, where a “heterarchical” social structure emerges. Results also demonstrate that successful agent societies adopt an evolutionary approach where cooperation is an emergent strategic behaviour. In simulation scenarios where the natural disaster module was enabled, we observe noticeable changes in the settlements’ distribution, relating to significantly higher migration rates immediately after the modeled Theran eruption. In addition, the initially cooperative behaviour is transformed to a non-cooperative one, thus providing support for archaeological theories suggesting that the volcanic eruption led to a clear breakdown of the Minoan socio-economic system.

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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 agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory

The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.