IOP 2.1.2 is an agent-based simulation model designed to explore the relations between (1) employees, (2) tasks and (3) resources in an organizational setting. By comparing alternative cognitive strategies in the use of resources, employees face increasingly demanding waves of tasks that derive by challenges the organization face to adapt to a turbulent environment. The assumption tested by this model is that a successful organizational adaptation, called plastic, is necessarily tied to how employees handle pressure coming from existing and new tasks. By comparing alternative cognitive strategies, connected to ‘docility’ (Simon, 1993; Secchi, 2011) and ‘extended’ cognition (Clark, 2003, Secchi & Cowley, 2018), IOP 2.1.2 is an attempt to indicate which strategy is most suitable and under which scenario.

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.

Logônia is a NetLogo model that simulates the growth response of a fictional plant, logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 and demonstrates how to integrate the LogoClim model through the LevelSpace extension.

Logônia follows the FAIR Principles for Research Software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub.

LogoClim is a NetLogo model for simulating and visualizing global climate conditions. It allows researchers to integrate high-resolution climate data into agent-based models, supporting reproducible research in ecology, agriculture, environmental science, and other fields that rely on climate data integration.

The model utilizes raster data to represent climate variables such as temperature and precipitation over time. It incorporates historical data (1951-2024) and future climate projections (2021-2100) derived from global climate models under various Shared Socioeconomic Pathways (SSPs) (O’Neill et al., 2017). All climate inputs come from WorldClim 2.1, a widely used source of high-resolution, interpolated climate datasets based on weather station observations worldwide (Fick & Hijmans, 2017), available for academic and other non-commercial use.

See the Logônia model for an example of how to integrate LogoClim into your model.

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This model is an extension of the Artificial Long House Valley (ALHV) model developed by the authors (Swedlund et al. 2016; Warren and Sattenspiel 2020). The ALHV model simulates the population dynamics of individuals within the Long House Valley of Arizona from AD 800 to 1350. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. The present version of the model incorporates features of the ALHV model including realistic age-specific fertility and mortality and, in addition, it adds the Black Mesa environment and population, as well as additional methods to allow migration between the two regions.

As is the case for previous versions of the ALHV model as well as the Artificial Anasazi (AA) model from which the ALHV model was derived (Axtell et al. 2002; Janssen 2009), this version makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original AA model to estimate annual maize productivity of various agricultural zones within the Long House Valley. A new environment and associated methods have been developed for Black Mesa. Productivity estimates from both regions are used to determine suitable locations for households and farms during each year of the simulation.

This version 2.1.0 of the uFunk model is about setting a business strategy (the S in the name) for an organization. A team of managers (or executives) meet and discuss various options on the strategy for the firm. There are three aspects that they have to agree on to set the strategic positioning of the organization.
The discussion is on market, stakeholders, and resources. The team (it could be a business strategy task force) considers various aspects of these three elements. The resources they use to develop the discussion can come from a traditional approach to strategy or from non-traditional means (e.g., so-called serious play, creativity and imagination techniques).
The S-uFunk 2.1.0 Model wants to understand to which extent cognitive means triggered by traditional and non-traditional resources affect the making of the strategy process.

The Netlogo model is a conceptualization of the Moria refugee camp, capturing the household demographics of refugees in the camp, a theoretical friendship network based on values, and an abstraction of their daily activities. The model then simulates how Covid-19 could spread through the camp if one refugee is exposed to the virus, utilizing transmission probabilities and the stages of disease progression of Covid-19 from susceptible to exposed to asymptomatic / symptomatic to mild / severe to recovered from literature. The model also incorporates various interventions - PPE, lockdown, isolation of symptomatic refugees - to analyze how they could mitigate the spread of the virus through the camp.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

The model aims to simulate predator-prey relationships in an agricultural setting. The focus lies on avian communities and their effect on different pest organisms (here: pest birds, rodents, and arthropod pests). Since most case studies focused on the impact on arthropod pests (AP) alone, this model attempts to include effects on yield outcome. By incorporating three treatments with different factor levels (insectivorous bird species, falconry, nest box density) an experimental setup is given that allows for further statistical analysis to identify an optimal combination of the treatments.
In light of a global decline of birds, insects, and many other groups of organisms, alternative practices of pest management are heavily needed to reduce the input of pesticides. Avian pest control therefore poses an opportunity to bridge the disconnect between humans and nature by realizing ecosystem services and emphasizing sustainable social ecological systems.

This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).

As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.