This is an agent-based model of a simple insurance market with two types of agents: customers and insurers. Insurers set premium quotes for each customer according to an estimation of their underlying risk based on past claims data. Customers either renew existing contracts or else select the cheapest quote from a subset of insurers. Insurers then estimate their resulting capital requirement based on a 99.5% VaR of their aggregate loss distributions. These estimates demonstrate an under-estimation bias due to the winner’s curse effect.

Machine learning technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, it remains unclear how human-machine trust, which is an important factor that influences human-machine knowledge exchange, affects the effectiveness of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust.

The Rigor and Transparency Reporting Standard (RAT-RS) is a tool to improve the documentation of data use in Agent-Based Modelling. Following the development of reporting standards for models themselves, attention to empirical models has now reached a stage where these standards need to take equally effective account of data use (which until now has tended to be an afterthought to model description). It is particularly important that a standard should allow the reporting of the different uses to which data may be put (specification, calibration and validation), but also that it should be compatible with the integration of different kinds of data (for example statistical, qualitative, ethnographic and experimental) sometimes known as mixed methods research.

For the full details on the RAT-RS, please refer to the related publication “RAT-RS: A Reporting Standard for Improving the Documentation of Data Use in Agent-Based Modelling” (http://dx.doi.org/10.1080/13645579.2022.2049511).

Here we provide supplementary material for this article, consisting of a RAT-RS user guide and RAT-RS templates.

With this model, we investigate resource extraction and labor conditions in the Global South as well as implications for climate change originating from industry emissions in the North. The model serves as a testbed for simulation experiments with evolutionary political economic policies addressing these issues. In the model, heterogeneous agents interact in a self-organizing and endogenously developing economy. The economy contains two distinct regions – an abstract Global South and Global North. There are three interlinked sectors, the consumption good–, capital good–, and resource production sector. Each region contains an independent consumption good sector, with domestic demand for final goods. They produce a fictitious consumption good basket, and sell it to the households in the respective region. The other sectors are only present in one region. The capital good sector is only found in the Global North, meaning capital goods (i.e. machines) are exclusively produced there, but are traded to the foreign as well as the domestic market as an intermediary. For the production of machines, the capital good firms need labor, machines themselves and resources. The resource production sector, on the other hand, is only located in the Global South. Mines extract resources and export them to the capital firms in the North. For the extraction of resources, the mines need labor and machines. In all three sectors, prices, wages, number of workers and physical capital of the firms develop independently throughout the simulation. To test policies, an international institution is introduced sanctioning the polluting extractivist sector in the Global South as well as the emitting industrial capital good producers in the North with the aim of subsidizing innovation reducing environmental and social impacts.

This computational model accompanies the article “The Informational Assumptions of Schelling Segregation: An Agent-Based Decomposition of Cue Inference, Cultural Schemas, and Residential Sorting.” It implements an agent-based model in which agents infer latent neighborhood-type classes from noisy non-demographic cues through schema-specific diagnostic mappings, update beliefs, and relocate when satisfaction on a preferred latent class falls below a threshold.

The model serves as a mechanism-isolation device for studying the informational architecture underlying Schelling-style residential sorting. It includes the principal sweep configuration (14,400 runs across a seven-parameter grid), a disagreement-metric sub-sweep with permutation-minimized Jensen-Shannon divergence recorded natively, controls (positive, negative, and frozen-belief), a paired-seed cue-channel perturbation experiment, and selected-cell sensitivity sweeps for cue persistence and home-biased mobility.

The full ODD protocol, parameter manifests, deterministic seed schedules, processed outputs, regenerable figure scripts, the verification test suite, and the satisfaction-mapping audit document are included. Every reported run is deterministic given a (config, seed) pair, and an included audit script verifies bit-for-bit replay on sampled runs.

The purpose of this model is to analyze different configurations and scenarios of ecological corridors to simulate the movement of three avoider bird species at a local scale: Chondrohierax uncinatus (Accipitridae), a large carnivorous bird; Ampelion rubrocristatus (Cotingidae), a species that seeks areas with substantial land cover for refuge and rest; and Coeligena bonapartei (Trochilidae), a large hummingbird that prefers areas with a rich and diverse food supply. The model focusses on juvenile bird individuals seeking refuge and food, taking into account the mobility parameters of each species and the existing land cover types within the study area.
Specifically, the model aims to:
• Simulate the movement of 45 avoiders birds which are considered umbrella species sensitive to urban changes (which were chosen based on their specific biological and ecological requirements and parameters relevant to urban conservation efforts), 15 avoiders birds per specie to cross a two-dimensional world predominant urban.
• To be able to select which corridor scenario would be the most beneficial, in order to help the mobility of other species affected by urban fragmentation.
• Contribute to urban ecology research and support decision-making processes by relevant stakeholders.

Considering that two of the three avoider species could not reach the target area in the inittial scenario, five alternative corridor scenarios were created. In all cases, we generated a greater amount of cover area under ‘Urban forest’, including elements such as scattered trees, woody plants, wooded areas, and rows of trees. This covered type was selected since all three species use it as a regular habitat. That is the second sceneario where those ecological parks and other areas inside the capital city were boostered into “urban forest patches” or buffer points, with the idea of improving the survive of the three bird species and their movement. However one of the most restrictive specie was still having movement and survival issues.

An ABM, derived from a case study and a series of surveys with greenhouse growers in the Westland, Netherlands. Experiments using this model showshow that the greenhouse horticulture industry displays diversity, adaptive complexity and an uneven distribution, which all suggest that the industry is an evolving system.

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 HUMan impact on LANDscapes (HUMLAND) model has been developed to track and quantify the intensity of different impacts on landscapes at the continental level. This agent-based model focuses on determining the most influential factors in the transformation of interglacial vegetation with a specific emphasis on burning organized by hunter-gatherers. HUMLAND integrates various spatial datasets as input and target for the agent-based model results. Additionally, the simulation incorporates recently obtained continental-scale estimations of fire return intervals and the speed of vegetation regrowth. The obtained results include maps of possible scenarios of modified landscapes in the past and quantification of the impact of each agent, including climate, humans, megafauna, and natural fires.