The model, presented here, is a re-implementation of the Pepper and Smuts’ model : - Pepper, J.W. and B.B. Smuts. 2000. “The evolution of cooperation in an ecological context: an agent-based model”. Pp. 45-76 in T.A. Kohler and G.J. Gumerman, eds. Dynamics of human and primate societies: agent-based modeling of social and spatial processes. Oxford University Press, Oxford. - Pepper, J.W. and B.B. Smuts. 2002. “Assortment through Environmental Feedback”. American Naturalist, 160: 205-213 […]

In 1985 Dr Michael Palmiter, a high school teacher, first built a very innovative agent-based model called “Simulated Evolution” which he used for teaching the dynamics of evolution. In his model, students can see the visual effects of evolution as it proceeds right in front of their eyes. Using his schema, small linear changes in the agent’s genotype have an exponential effect on the agent’s phenotype. Natural selection therefore happens quickly and effectively. I have used his approach to managing the evolution of competing agents in a variety of models that I have used to study the fundamental dynamics of sustainable economic systems. For example, here is a brief list of some of my models that use “Palmiter Genes”:
- ModEco - Palmiter genes are used to encode negotiation strategies for setting prices;
- PSoup - Palmiter genes are used to control both motion and metabolic evolution;
- TpLab - Palmiter genes are used to study the evolution of belief systems;
- EffLab - Palmiter genes are used to study Jevon’s Paradox, EROI and other things.

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This model visualizes gradient descent optimization - the fundamental algorithm used to train neural networks and other machine learning models. Agents represent different optimization algorithms searching for the minimum of a loss landscape (the “error surface” that ML models try to minimize during training).

The model demonstrates how different optimizer types (SGD, Momentum with different parameters) behave on various loss landscapes, from simple bowls to the notoriously difficult Rosenbrock “banana valley” function. This helps build intuition about why certain optimization algorithms work better than others for different problem geometries.

HOW IT WORKS

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STiMUS (Stigmergic–Mutualistic IMOI Model) is an agent-based model of teamwork in socio-technical systems where contributors collaborate through shared digital artefacts — wiki pages, code files, issue tickets, project cards, Scratch projects — represented as patches in a NetLogo world. The model integrates two coordination mechanisms. Stigmergy is indirect coordination through traces left in a shared environment: each edit deposits a pheromone that diffuses to neighbouring patches and evaporates over time, so recent activity attracts further contributions. Mutualism is a reciprocal benefit loop in which valuable, well-maintained artefacts raise contributor motivation and shared understanding, while motivated contributors improve artefacts.

Contributors (turtles of the contributor breed) carry individual state: skill, motivation, shared-mental-model, specialty, benefit-gain, and an explicit-mode flag. At each tick every contributor selects a target artefact with an ant-colony-optimization-style rule weighing the artefact’s pheromone, incompleteness (1 - completeness), resource-value, and topic match between specialty and the artefact’s topic-tag; with probability p-explicit it instead takes the patch with the highest maintenance-need, modelling explicit task assignment. Each edit increases pheromone, quality, completeness and reuse-count, raises resource-value, lowers maintenance-need, and appends the editor to the artefact’s edit-authors list. When the previous last-editor-id differs from the current editor, the Edit Succession Ratio rises, the editor’s shared-mental-model grows, and a co-editing link is created — operationalising the idea that repeated cross-author succession on the same artefact builds shared understanding. Contributors’ motivation is updated from the benefit drawn from the visited artefact.

Each patch maintains a stigmergic layer (pheromone, quality, completeness, recentness, last-editor-id, edit-count, edit-authors) and a mutualistic layer (resource-value, reuse-count, maintenance-need, topic-tag), plus task flags (is-task?, task-complexity). Global monitors report the Edit Succession Ratio (ESR = cross-author-edits / total-edits, and an alternative esr-value = share of edited patches with more than one distinct author), mean-quality, mean-resource-value, a mutualism-index averaging contributor benefit and resource value, coediting-density (network density of the co-editing graph), active-pages-share, and task-completion-rate. The model logs every edit as a bipartite edge (tick, author_id, pageid, specialty, topic_tag, quality), exportable to CSV.

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We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.

This model is an extended version of the original MERCURY model (https://www.comses.net/codebases/4347/releases/1.1.0/ ) . It allows for experiments to be performed in which empirically informed population sizes of sites are included, that allow for the scaling of the number of tableware traders with the population of settlements, and for hypothesised production centres of four tablewares to be used in experiments.

Experiments performed with this population extension and substantive interpretations derived from them are published in:

Hanson, J.W. & T. Brughmans. In press. Settlement scale and economic networks in the Roman Empire, in T. Brughmans & A.I. Wilson (ed.) Simulating Roman Economies. Theories, Methods and Computational Models. Oxford: Oxford University Press.

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The Friendship Field model aims at modelling friendship formation based on three factors: Extraversion, Resemblance and Status, where social interaction is motivated by the Social Battery. Social Battery is one’s energy and motivation to engage in social contact. Since social contact is crucial for friendship formation, the model included Social Battery to affect social interactions. To our best knowledge, Social Battery is a yet unintroduced concept in research while it is a dynamic factor influencing the social interaction besides one’s characteristics. Extraverts’ Social Batteries charge while interacting and exhaust while being alone. Introverts’ Social Batteries charge while being alone and exhaust while interacting. The aim of the model is to illustrate the concept of Social Battery. Moreover, the Friendship Field shows patterns regarding Extraversion, Resemblance and Status including the mere-exposure effect and friendship by similarity. For the implementation of Status, Kemper’s status-power theory is used. The concept of Social Battery is also linked to Kemper’s theory on the organism as reference group. By running the model for a year (3 interactions moments per day), the friendship dynamics over time can be studied.

We presented the model at the Social Simulation Conference 2022.

This package implements a simplified artificial agent-based demographic model of the UK. Individuals of an initial population are subject to ageing, deaths, births, divorces and marriages. A specific case-study simulation is progressed with a user-defined simulation fixed step size on a hourly, daily, weekly, monthly basis or even an arbitrary user-defined clock rate. While the model can serve as a base model to be adjusted to realistic large-scale socio-economics, pandemics or social interactions-based studies mainly within a demographic context, the main purpose of the model is to explore and exploit capabilities of the state-of-the-art Agents.jl Julia package as well as other ecosystem of Julia packages like GlobalSensitivity.jl. Code includes examples for evaluating global sensitivity analysis using Morris and Sobol methods and local sensitivity analysis using OFAT and OAT methods. Multi-threaded parallelization is enabled for improved runtime performance.

The model is designed to simulate the behavior and decision-making processes of individuals (agents) in a social network. It aims to represent the changes in individual probability to take any action based on changes in attributes. The action is anything that can be reasonably influenced by the three influencing methods implemented in this model: peer pressure, social media, and state campaigns, and for which the user has a decision-making model. The model is implemented in the multi-agent programmable environment NetLogo 6.3.0.

An individual-based model to evaluate, whether time delays in plant responses to insect herbivory can be beneficial for the plant.