An agent-based model built in NetLogo that simulates a closed-loop recirculating aquaponics and integrated multitrophic aquaculture (IMTA) ecosystem, modeling the complex dynamics between aquatic species, vermaculture, and hydroponic plant beds. This model captures the operational mechanics of a complex, sustainable production network. It tracks nutrient flows, water recirculation pathways, biological growth stages, and environmental feedback penalties (such as nutrient toxicity or starvation) across multiple specialized tanks and grow beds. Model Architecture & HabitatsAquatic Subsystem: Duckweed Tank, Breeding Tank, Fry Tank, Fingerling Tank, Adult Tank, and a Holding Tank for harvested fish. Benthic & Waste Management: Crawdad Tank 1 & 2 for bottom-feeding and waste processing, alongside a Vermaculture node populated by composting worms. Hydroponic Subsystem: Five sequential Grow Beds and a recirculating loop feeding back into the system. Key Agent DynamicsWater Flow Visualization: Active links between tanks spawn animated droplet agents that visually simulate recirculating water dynamics across the network. Biological Growth & Aging: Fish and plants progress through age-based development thresholds governed by species-specific parameters. Feedback Loops:Toxicity Penalty: Excess nutrients (nutrient-per-day > 80) slow fish growth rates by 50%. Starvation Penalty: Insufficient nutrient levels (nutrient-per-day < 30) delay plant maturation by 50%. Interface Controlsfish-type Chooser: Selects the primary aquatic species (Tilapia, Channel Catfish, Trout, Goldfish), altering base growth thresholds. plant-type Chooser: Selects the cultivated crop (Lettuce, Basil, Tomatoes, Mint). nutrient-per-day Slider: Manages daily nutrient input to balance system load and avoid toxicity or starvation thresholds. Dashboard Plot: Real-time tracking of total-fish-harvested and total-plants-harvested.

This generic individual-based model of a bird colony shows how the influence neighbour’s stress levels synchronize the laying date of neighbours and also of large colonies. The model has been used to demonstrate how this form of simulation model can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions.

AMIRIS is the Agent-based Market model for the Investigation of Renewable and Integrated energy Systems.

It is an agent-based simulation of electricity markets and their actors.
AMIRIS enables researches to analyse and evaluate energy policy instruments and their impact on the actors involved in the simulation context.
Different prototypical agents on the electricity market interact with each other, each employing complex decision strategies.
AMIRIS allows to calculate the impact of policy instruments on economic performance of power plant operators and marketers.
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Overview

The Weather model is a procedural generation model designed to create realistic daily weather data for socioecological simulations. It generates synthetic weather time series for solar radiation, temperature, and precipitation using algorithms based on sinusoidal and double logistic functions. The model incorporates stochastic variation to mimic unpredictable weather patterns and aims to provide realistic yet flexible weather inputs for exploring diverse climate scenarios.

The Weather model can be used independently or integrated into larger models, providing realistic weather patterns without extensive coding or data collection. It can be customized to meet specific requirements, enabling users to gain a better understanding of the underlying mechanisms and have greater confidence in their applications.

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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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The TechNet_04 is an abstract model that embeds a simple cultural tranmission process in an environment where interaction is structured by spatially-situated networks.

Agent-based model of cooperation among fourteen EU member states under geopolitical shocks, 2024-2040. States are adaptive agents on the observed intra-EU trade network, updating a cooperation propensity through inertia, network influence, and a repeated-game payoff structure built from verified Eurostat, Eurobarometer and IMF data. An anchoring device makes the observed 2024 configuration exactly stationary absent shocks, so results are read as deviations from an empirical baseline. Five scenarios of increasing severity are simulated with Monte Carlo replication, up to an extreme counterfactual crisis in the Taiwan Strait. Includes six robustness batteries and a full ODD description.

WeDiG Sim- Weighted Directed Graph Simulator - is an open source application that serves to simulate complex systems. WeDiG Sim reflects the behaviors of those complex systems that put stress on scale-free, weightedness, and directedness. It has been implemented based on “WeDiG model” that is newly presented in this domain. The WeDiG model can be seen as a generalized version of “Barabási-Albert (BA) model”. WeDiG not only deals with weighed directed systems, but also it can handle the […]

IMine is a flexible framework which can be adopt multiple criteria for convergence to solve Influence Minig problems. It can use any diffusion model, as well as resilience to compute the influence of a set of nodes base on the use case.
The code is written and tested on ‘R’ v3.5

Individually parameterized mussels (Mytilus californianus) recruit, grow, move and die in a 3D environment while facing predation (in the form of seastar agents), heat and desiccation with increased tide height, and storms. Parameterized with data collected by Wootton, Paine, Kandur, Donahue, Robles and others. See my 2019 CoMSES video presentation to learn more.