the model can be used to describe the product diffusion in an Advance Selling Strategy. this model takes into account the consumers product adoption, and describe consumer’s online behavior based on four states.

CPNorm is a model of a community of harvesters using a common pool resource where adhering to the optimal extraction level has become a social norm. The model can be used to explore the robustness of norm-driven cooperation in the commons.

The model employs an agent-based model for exploring the victim-centered approach to identifying human trafficking and the approach’s effectiveness in an abstract representation of migrant flows.

Cultural group selection model of agents playing public good games and who are able to punish and punish back.

This is the R code of the mathematical model that includes the decision making formulations for artificial agents. This code corresponds to equations 1-70 given in the paper “A Mathematical Model of The Beer Game”.

The aim of this model is to study the dynamic propagation of individual climate adaptive behaviours in different scenarios within the analytical framework of conservation motivation theory, focusing on the impact of social and experiential learning on the adoption of climate adaptive behaviours by coastal farmers.
Model for paper “Promoting climate resilience through learning-based behavioural change: Insights from an agent-based model of a coastal farming community in Guangxi, China” in Environmental Science & Policy, Volume 179, May 2026, 104375, https://doi.org/10.1016/j.envsci.2026.104375

The model includes different formulations how agents make decisions in irrigation games and this is compared with empirical data. The aim is to test different theoretical models, especially explaining effect of communication.

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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Although beneficial to scientific development, data sharing is still uncommon in many research areas. Various organisations, including funding agencies that endorse open science, aim to increase its uptake. However, estimating the large-scale implications of different policy interventions on data sharing by funding agencies, especially in the context of intense competition among academics, is difficult empirically. Here, we built an agent-based model to simulate the effect of different funding schemes (i.e., highly competitive large grants vs. distributive small grants), and varying intensity of incentives for data sharing on the uptake of data sharing by academic teams strategically adapting to the context.

This is the R code of the mathematical model that includes the decision making formulations for artificial agents. Plus, the code for graphical output is also added to the original code.