This model is designed to address the following research question: How does the amount and topology of intergroup cultural transmission modulate the effect of local group extinction on selectively neutral cultural diversity in a geographically structured population? The experimental design varies group extinction rate, the amount of intergroup cultural transmission, and the topology of intergroup cultural transmission while measuring the effects of local group extinction on long-term cultural change and regional cultural differentiation in a constant-size, spatially structured population. The results show that for most of the intergroup social network topologies tested here, increasing the amount of intergroup cultural transmission (similar to increasing gene flow in a genetic model) erases the negative effect of local group extinction on selectively neutral cultural diversity. The stochastic (i.e., preference attachment) network seems to stand out as an exception.

The BASAR model aims to investigate different approaches to describe small-scale farmers’ decision-making in the context of diversified agroforestry adoption in rural Rwanda. Thereby, it compares random behaviour with perfect rationality (non-discounted and discounted utility maximization), bounded rationality (satisficing and fast and frugal decision tree heuristics), Theory of Planned Behaviour, and a probabilistic regression-based approach. It is aimed at policy-makers, extension agents, and cooperatives to better understand how rural farmers decide about implementing innovative agricultural practices such as agroforestry and at modelers to support them in selecting an approach to represent human decision-making in ABMs of Social-Ecological Systems. The overall objective is to identify a suitable approach to describe human decision-making and therefore improve forecasts of adoption rates and support the development and implementation of interventions that aim to raise low adoption rates.

A reimplementation of the Wedding Ring model by Francesco Billari. We investigate partnership formation in an agent-based framework, and combine this with statistical demographic projections using real empirical data.

chitwanabm is a spatially explicit agent-based model of population and land use in the Chitwan Valley, Nepal, designed to explore feedbacks between population and environment, with a heavy focus on community context and individual-level variation.

This model represents technological and ecological behaviors of mobile hunter-gatherers, in a variable environment, as they produce, use, and discard chipped stone artifacts. The results can be analyzed and compared with archaeological sites.

This ABM deals with commuting choices in the Italian city of Varese. Empirical data inform agents’ attitudes and modal choices costs and emissions. We evaluate ex ante the impact of policies for less polluting commuting choices.

ABM focused on examining the dissemination of opinions through a notional terrorist network to generate terrorist attacks caused by drone strikes.

MixFarmABM Model examines the competitiveness of second-generation biofuel crops with existing crops and beef cows at the farm level and their impact on the farm structure.

This is an agent-based model of the implementation of the self-enforcing agreement in cooperative teams.

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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