NetLogo model of patch choice model from optimal foraging theory (human behavioral ecology).

Diet breadth is a classic optimal foraging theory (OFT) model from human behavioral ecology (HBE). Different resources, ranked according to their food value and processing costs, are distributed in th

Simulates biobehavioral interactions between 2 populations of hominins.

Biobehavioral interactions between two populations under different movement strategies.

Models land-use, perception, and biocultural interactions between two forager populations.

Model of shifting cultivation. All parameters can be controlled by the user or the model can be run in adaptive mode, in which agents innovate and select parameters.

patch_choice_enhanced.nlogox is an agent-based model (ABM) implementing the patch choice model from optimal foraging theory (OFT) in a multi-agent simulation environment. It extends the classical single-forager, equation-based Marginal Value Theorem (MVT) formulation to support multiple competing foragers, demographic processes, bounded memory, and stochastic resource dynamics on a spatially heterogeneous landscape.

Viking Sailing is a spatially explicit agent-based simulation for conditional exploration of how rasterized coastlines, wind, and
surface-current conditions interact with simplified ship movement, sailing, rowing, navigation, and land-avoidance rules. The
model compares simulated route trajectories and terminal outcomes for hypothetical Viking-era small-ship voyages. It includes an example dataset representing sea surface winds and currents from June 1000 CE. Additional data can are downloaded from Zenodo: https://zenodo.org/records/23213147.

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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Routes & Rumours is an agent-based model of (forced) human migration. We model the formation of migration routes under the assumption that migrants have limited geographical knowledge concerning the transit area and rely to a large degree on information obtained from other migrants.