Computational Model Library

Displaying 10 of 981 results for "M Van Den Hoven" clear search

Cetina ABM

Maja Gori Frederik Schaff | Published Sunday, February 16, 2025

We provide a theory-grounded, socio-geographic agent-based model to present a possible explanation for human movement in the Adriatic region within the Cetina phenomenon.

Focusing on ideas of social capital theory from Piere Bordieu (1986), we implement agent mobility in an abstract geography based on cultural capital (prestige) and social capital (social position). Agents hold myopic representations of social (Schaff, 2016) and geographical networks and decide in a heuristic way on moving (and where) or staying.

The model is implemented in a fork of the Laboratory for Simulation Development (LSD), appended with GIS capabilities (Pereira et. al. 2020).

SimPLS - The PLS Agent

Iris Lorscheid Sandra Schubring Matthias Meyer Christian Ringle | Published Monday, April 18, 2016 | Last modified Tuesday, May 17, 2016

The simulation model SimPLS shows an application of the PLS agent concept, using SEM as empirical basis for the definition of agent architectures. The simulation model implements the PLS path model TAM about the decision of using innovative products.

Demand planning requires processing of distributed information. In this process, individuals, their properties and interactions play a crucial role. This model is a computational testbed to investigate these aspects with respect to forecast accuracy.

a computer-based role-playing game simulating the interactions between farming activities, livestock herding and wildlife in a virtual landscape reproducing local socioecological dynamics at the periphery of Hwange National Park (Zimbabwe).

Gradient Descent Simulation

Ilyes Azouani | Published Wednesday, March 18, 2026

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

We provide a full description of the model following the ODD protocol (Grimm et al. 2010) in the attached document. The model is developed in NetLogo 5.0 (Wilenski 1999).

Coupled Housing and Land Markets (CHALMS)

Nicholas Magliocca Virginia Mcconnell Margaret Walls | Published Friday, November 02, 2012 | Last modified Monday, October 27, 2014

CHALMS simulates housing and land market interactions between housing consumers, developers, and farmers in a growing ex-urban area.

A simplified Arthur & Polak logic circuit model of combinatory technology build-out via incremental development. Only some inventions trigger radical effects, suggesting they depend on whole interdependent systems rather than specific innovations.

The Informational Dynamics of Regime Change

Dominik Klein Johannes Marx | Published Saturday, October 07, 2017 | Last modified Tuesday, January 14, 2020

We model the epistemic dynamics preceding political uprising. Before deciding whether to start protests, agents need to estimate the amount of discontent with the regime. This model simulates the dynamics of group knowledge about general discontent.

LimnoSES - social-ecological lake management undergoing regime shifts

Romina Martin | Published Thursday, November 24, 2016 | Last modified Friday, January 18, 2019

LimnoSES is a coupled system dynamics, agent-based model to simulate social-ecological feedbacks in shallow lake use and management.

Displaying 10 of 981 results for "M Van Den Hoven" clear search

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