Computational Model Library

Team Cognition

Iris Lorscheid | Published Sun May 23 14:25:17 2021

The teamCognition model investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. The agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions.

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 model on feeding and social interaction behaviour of pigs

Iris J.M.M. Boumans | Published Thu May 4 11:46:38 2017 | Last modified Tue Feb 27 11:12:18 2018

The model simulates interaction between internal physiological factors (e.g. energy balance) and external social factors (e.g. competition level) underlying feeding and social interaction behaviour of commercially group-housed pigs.

SimPLS - The PLS Agent

Iris Lorscheid Sandra Schubring Matthias Meyer Christian Ringle | Published Mon Apr 18 09:50:36 2016 | Last modified Tue May 17 11:35:16 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.

The simulation model LAMDA investigates the influences of varying cognitive abilities of the decision maker on the truth-inducing effect of the Groves mechanism. Bounded rationality concepts are represented by information states and learning models.

Tail biting behaviour in pigs

Iris Jmm Boumans Iris J.M.M. Boumans | Published Fri Apr 22 11:40:25 2016 | Last modified Wed Sep 14 06:40:05 2016

The model simulates tail biting behaviour in pigs and how they can turn into a biter and/or victim. The effect of a redirected motivation, behavioural changes in victims and preference to bite a lying pig on tail biting can be tested in the model

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