Computational Model Library

Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and turnover. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group turnover either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group turnover too far might backfire and decrease performance significantly.

Individual bias and organizational objectivity

Bo Xu | Published Mon Apr 15 08:22:32 2013 | Last modified Mon Apr 8 20:43:28 2019

This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.

TRAINING AND TURNOVER

Kehinde Salau | Published Tue Dec 16 11:24:34 2008 | Last modified Sat Apr 27 20:18:51 2013

The purpose of the model presented by Glance et al is to study the ‘contribute vs. free-ride’ dilemma present in organizations.

Scilab version of an agent-based model of societal well-being, based on the factors of: overvaluation of conspicuous prosperity; tradeoff rate between inconspicuous/conspicuous well-being factors; turnover probability; and individual variation.

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