|InterJournal Complex Systems, 1062
|Manuscript Number: |
Submission Date: 2004
|Modeling Social Structure as Network Effects: Computational Evidence That Rewarding Learning Improves Performance|
Building on the axiomatic base of computational organization theory, we developed a theoretical representation of social structures and structuration in agent-based organizations. Using this framework, an idealized agent-based model was developed. To test the model’s experimental adequacy with respect to theory, we generated a hypothesis from organizational learning theory and tested it using computational experiments. We found that rewarding agent learning increased collective output over and above pay for performance. The significance and generalizability of these results are discussed, as are philosophical implications of this research approach.
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