Some models are more symbolic, containing detailed information about the task in agent problem solving. This is evident in the work of Kunz et al. The recent emergence of computational paradigms and data structures could help in the development of such models to account for the organizational phenomena. From an implementation perspective, the metaphor of distributed objects could be used to mimic agent interaction. The genetic algorithm provides a useful approach for examining organizational change because it evolves populations of individuals from one generation to the next.
Agents are boundedly rational in their decision making.
Agents work in hierarchical organizations. Organizational problems are complex requiring different types of expertise. In this paper, the advantages of using computational models for experimentation, was discussed. Previous models have considered the effects of variables such as cognitive capacity, commitment, decision making style, and reliability on agent behavior. These models served the dual role of theory confirmation as well as theory generation.