Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Learning organizational roles for negotiated search in a multiagent system
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Simulation for the Social Scientist
Simulation for the Social Scientist
Structural Learning: Attraction and Conformity in Task-Oriented Groups
Computational & Mathematical Organization Theory
Performance of Organizational Design Models and Their Impact on Organization Learning
Computational & Mathematical Organization Theory
Sharing Metainformation to Guide Cooperative Search Among Heterogeneous Reusable Agents
IEEE Transactions on Knowledge and Data Engineering
A Meta-Model for the Analysis and Design of Organizations in Multi-Agent Systems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
How Individuals Negotiate Societies
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Guanxi Practices and Trust in Management: A Procedural Justice Perspective
Organization Science
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Experiences creating three implementations of the repast agent modeling toolkit
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Autonomous Agents and Multi-Agent Systems
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In this paper, an agent-based simulation approach is applied to explore how employee behavior interacts with dynamic tasks. An assessment model for matching between employees and tasks is presented, and two algorithms to allocate tasks to employees are designed: the minimal matching and the greedy matching algorithm. The algorithms are then translated into multi-agent simulation systems, which are programmed in Java based on Repast J 3.0. The simulation experiment results showed that minimal matching is better than greedy matching for rapid task allocation. The former can reduce interface communication cost and effectively promote the use of employee capability. Moreover, with the minimal matching algorithm, the following effects are evident: (1) the different percentage of generalists and specialists have a distinct effect on completion of tasks; (2) the different preference of manager has a rarer impact on the completion of tasks than on the increase of individual capability; (3) the higher rate of individual capability is positively correlated with collaborative learning rate; and (4) the higher rate of individual capability has a marginal, significant effect when the variance of task capability distribution increases and the expectation remains constant. The increase will be significant when the expectation of the task capability requirement increases. The increase of task number has positive impact on the average rate of increase of capability of employees.