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Artificial worlds modeling of human resource management systems

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3 Author(s)
Jong-Chen Chen ; Dept. of Manage. Inf. Syst., Nat. Yunlin Univ. of Sci. & Technol., Touliu, Taiwan ; Tze-Lan Lin ; Mao-Hung Kuo

Effective human resource management facilitates the success of an organization and the progress of a society. We describe an evolutionary computer model that simulates different modes of interaction between people and their environment. A two-level genotype-phenotype structure is used to represent the characteristics of an individual. The environment is modeled as a two-dimensional array of regions in which each region is characterized by a set of regional features and organizational culture. Evolution can occur at the regional and organizational levels. At the level of regional learning, the experimental results show that people tend to migrate from lesser-fitting regions to better-fitting regions to increase their fitness, which in turn results in the problem that some regions become extremely crowded and other areas have few residents. This problem can be partially eased by putting pressure on the number of people allowed in each region. However, our results show that too great an increase in pressure worsens the problem. At the level of organizational learning, our experiments show that individuals with a local mutation operator are better at adapting to a constant leadership strategy (type), while those with a global mutation operator are better at coping with the changes in leadership strategy. The individuals who sustain a balance between a global and a local mutation operator achieve better performance in a changing leadership strategy than a constant leadership strategy. The results demonstrate that the model is imparted with sufficient dynamics to allow different types of outputs to occur. The artificial worlds approach makes it possible to conduct some experiments that are infeasible to perform in the real world. Combining more selected features into the model would show its potential use in investigating complex human resource management issues.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:6 ,  Issue: 6 )