| 研究実績の概要 |
This study provides theoretical studies on labor markets incorporating econophysics through statistical mechanical approaches.
Inspired by the unsupervised learning or self-organization in the machine learning context, we attempt to draw a ‘learning curve’ for the collective behavior of job-seeking ‘non-intelligent’ labors in successive job-hunting processes. We discuss the speed of convergence for the error-measurement, where we consider a scenario in which the students do not use any information about the result of job-hunting in a previous process. Our approach enables us to examine the existence of the condition on which macroscopic quantity, say, ‘stage-wise unemployment rate’ becomes ‘scale-invariant’ in the sense that it does not depend on the job-hunting stage. From the macroscopic view point, the problem could be regarded as a human resource allocation.
We provide a mathematical model to investigate the human resource allocation problem for students. The basic model is described by the Potts spin glass. In the model, each Potts spin represents the action of one student, and it takes a discrete variable corresponding to the company he/she applies for. We construct the energy to include the distinct effects on students’ behavior. The correlations (the adjacent matrix) between students are taken into account through the pairwise spin-spin interactions. We carry out computer simulations to examine the efficiency of the model. We also show that some chiral representation of the Potts spin enables us to obtain some analytical insights into our labor markets.
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