Agent-based simulation for computational experimentation: Developing an artificial labor market

This paper discusses the creation of an artificial labor market (ALM) as an agent-based simulation model. We trace the development of the ALM by adapting the traditional simulation life cycle into two main parts: the model phase and the simulation phase. In the modeling phase of the life cycle, we focus upon agent representation and specification within the virtual world. In the simulation phase, we discuss the use of scenario planning as the experimentation vehicle. Throughout, we use military recruit market as an example to illustrate the methodology. The benefits of the ALM are (1) it provides a virtual world for continuous computational experimentation, (2) it supports market segmentation by allowing ‘drilldowns’ to finer and finer levels of granularity, and (3) when connected via a common OLAP interface to a ‘real world’ counterpart, it facilitates a tightly integrated, persistent, ‘sense and respond’ decision support functionality.