Beyond Educational Policy Making

Abstract

In recent years, formulation of educational policy has come to be based on data. That data, however, can turn out to be difficult to access, or mixed with so much noise interfering with education policy formulation, that it cannot be used directly for policy making. To address this issue, an increasing number of attempts to contribute to policy formulation have been made using agent-based simulation (ABS). In the majority of research, ABS is used in the ex post facto analysis of why educational policy has not been effective. In this paper, case studies show that by incorporating ABS into the policy formulation process, the risk of failure can be reduced. By illustrating the relationships between model level, stage of educational policy formulation and the output scenarios of ABS, it is possible to determine which types of risks can be reduced. This paper presents ABS description levels, and discusses risks that both can and cannot be expressed using ABS. We show two ways to use ABS for educational policy making by identifying risks that can be reduced and risks that cannot be dealt with by ABS.