Understanding the historical turn in the policy sciences: A critique of stochastic, narrative, path dependency and process-sequencing models of policy-making over time

Michael Howlett, Simon Fraser University
Jeremy Rayner, University of Regina

Abstract

This article evaluates four general models of historical change processes which have emerged in various fields in the social sciences - namely stochastic, historical narrative, path dependency and process sequencing - and their application to the study of public policy-making. The article sets out and assesses the merits and evidence for each, both in general social research and in the policy sciences. The article suggests that more work needs to be done examining the assumptions and presuppositions of each model before it can be concluded that any represents the general case in policy processes. However, since neither the irreversible linear reality assumed by narrative models, nor the random and chaotic world assumed by stochastic models, nor the contingent turning points and irreversible trajectories required of the path dependency model are found very often in policy-making, these models are likely to remain less significant than process-sequencing models in describing the overall general pattern of policy dynamics.