9 - Representing Problem Representation

Summary

Introduction

If we are ever to reconstruct the path to the Cold War's end and sort out the relative validity of rival theories concerning the impact of its many discrete contributing causes, we, thus, will need to map the cognitive terrain of the actors in the process first. In this way we could describe the decision calculus of those who orchestrated the Cold War's end and trace the balance between “opportunity and willingness” in these policy makers’ conceptualizations of the structural possibilities of their environments and their decision structures. From these data, we could estimate the accuracy of their images. And the images themselves could be placed into a dynamic process model that visualizes the contributing factors colliding and influencing one another at certain points and not at others, with some factors moving together but not affecting one another and other factors in constant interaction. Such a map of the conceptual terrain would chart the ways in which the [factors] were perceived by Soviet leaders in the flow of changing circumstances as the Cold War perished. - Charles Kegley (1994: 35)

In Part I of this volume, James Voss presents a discussion of problem representation as it fits into the problem-solving framework. Voss also discusses several problems with the problem-solving model (the need for a metric, lack of application to groups and organizations, defining constraints and assessing their importance, how information is retrieved and used, the difficulty of prediction), which, at their heart, point to the difficulty of building appropriate models without knowing the content of particular problem representations or the processes of reasoning used. To move forward, as Charles Kegley notes, we need to “map the cognitive terrain” and build “dynamic process models.” That is, we need to build carefully representations (models) of the problem representations of individuals whose policy choices we seek to explain and predict. Furthermore, these representations provide one set of inputs for dynamic process models, with other inputs coming from the policy makers’ environment. Representing problem representations provides a basis for solving the “problems of problem solving” and provides meaning to discussions of change by suggesting mechanisms for change. A useful strategy for accomplishing these goals is a combination of careful content analysis and computational modeling, which also offers a reasonable hope of prediction in addition to understanding.