Decisionmaking, Risk, and Uncertainty: An Analysis of Climate Change Policy

This article explores four questions. First, what theoretical frameworks help describe policy failure and success? Second, how might the decision that leads to failure or success be understood in terms of differing concepts of rationality and decisionmaking? Third, how does the discussion of risk and uncertainty as originally proposed by Frank Knight (1921) apply to a better understanding of both the first and second questions? Fourth, what is the relationship between serial and parallel processing and how are these administrative systems related to important aspects of the prior questions? Our chief contribution in this article is to show the ways in which these questions and their respective theoretical frameworks are interrelated as applied to one important contemporary policy question--climate change. We think our proposed integration of the various literatures offers important insights into the challenges policymakers face in deciding whether or not to adopt a particular policy. The first question is based on the notion that decisionmakers can commit two fundamental types of policy errors: the wrong policy can be implemented or there can be a failure to implement the needed policy. Based on work by Heimann (1993), we construct a simple model of hypothesis testing using Type I versus Type II errors that is common in the social and behavioral sciences. The second question is based on Vernon Smith's (2003) discussion of two fundamental types of decisionmaking: constructivist and ecological. The former approach, which has important similarities to expected unity theory, represents a highly deductive, top-down approach that places a high degree of confidence in model building and the ability to make accurate predictions about phenomena over extended periods. The latter represents a more bottom-up strategy that assumes decisionmakers do not have all encompassing information available to them and thus rely on heuristic models of decisionmaking. Such decisionmaking does not reject theory, but allows for theory-building to be done incrementally with ongoing adjustments of beliefs. The third question provides an analytic framework for evaluating the probability of different kinds of events. Knight (1921) drew the important distinction between risk and uncertainty, and the consequences for understanding the probability of the occurrence or non-occurrence of particular events (see Raines and Jung 1992, Jarvis 2008). Risk refers to a calculated and predictable outcome, whereas uncertainty refers to an event for which the probability of outcomes is difficult to objectively measure. We believe that policymakers, as well as policy analysts, are all too likely to misinterpret one set of events for another, leading to faulty policymaking. The final conceptual framework in which we examine the relevance of the forgoing issues to questions of administrative systems offers additional and important insights into the nature of decisionmaking in organizations. Ultimately, we think our analysis offers a roadmap for future research that will be of assistance to scholars and decisionmakers in better understanding decisionmaking and its consequences. In the following section, we discuss the desirability of policy action or inaction (Type I versus Type II errors). We then move to a comparison of constructivist as opposed to ecological decisionmaking and the distinction between Knightian risk and uncertainty in the subsequent two sections. We then apply our arguments to the issue of climate change. To Act or Not to Act: The Nature of Policy Success or Failure What is the nacre of policy success or failure? Many scholars have explored that question from the standpoint of what happens after a decision has been made. Our approach is somewhat different. Instead of asking how many resources are devoted to a policy or how principal-agent problems influence policy outcomes, we inquire about the fundamental nature of decisionmaking--that is, should one act or not act? …