Abstract

Most non-trivial policy analyses confront the problems of complexity either through the definition of objectives or through such instrumental means as the selection of alternatives, tools of evaluation, or time horizons. multidimensional nature of policy issues and the incomplete nature of analytical understanding generate uncertainties that directly affect the probability of success in implementation. This disturbing situation has led scholars to search for ways to overcome, or at least bypass, the obstacles to good analysis and execution. Although most agree that the interrelation between facts and values, or ends and means, and the lack of good theories are among the main constraints that limit successful policy analysis, not all accept one single method with which to resolve these analytical shortcomings. Thus, some scholars propose techniques that could be utilized in an attempt to increase the reliability of operating systems and safeguard them against failure,1 while others promote conservative, status quo-oriented measures for public policy deduced from the assumption that the known (present) is inherently preferable to the uncertainties of the future.2 There are those who see the market as a viable and preferable alternative to government-directed public policy,3 and those who propose to extend our knowledge and understanding capabilities beyond the limitations of science, rationality or the conventional methods of doing things.4 two articles by Martin Kreiger and Thomas Kaplan appear to stem from the tradition developed by scholars from the last group. Kreiger's Big Decisions and the Culture of Decision Making and Kaplan's The Narrative Structure of Policy Analysis are unconventional and even counter-conventional. While the former is concerned with the nature of complex phenomenon and its meanings, i.e., the question of what is, the latter directs its attention to the question of how-how to bypass and overcome complexity in analysis and policy advice. Both attempt to enrich

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