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A Model of Muddling Through

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  • Bendor, Jonathan

Abstract

As arguments about the effectiveness of “muddling through†have proven frustratingly inconclusive, incrementalism—once a major approach to the study of boundedly rational policy processes—has gone dormant. In an attempt to revitalize the debate, I present a formal model of muddling through. The model, by clarifying the logical structure of the informal theory, presents a clearer target for criticism. More importantly, it establishes numerous deductive results. First, some of Lindblom's less controversial conjectures—about the benefits of seriality (repeated attacks on the same policy problem) and redundancy (multiple decision makers working on the same problem)—turn out to be correct if conflict across policy domains is absent or takes certain specified forms. But given other empirically reasonable types of conflict, even these claims are wrong. Second, the advantages of incremental (local) policy search (Lindblom's best-known and most controversial claim) turn out to be still less well founded: in many empirically plausible contexts the claim is invalid.

Suggested Citation

  • Bendor, Jonathan, 1995. "A Model of Muddling Through," American Political Science Review, Cambridge University Press, vol. 89(4), pages 819-840, December.
  • Handle: RePEc:cup:apsrev:v:89:y:1995:i:04:p:819-840_09
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    Cited by:

    1. Kollman, Ken & Miller, John H. & Page, Scott E., 1997. "Landscape formation in a spatial voting model," Economics Letters, Elsevier, vol. 55(1), pages 121-130, August.
    2. Martens, Rudy & Matthyssens, Paul & Vandenbempt, Koen, 2012. "Market strategy renewal as a dynamic incremental process," Journal of Business Research, Elsevier, vol. 65(6), pages 720-728.
    3. Elliott Ash & Massimo Morelli & Matia Vannoni, 2022. "More Laws, More Growth? Evidence from U.S. States," BAFFI CAREFIN Working Papers 22178, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. I. A. Antipin & N. Yu. Vlasova, 2020. "Incremental approach to regional strategising: Theory, methodology, practices," Journal of New Economy, Ural State University of Economics, vol. 21(3), pages 73-90, October.
    5. Sai Yayavaram & Sasanka Sekhar Chanda, 2023. "Decision making under high complexity: a computational model for the science of muddling through," Computational and Mathematical Organization Theory, Springer, vol. 29(2), pages 300-335, June.
    6. Alberto Asquer & Inna Krachkovskaya, 2021. "Uncertainty, institutions and regulatory responses to emerging technologies: CRISPR Gene editing in the US and the EU (2012–2019)," Regulation & Governance, John Wiley & Sons, vol. 15(4), pages 1111-1127, October.
    7. Bettis-Outland, Harriette, 2012. "Decision-making's impact on organizational learning and information overload," Journal of Business Research, Elsevier, vol. 65(6), pages 814-820.
    8. Wiek, Arnim & Walter, Alexander I., 2009. "A transdisciplinary approach for formalized integrated planning and decision-making in complex systems," European Journal of Operational Research, Elsevier, vol. 197(1), pages 360-370, August.

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