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Decision making under high complexity: a computational model for the science of muddling through

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I welcome any attempt to specify more precisely than I have done the circumstances in which “muddling through” is a defensible strategy for decision making. Charles Lindblom (1964, p. 157).

Abstract

It is well recognized that many organizations operate under situations of high complexity that arises from pervasive interdependencies between their decision elements. While prior work has discussed the benefits of low to moderate complexity, the literature on how to cope with high complexity is relatively sparse. In this study, we seek to demonstrate that Lindblom’s decision-making principle of muddling through is a very effective approach that organizations can use to cope with high complexity. Using a computational simulation (NK) model, we show that Lindblom’s muddling through approach obtains outcomes superior to those obtained from boundedly rational decision-making approaches when complexity is high. Moreover, our results also show that muddling through is an appropriate vehicle for bringing in radical organizational change or far-reaching adaptation.

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Notes

  1. For our purposes, a decision comprises of a certain number of dimensions/elements representing a set of managerial imperatives. For example, when a firm is considering a decision to reduce costs, some imperatives may be: the extent to which such reduction can come from lowering inventory, reducing R&D spending, halting certain planned capital expenditure and cutting the Sales and Marketing budget. When each imperative is further broken down to actual activities to be undertaken, a decision configuration is obtained, comprising the superset of the statuses (undertaken/withdrawn) of all activities across the four imperatives. Research problems are frequently framed as that of finding decision configurations with superior fitness (performance).

  2. Lindblom (1959) is merely referred in the passing in seminal publications by organizational scholars, for example, Levinthal and March (1981), Levitt and March (1988).

  3. In a system comprised of modules, a nearly-decomposable structure is one where there is intense interaction between elements of a module, and very weak interaction between elements housed in different modules.

  4. In centralized search, a randomly-chosen decision element is changed at a time and the new configuration is accepted only if it has higher fitness; search stops when it is no longer possible to obtain higher fitness by changing one element (Siggelkow and Rivkin 2005). The intelligent model of myopic local search (Chanda 2021) constitutes a variant involving less resource-intensive computation. (Please see Table 1).

  5. Elsewhere, Bendor (1995) offers a mathematical model incorporating Lindblom’s ideas. However, Bendor’s model introduces a host of assumptions—incorporated solely for the sake of mathematical tractability—that have no analogue in Lindblom’s work. This makes it infeasible to compare Bendor’s work with that of organization and management scholars developing formal theory by computational simulation modeling.

  6. We may note though that Ethiraj and Levinthal (2004a, 2004b) focus on finding superior fitness outcomes under low and moderate complexity. This is outside our scope. The interdependence patterns in Ethiraj and Levinthal (2004a, 2004b) are infeasible when K is high, i.e. close to N (our main focus area). Nonetheless, in Sect. 5.2 we inspect the outcomes from muddling through under varying patterns of interdependence.

  7. In a live organization, managers decide the starting point. Later in our exposition, when we move to modeling by computer simulations, we invoke a start from a random decision configuration.

  8. Again, while managers may decide which action to tackle first based on their experience, in the simulation model we simply pick one action randomly.

  9. In a live organization, managers decide the sequence that imperatives are taken up. In our computer model we choose one cluster at random, in each time-step.

  10. Interested readers may refer to Levinthal (1997) for a more detailed description of the NK model.

  11. Hamming distance between two decision configurations is the number of bits in which they differ.

  12. For example, Marengo et al. (2000), Marengo and Dosi (2005), Brusoni et al. (2007)

  13. Interested readers may refer to Silverman et al. (2021) for an application of Lindblom’s theory to the context of politics among states and dissident members therein.

  14. Readers may refer to Chanda (2017) for a discussion on alternative conceptualizations of complexity.

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Acknowledgements

We thank Ann Langley, Dan Levinthal, Giovanni Gavetti and Henrich Greve for providing helpful comments on earlier drafts.

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Authors and Affiliations

  1. Indian Institute of Management Bangalore, Bannerghatta Road, Bangalore, Karnataka, 560076, India

    Sai Yayavaram

  2. C-101 Academic Block, Indian Institute of Management Indore, Prabandh Shikhar, Rau-Pithampur Road, Indore, Madhya Pradesh, 453556, India

    Sasanka Sekhar Chanda

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  1. Sai Yayavaram
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Correspondence to Sasanka Sekhar Chanda.

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Yayavaram, S., Chanda, S.S. Decision making under high complexity: a computational model for the science of muddling through. Comput Math Organ Theory 29, 300–335 (2023). https://doi.org/10.1007/s10588-021-09354-9

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