Reducing errors in judgment requires a disciplined process.

Envision the following situations: A board of directors considers acquiring a competitor. A marketing team decides whether to launch a new product. A venture capital investment committee chooses among an array of startups to fund.

All those strategic decisions share a common feature: They are evaluative judgments. To make such tough calls, people must boil down a large amount of complex information to either (1) numerical scores for competing options or (2) a yes-no decision on whether to choose a specific path. Of course, some management decisions are made without weighing quite so much information. But strategic decisions tend to involve the distillation of complexity into a single path forward.

Given how unreliable human judgment is, all evaluations are susceptible to errors. These errors can stem from known cognitive biases — or they can be random errors, sometimes called “noise.” Unreliability in judgment has long been recognized and studied, particularly in the context of decision-making about hiring. We draw inspiration from that body of research and experience to suggest a practical, broadly applicable approach to reducing errors in strategic decision-making. We call it the Mediating Assessments Protocol (MAP), and we’ll describe it here, after discussing the underpinning research. (This research was supported by an Australian Research Council Discovery Grant to coauthor Dan Lovallo.)

Strategic Options Are Like Job Candidates

The body of research on the job interview, the most common tool for employee selection, contains a wealth of information about the accuracy of evaluative judgments.1 Most companies still use traditional (unstructured) interviews to make a global evaluation. The interviewer starts with an open mind, accumulates information about the candidate, and then reaches a conclusion.


1. J. Levashina, C.J. Hartwell, F.P. Morgeson, and M.A. Campion, “The Structured Employment Interview: Narrative and Quantitative Review of the Research Literature,” Personnel Psychology 67, no. 1 (spring 2014): 241-293.

2. J. Dana, R. Dawes, and N. Peterson, “Belief in the Unstructured Interview: The Persistence of an Illusion,” Judgment and Decision Making 8, no. 5 (September 2013): 512-520; and D.A. Moore, “How to Improve the Accuracy and Reduce the Cost of Personnel Selection,” California Management Review 60, no. 1 (November 2017): 8-17.

3. M.R. Barrick, S.L. Dustin, T.L. Giluk, G.L. Stewart, et al., “Candidate Characteristics Driving Initial Impressions During Rapport Building: Implications for Employment Interview Validity,” Journal of Occupational and Organizational Psychology 85, no. 2 (June 2012): 330-352.

4. C.Y. Olivola and A. Todorov, “Fooled by First Impressions? Reexamining the Diagnostic Value of Appearance-Based Inferences,” Journal of Experimental Social Psychology 46, no. 2 (March 2010): 315-324.

5. D. Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux, 2011).

6. C.R. Sunstein and R. Hastie, Wiser: Getting Beyond Groupthink to Make Groups Smarter (Boston: Harvard Business Review Press, 2015).

7. R.S. Nickerson, “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises,” Review of General Psychology 2, no. 2 (June 1998): 175-220.

8. A. Tversky and D. Kahneman, “Judgment Under Uncertainty: Heuristics and Biases,” Science 185, no. 4157 (Sept. 27, 1974): 1,124-1,131.

9. D. Kahneman, A.M. Rosenfield, L. Gandhi, and T. Blaser, “Noise: How to Overcome the High, Hidden Cost of Inconsistent Decision Making,” Harvard Business Review, no. 10 (October 2016): 36-43.

10. J. Levashina et al., “The Structured Employment Interview.”

11. D. Kahneman, Thinking, Fast and Slow.

12. L. Bock, Work Rules! Insights From Inside Google That Will Transform How You Live and Lead (London: Hachette U.K., 2015).

13. A. Tversky and D. Kahneman, “Judgment Under Uncertainty.”

14. D. Kahneman, Thinking, Fast and Slow.

15. O. Sibony, D. Lovallo, and T.C. Powell, “Behavioral Strategy and the Strategic Decision Architecture of the Firm,” California Management Review 59, no. 3 (May 2017): 5-21.

16. D. Kahneman et al., “Noise.”

i. D. Lovallo and D. Kahneman, “Delusions of Success: How Optimism Undermines Executives’ Decisions,” Harvard Business Review 81, no. 7 (July 2003): 56-63.

2 Comments On: A Structured Approach to Strategic Decisions

  • Ricky Morton | March 7, 2019

    I’ve used similar mediated decision-making approaches for many years in making decisions on awarding large government contracts in the UK.
    In the UK public sector we are subject to EU procurement regulations and so the process must be seen to be clear and the guidance to suppliers must let them know how we will decide what we think good looks like for the deal – Price, Quality, Risk, etc., with all their subcategories.

    I know the MAP approach works as many of the vehicles I’ve set up – Joint Ventures, outsourcings, Strategic Partnerships – have outlived their original context as the organisations involved evolved, self-destructed or changed direction.

    The big difference between the approach I’ve used and the proposed MAP approach is that the EU regulations mandate that you assign explicit weightings to the criteria that you use.
    This does indeed lead to pushback from executive and political leadership who feel that the value of their individual expertise is being diminished as things are reduced to a simple formula.
    As an advisor I have to manage this carefully as I look to help my clients make the best decisions, but I have the personal evidence base, built up over years, that the approach works.

  • Wolfje Van Dijk | March 17, 2019

    I’d like to appraise anyone who finds this interesting of the work of two academic powerhouses who Dr. Kahnemann structurally seems to ignore, quite possibly because they hold opposing academic views … The first is Dr. Gerd Gigerenzer (Munich University), who has demonstrated in numerous experiments that heuristics outperform decision-making in contexts of high uncertainty when compared to statistical analyses. Requiring less data heuristics lead to similar or even better predictions. The second author is Dr. Kathleen Eisenhardt (Stanford University). Her studies have shown that simple rules result in better strategic decision-making. She even demonstrates that firms that have evolved internal learning mechanisms on these simple rules outperform competitors. Reading the works of these two authors as well as the interesting views put forward by Kahnemann et al will provide a full scope of both the benefits and drawbacks of using simple rules in strategic decision-making.

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