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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

15. Summary

verfasst von : Richard M. Adler

Erschienen in: Bending the Law of Unintended Consequences

Verlag: Springer International Publishing

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Abstract

In Greek mythology, the hero Odysseus had to sail through a narrow—and—lethal strait on his journey home from the Trojan War. The strait was guarded on one side by Scylla, a terrifying six-headed sea monster, and on the other side by Charybdis, an enormous and lethal whirlpool. Businesses face similarly unavoidable menaces of cognitive biases and bounded rationality when navigating critical decisions. Ignoring either one of these perils, or both, invites calamity. The fundamental question addressed by this book is how to improve critical decisions in the face of the Law of Unintended Consequences (LUC). This chapter distills the prescription offered in Part II of the book into a four-step methodology: (1) Institute a formal process for making and executing critical decisions. (2) Understand how LUC compromises critical decisions at different phases of this process. (3) Anticipate and compensate for cognitive biases. (4) Test drive critical decisions to push back against the bounds of rationality.

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Vorheriges Kapitel Pragmatics
Fußnoten
1
Kahneman [4] suggests that System 1 and 2 cognitive processes interact one another to aggravate LUC. Distortions induced by System 1 biases compromise System 2 tasks such as sensemaking and formulating decision options. So countering biases amplifies benefits “downstream” in decision-making.
 
2
Technology can push back other bounds. Software tools for modeling, simulation, business intelligence (BI), and analytics running on powerful computers relax economic constraints, limited cognitive capacities, and reasoning errors. They also take some of the sting out of uncertainty by making data gathering, sensemaking, and analysis of decision options more thorough, less fallible, easier, faster, and cheaper.
 
3
Measurement and feedback are crucial elements of all process management disciplines: it is impossible to improve performance if businesses don’t monitor what happened; analyze what worked and what didn’t; and refine their decision models and the process itself. Kahneman suggests that this is the single most important thing that leaders can do to improve the quality of their decisions. Schrage [7].
 
4
See, for example, process standards such as ISO [3] and Humphrey [2], and exemplars such as Keeney [5] and Russo and Schoemaker [6].
 
5
The dynamics of critical decisions are far more complex than those of inanimate physical systems. Physicists have luxuries that critical decision-makers lack. They can draw sharp boundaries in models to make them closed systems and ignore external forces, or treat them as constant to simplify simulations. And the dynamic laws that govern inanimate objects don’t need to account for intentional actors with adaptive behaviors.
 
6
Recently, a few simulation tools have entered the market that support mixed or “multi-modal” models, for example, by combining Monte Carlo, system dynamics and sometimes agent-based systems.
 
7
Test drive analyses may lead you to conclude that you need to adjust your objectives (or goals).
 
8
Taleb [8].
 
9
Point counting systems draw on probability theory. The decision test drive method marshals three knowledge resources that most companies fail to exploit: their experiential knowledge about the behavior of their market and key actors; expert knowledge of situational dynamics built into dynamic scenarios; and the wizardry behind the simulation toolbox.
 
10
The test drive method also accommodates decisions structured as real options (cf. Sect. 8.​4). Real options essentially allow decision-makers to stall or postpone their bets, deferring investment choices until sufficient time passes to resolve key uncertainties. As such, real options are a potent adjunct that further reduces exposure to LUC. The method integrates them by provisioning templates for dynamic scenarios to capture decisions with the set of input parameters needed to specify relevant real options and extending the simulation toolbox with the desired valuation techniques (e.g., Black-Sholes or binomial trees).
 
11
Hogarth [1] makes a similar point that choosing decision-making methods is itself a critical decision.
 
Literatur
1.
Hogarth, Robin M. 1980. Judgement and Choice: The Psychology of Decision. Hoboken, NJ: John Wiley & Sons.
2.
Humphrey, Watts S. (1988). Characterizing the software process: a maturity framework. IEEE Software.5(2): 73–79. doi:10.1109/52.2014.
3.
4.
Kahneman, Daniel. 2011. Thinking Fast and Slow. New York: Farrar, Strauss & Giroux.
5.
Keeney, Ralph L. 1992. Value-Focused Thinking: A Path to Creative Decisionmaking. Cambridge, MA: Harvard University Press.
6.
Russo, J. Edward, and Paul J.H. Schoemaker. 2001. Winning Decisions: Getting It Right the First Time. New York. Doubleday Currency.
7.
8.
Taleb, Nassim Nicholas. 2007. The Black Swan: The Impact of the Highly Improbable. New York: Random House.
Metadaten
Titel
Summary
verfasst von
Richard M. Adler
Copyright-Jahr
2020
Verlag
Springer International Publishing
Buch
Bending the Law of Unintended Consequences

Print ISBN: 978-3-030-32713-2

Electronic ISBN: 978-3-030-32714-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-32714-9_15