Retroflexive decision-making

How do companies make major decisions?  The gurus of classical Decision Theory – people like economist Jimmie Savage and statistician Dennis Lindley - tell us that there is only one correct way to make decisions:  List all the possible actions, list the potential consequences of each action, assign utilities  and probabilities of occurence to each consequence, multiply these numbers together for each consequence and then add the resulting products for each action to get an expected utility for each action, and finally choose that action which maximizes expected utility.

There are many, many problems with this model, not least that it is not what companies – or intelligent, purposive individuals for that matter – actually do.  Those who have worked in companies know that nothing so simplistic or static describes intelligent, rational decision making, nor should it.  Moreover, that their model was flawed as a description of reality was known at the time to Savage, Lindley, et al,  because it was pointed out to them six decades ago by people such as George Shackle, an economist who had actually worked in industry and who drew on his experience.  The mute, autistic behemoth that is mathematical economics, however, does not stop or change direction merely because its utter disconnection with empirical reality is noticed by someone, and so - TO THIS VERY DAY – students in business schools still learn the classical theory.  I guess for the students it’s a case of:  Who are we going to believe – our textbooks, or our own eyes?    From my first year as an undergraduate taking Economics 101, I had trouble believing my textbooks.

So what might be a better model of decision-making?  First, we need to recognize that corporate decision-making is almost always something dynamic, not static – it takes place over time, not in a single stage of analysis, and we would do better to describe a process, rather than just giving a formula for calculating an outcome.   Second, precisely because the process is dynamic, many of the inputs assumed by the classical model do not exist, or are not known to the participants, at the start, but emerge in the course of the decision-making process.   Here, I mean things such as:  possible actions, potential consequences, preferences (or utilities), and measures of uncertainty (which may or may not include probabilities).     Third, in large organizations, decision-making is a group activity, with inputs and comments from many people.   If you believe – as Savage and Lindley did – that there is only one correct way to make a decision, then your model would contain no scope for subjective inputs or stakeholder revisions, which is yet another of the many failings of the classical model.    Fourth, in the real world, people need to consider – and do consider – the potential downsides as well as the upsides of an action, and they need to do this – and they do do this – separately, not merged into a summary statistic such as “utility”.   So, if  one possible consequence of an action-option is catastrophic loss, then no amount of maximum-expected-utility quantitative summary gibberish should permit a rational decision-maker to choose that option without great pause (or insurance).   Shackle knew this, so his model considers downsides as well as upsides.   That Savage and his pals ignored this one can only assume is the result of the impossibility of catastrophic loss ever occurring to a tenured academic.

So let us try to articulate a staged process for what companies actually do when they make major decisions, such as major investments or new business planning:

  1. Describe the present situation and the way or ways it may evolve in the future.  We call these different future paths scenarios.   Making assumptions about the present and the future is also called taking a view.
  2. For each scenario, identify a list of possible actions, able to be executed under the scenario.
  3. For each scenario and action, identify the possible upsides and downsides.
  4. Some actions under some scenarios will have attractive upsides.   What can be done to increase the likelihood of these upsides occurring?  What can be done to make them even more attractive?
  5. Some actions under some scenarios will have unattractive downsides.   What can be done to eliminate these downsides altogether or to decrease their likelihood of occurring?   What can be done to ameliorate, to mitigate, to distribute to others, or to postpone the effects of these downsides?
  6. In the light of what was learned in doing steps 1-5, go back to step 1 and repeat it.
  7. In the light of what was learned in doing steps 1-6, go back to step 2 and repeat steps 2-5.  For example, by modifying or combining actions, it may be posssible to shift attractive upsides or unattractive downsides from one action to another.
  8. As new information comes to hand, occasionally repeat step 1. Repeat step 7 as often as time permits.

This decision process will be familiar to anyone who has prepared a business plan for a new venture, either for personal investment, or for financial investors and bankers, or for business partners.   Having access to spreadsheet software such as Lotus 1-2-3 or Microsoft EXCEL has certainly made this process easier to undertake.  But, contrary to the beliefs of many, people made major decisons before the invention of spreadsheets, and they did so using processes similar to this, as Shackle’s work evidences.

Because this model involves revision of initial ideas in repeated stages, it bears some resemblance to the retroflexive argumentation theory of philosopher Harald Wohlrapp.  Hence, I call it Retroflexive Decision Theory.  I will explore this model in more detail in future posts.

References:

D. Lindley [1985]:  Making Decisions.  Second Edition. London, UK: John Wiley and Sons.

L. J. Savage [1950]: The Foundations of Statistics.  New York, NY, USA:  Wiley.

G. L. S. Shackle [1961]: Decision, Order and Time in Human Affairs. Cambridge, UK:  Cambridge University Press.

H. Wohlrapp [1998]:  A new light on non-deductive argumentation schemes.  Argumentation, 12: 341-350.

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