Don’t use a tame approach to “wicked” problems

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Don’t use a tame approach to “wicked” problems

People have a pervasive tendency to substitute simple questions for difficult ones, as Kahneman points out in Thinking, Fast and Slow. Even worse, they most often don’t even realize it. Our fast  instinctive thinking  “finds a related question that is easier and will answer it,” he says, most often without us even being aware of it.

This is one reason policy outcomes and market returns and business decisions often turn out very badly. It is very difficult to be get the right answer if you’re solving the wrong problem.

So part of any sensible approach to a decision is to stop, think, and  recognize what kind of problem you are dealing with. Here’s one good way to look at it,  invented in a famous paper by Horst Rittel and Melvin Webber in 1973.  Up until the 1960s, they say, people had a great deal of confidence that experts and scientists could solve almost all the problems of society. But once the easy problems had been solved, the more difficult and stubborn ones remained.  They argue:

The problems that scientists and engineers have usually focused upon are mostly “tame” or “benign” ones. As an example, consider a problem of mathematics, such as solving an equation; or the task of an organic chemist in analyzing the structure of some unknown compound; or that of the chessplayer attempting to accomplish checkmate in five moves. For each the mission is clear. It is clear, in turn, whether or not the problems have been solved.

Wicked problems, in contrast, have neither of these clarifying traits; and they include nearly all public policy issues–whether the question concerns the location of a freeway, the adjustment of a tax rate, the modification of school curricula, or the confrontation of crime.

They identify ten characteristics of a wicked problem, including:

  1. There is no definitive formulation of a wicked problem.
  2. Wicked problems have no “stopping rule”, i.e you can’t be sure when you have actually found a perfect solution. You stop when you have run out of time, money, or when you decide an approach is “good enough.” In other words, you can’t “solve” the bond market once and for all.
  3. Solutions to wicked problems are not true-or-false, but good-or-bad.
  4. There is no immediate and no ultimate test of a solution to a wicked problem, i.e There are so many possible repercussions and connections that “you can never trace all the waves through all the affected lives ahead of time or within a limited time span.”
  5. Every solution to a wicked problem is a “one-shot operation”; because there is no opportunity to learn by trial-and-error, so every attempt counts significantly

Skipping over a few,

8. Every wicked problem can be considered to be a symptom of another problem.

10. “The planner has no right to be wrong.” By this they mean the decision-maker is going to be held liable for the consequences, and pay a price if it turns out badly.

Are you starting to see the similarities here?  Financial markets have reached the same point.  Public policy reached that point decades ago, which helps to explain why monetary policy has so often been a tale of alternating complacency and disaster.

The “tame” problems have largely been solved, the ones that are easily quantified and reducible to tractable models or algorithms. Algorithms have automated some of the learning process and squeezed out whatever value there is in the big data sets. The low-hanging fruit has been picked.

Result: there isn’t much profit or alpha left to exploit that way. That leaves the “wicked” problems. Public policy issues, like monetary policy or Greek defaults or Chinese politics, are “wicked” issues.  And central banks stumble when they try to apply tame approaches to their wicked problems as well.

Compare the similar distinction between puzzles and mysteries, or linear and nonlinear system problems.

The first step in dealing with wicked problems is realize a tame approach won’t work.

 

2017-05-11T17:32:42+00:00 February 27, 2015|Assumptions, Decisions, Systems and Complexity|