How Not to Think to Get Outside the Box

7/10/2015 in Blog Categories, News

Some problems are bigger than others.  Consider the case of Archimedes and the water screw:  how do you get lots of water uphill when you live in the 3rd century BCE and don’t have a pump?  While the origin of Archimedes’ screw is subject to debate, the fact is that sometime between 700 BCE and 200 BCE the Assyrians or the Greeks figured out a way to move water uphill efficiently without having mechanical, machine powered pumps.  The solution was to enclose a double or triple helical surface, more commonly known as a screw, inside a pipe.  One end of the pipe is placed in water and as the screw shaft is turned, the bottom scoops up water, which slides up on the tube until it reaches the top and spills out.  The screw was used to move water to and from irrigation ditches and to drain water from low-lying areas or mines.  Part of the genius of the screw was that the seal between the screw edge and the pipe did not need to be watertight since it would work as long as the volume of water being scooped up was larger than the volume of water leaking down.  Regardless, Archimedes’ screw was able to move significantly more water uphill both faster and at lower energy costs than using buckets and pails.

Archimedes’ screw is remarkable when you think about it:  in a pre-industrial society without electric or other non-manual power sources, someone figured out a way to move water uphill with efficiency comparable to a mechanical pump using a screw.  Not exactly the method that would come to mind for most of us when thinking about how to move water uphill.  This sort of unconventional, out-of-the box thinking is an example of what business and cognitive science experts call “distant search”; which has been neatly defined as “problem solving outside the neighborhood of what is already known.”  Distant search contrasts with “local search,” which is the most common way we solve problems.  Local search is “problem solving that focuses only on the neighborhood of what is already known, drawing on the pre-existing knowledge base and on how the problem (or similar problems) had been solved in the past.” 

The fact that most problem solving is accomplished using local search is not surprising.  Most of us are familiar with the idea that specialization and expertise are important factors in achieving high levels of competence in most fields.  Most have probably heard about Malcolm Gladwell’s argument that expertise arises after 10,000 hours of practice in any given field.  Some may also be familiar with research demonstrating that the single biggest factor predicting a surgeon’s success rate at a particular procedure is the frequency with which the surgeon performs the procedure.  All of this may leave us with the impression that the key factor to achieving success is practice, repetition, and familiarity.  The bottom line is that for most problems, expertise is useful because most problems arise out of and can be solved using “the neighborhood of what is already known.”

Undoubtedly practice, familiarity, and repetition help build the fund of knowledge available for local searches and are useful for solving most of the day-to-day problems we encounter; however, these factors are insufficient for solving the biggest and most difficult problems we encounter.  The reason is fairly simple:  local searches are cognitively rigid.  Unfortunately, cognitive rigidity acts as a bar to creative and unconventional thinking, which often makes the difference between whether a seemingly intractable problem gets solved or not.  For the most difficult problems, innovative and unexpected solutions are usually needed but local search is narrow, predictable, and based on existing knowledge of the status quo.  Local search is not particularly creative or innovative.  For more difficult problems, a different type of solution is needed and the solution is found through distant search.

In distant search, a solution is sought that is significantly different from what might be considered the intuitive or logical starting point.  The Archimedes screw is a classic historical example of distant search.   Others would include Copernicus, who did not continue the refinement of Ptolemaic solutions to the problems caused by the geocentric model (i.e. the need for epicycles) but instead proposed the wholly different and (it would turn out) correct solution of a heliocentric model.  Copernicus had, at some point, to abandon the idea of the geocentric model in order to conceive of a different solution to the problem.  This move away, this search for a solution different in quality and kind, is distant searching.  Einstein’s conception of relativity is a similar example of distant search:  a wholly novel solution to cosmological questions that differed in quality and kind from Newton’s solution.  Simply refining or correcting the flaws in Newton’s mechanics would not have led to a correct model, but would have only further refined an incorrect model.  The model would have still been incorrect.  The flaws would have remained. 

Contemporary examples of distant search include e-books and the iPhone.  Publishers’ use of CD-ROM in response to the digitization of information storage and transmission was the result of local search.  This is in contrast to Amazon’s distant search which resulted in the development and adoption of e-books.  The iPhone touchscreen is another great example of distant search.  Rather than putting a keyboard on a phone, a la Blackberry, Apple developed a more elegant and unexpected solution to the interface between user and PDA.  In the annals of history, we know who which innovations will be remembered and which will be forgotten.  It is often this way with distant search.  Distant search is the Eureka moment, the breakthrough that changes everything, the revolution that changes industries and societies.      

The problem most of us have with distant search is that it essentially requires us to stop thinking about the problem.  Usually this is a matter of happenstance.  We get so frustrated with a difficult problem that we simply walk away from it.  The solution then comes to us at an unexpected moment (if it comes to us at all).  Consider the case of a musician who can’t complete a partially formed melody.  Local search would involve playing the fragment over and over, maybe adding to it or varying it a bit. The problem is that the musician has reached what could be termed an arrest of thought (she’s stumped, in other words).  Continuing to repeat the fragment that led to the arrest of thought is only likely to deepen its hold on her brain and increase her frustration.  Eventually she will abandon the fragment and move on.  If she is to complete the melody, she will most likely find the solution when she is not thinking about the problem.  Perhaps the sound of subway doors closing triggers a connection previously unavailable to her and causes her to find a solution.  Or maybe the susurration of the wind shaking leaves triggers the connection that leads to the solution.  The point is that the musician is most likely to solve the problem with information “from outside the neighborhood of what is already known” about the problem and this information will only become available when she is not thinking about the problem.  

Unfortunately, happenstance is inefficient, unpredictable, and not always successful.  The process of happenstance is a distant search whereby the musician is confronted with something, however trivial or mundane, that causes her to think of the problem in a novel way which offers a hitherto inaccessible solution to the problem.  However, the connection or solution that presents itself is wholly dependent on the chance that something in the musician’s daily life will trigger the connection.  Perhaps she never rides the subway at the moment when her mind is clear and receptive.  Perhaps she needs to hear the sound of leaves rustling in the wind but never takes a walk in the woods.  Regardless, happenstance is a poor method to rely on for performing distant search.

Fortunately, we can train ourselves to use distant search more efficiently.  The key is to become self-aware of our thought process and how we are thinking about the problem.  The term that has sway in the cognitive sciences right now is mindful metacognition.  In layperson’s terms this simply means self-aware thinking about thinking.  Rather than dive into the problem, mindful metacognition would have us think about the problem, but then would have us abandon the thought rather than follow it.  Then we would allow other thoughts to form in the cognitive space vacated by our initial solution to the problem.  Mindful metacognition offers a more targeted method to achieve distance search.  The process allows the musician in our example to consciously shut off the ruminative stream of thought that has got her stuck and to open her field of consciousness to new thoughts in close enough proximity to the melody fragment problem that the new thoughts become available as possible solutions or modes of thought that can precipitate a solution to the melody fragment.  The likelihood of reaching a distant solution in a shorter time is thereby increased versus happenstance alone.

Hence, when a roadblock arises, try to use mindful metacognition to explore distant search solutions.  In a sense, using mindful metacognition to trigger distant search is like an internal email to colleagues or an online query:  rather than sending out a question to a few or many others, mindful metacognition essentially lets you float the question to your whole mind a memory store.  Thus, the free associations that you allow to form and retrieve thoughts and memories that increase the likelihood that somewhere in our amazing, complex, and data-filled brain a solution or way of seeing the problem that will lead to the solution already exists and just needs to be brought out for the connection to the problem to be established.  These thoughts and memories are like the vast array of potential individuals available to us when we crowdsource the solution to a problem.  Not every problem requires distant search, but when one arises it is certainly better to use a targeted method that offers a reasonable chance at a solution than to use local search and beat one’s head against the wall over and over or to rely on happenstance, which may never, in fact, happen.

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