Double-loop learning

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Double-loop learning is the modification or rejection of a goal in the light of experience. Double-loop learning recognises that the way a problem is defined and solved can be a source of the problem.[1]

Detail

Double-loop learning is contrasted with "single-loop learning": the repeated attempt at the same problem, with no variation of method and without ever questioning the goal. Argyris described the distinction between single-loop and double-loop learning using the following analogy:

   [A] thermostat that automatically turns on the heat whenever the temperature in a room drops below 68°F is a good example of single-loop learning. A thermostat that could ask, "why am I set to 68°F?" and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaged in double-loop learning.[1]: 99 

Double loop learning is used when it is necessary to change the mental model on which a decision depends. Unlike single loops, this model includes a shift in understanding, from simple and static to broader and more dynamic, such as taking into account the changes in the surroundings and the need for expression changes in mental models.[2]

Process of learning
 
Single-loop learning
 
Double-loop learning

See also

References

  1. ^ a b Argyris, Chris (1991). "Teaching Smart People How to Learn" (PDF). Harvard Business Review. 4 (2): 99–109. Retrieved 22 November 2015.
  2. ^ Mildeova, S., Vojtko V. (2003). Systémová dynamika (in Czech). Prague: Oeconomica. pp. 19–24. ISBN 80-245-0626-2.{{cite book}}: CS1 maint: multiple names: authors list (link)

Further reading