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Double-loop learning


Double-loop learning entails the modification of goals or decision-making rules in the light of experience. The first loop uses the goals or decision-making rules, the second loop enables their modification, hence "double-loop". Double-loop learning recognises that the way a problem is defined and solved can be a source of the problem.

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. Chris 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

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.

Single-loop learning

Double-loop learning

A Behavioral Theory of the Firm (1963) describes how organizations learn, using (what would now be described as) double-loop learning:

An organization ... changes its behavior in response to short-run feedback from the environment according to some fairly well-defined rules. It changes rules in response to longer-run feedback according to more general rules, and so on.




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Wikipedia

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