Double-loop learning: Difference between revisions

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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:

{{Quote|text=[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.|author=Chris Argyris |source=<ref name="c-argyris-learning"/>{{rp|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.<ref>{{cite book |author=Mildeova, S., Vojtko V. |title=Systémová dynamika |year=2003 |isbn=80-245-0626-2 |publisher=Oeconomica |___location=Prague |pages=19–24 |language=Czech}}</ref>