Article++ (Teaching smart people how to learn – Chris Argyris)

Chris Argyris brings forward a very useful article which I believe to be one of the best articles I’ve read on the subject not just because it gives some new phrases like ‘single loop’ or “double loop” but because the insight he delivers is very realistic and personally, I found considerable improvement in my practical experiences.

What is Learning? How to learn? Argyris does not start off by forwarding his doctrine on these questions but rather focuses on human behavior and our learning patterns. On an empirical justification, he points out that learning is the process of solving problems out of one’s comfort zones of presuppositions and beliefs. According to him, failure is an important anecdote to better learning in the sense that those who have never failed, have never seen an uncomfortable or unforeseeable situation and are not equipped to handle under distress. Another reason for people failing to learn beyond their routine tasks (termed single loop learning) is the difference between their perceived behavior and their actual behavior. By nature, humans tend to blame the environment and contradict themselves when the perceived and the actual don’t match.

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Argyris then explains why people avoid learning (double loop) and attributes the fear of failure and over ambitious goals to be the reason. People never set average standards, they set the best landmark to achieve and often fail to achieve them. Yet subconsciously, people deny these failures to their own setting of high standards but rather to the external environment. The fear of failure stops people from adventuring outside their comfort zones and into self analysis which hurts the ego.

This thesis is very well backed up by empirical human psychology and provides some tips to avoid locking oneself up into a single loop learning situation. In Statistical Learning Theory, we call a learning model is over fit (and hence, not acceptable) if it becomes too hard lined on its belief based on past knowledge. A flexible learning model, one that has not ‘converged’ and stuck in a local loop is sought after.

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