Learning Through Inexperts

One of the activities considered a great benefit to the transparency that social networks afford is the ability find, connect with, and learn from others. If we narrate our work in social networks, we gain contribution, get questions answered, and can see the work of others which improves our own knowledge and skills.

Many critics of social networking have exclaimed "what if the information being shared is wrong?" My typical response is that inaccurate information in the open sure beats it being perpetuated and propagated where nobody can see it and correct it - which is likely what is happening now!

But what if observing and mimicking others' mistakes actually helps us learn and perform better?  It's hard to promote the idea that we should tell folks to find inexpert, someone muddling through to watch and learn from but some interesting research suggests that we'd be better off than just going solo.

In Alan Winfield's post "Noisy Learning Speeds up Group Learning" He shares some interesting observations from recent robotics research. In the studies, a learning (novice) robot with the ability to observe and process another robot's novice actions, learned to solve its own problem faster than if it had learned through its own experience. Most interesting is that the robot's attempts to mimic are often imperfect, meaning the observing robot can't always do exactly as the robot it observed can do.

 "...it's as if you are spying on the other cook - try to copy what they're doing but get it wrong and, by accident, end up with better chicken soup." - Alan Winfield

So, two wrongs make a right?

One lesson we might take away is that in our ever increasing complex environments, where being an expert is a temporary status, is we need to learn more how to learn through others not just from them.