The Spilled Milk Syndrome
Creativity expresses itself by disrupting the prevailing order. Alas the new ideas need to fit well into their own order. Being order-defying, innovators tend to underestimate the importance of order, and administrative rigidity, and end up with a stream of innovation that gets lost, overrun, forgotten, unattended, resulting in poor innovation productivity.
When your cow gives you a lot of milk, you better take along enough bottles, otherwise the milk spills to the ground. The same spilled milk syndrome applies to innovation.
Curiously the more sizzling innovators tend to be more disrespectful of order, routine, pattern, and rigidity. These geniuses can cure cancer, and forget about it when the next morning they have a grand idea about climate change.
We teach innovators to be (i) aware of the spilled milk syndrome, and (ii) take mindful steps to bring order to their innovation stream: documentation, reporting, budgeting of cost and time, appraisal of the innovation load ahead, etc.
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Learn, Don't Burn!
Facing a great risk for error, don't take direct action, buy a delay, and use it to learn the situation better.
Consider a problem for which there are two theories describing what is going on. Each theory is matched with a solution, however each solution will be harmful if in fact the opposite theory is the correct one. In the case where the chance for either theory to be the right one is about 50%, it would be very risky to opt for any of the two matching solutions. In that case it is advisable to buy time, if necessary, to dedicate resources, not to apply a solution per se but to better learn the situation with the aim if decreasing its entropy, reducing the prevailing equivocation. Learning the case at hand better may result in one theory marked as 80% chance to be correct, and the other theory claiming only 20%, The risk for harm in choosing the solution for the first theory is much reduced. This is lesson of "Learn, Don't Burn!"
We learn how to appraise the probability range among theories, how to reduce the entropy, and how to optimize resource management between learning and fixing.
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