It’s worth considering the effects of the “exploration/exploitation” tradeoff: decreasing coordination/efficiency can increase the efficacy of search in problem space over the long run, precisely because efforts are duplicated. When efforts are duplicated, you increase the probability that someone will find the optimal solution. When everyone is highly coordinated, people all look in the same place and you can end up getting stuck in a “local optimum”—a place that’s pretty good, but can’t be easily improved without scrapping everything and starting over.
It should be noted that I completely buy the “lowest hanging fruit is already picked” explanation. The properties of complex search have been examined somewhat in depth by Stuart Kauffman (“nk space”). These ideas were developed with biological evolution in mind but have been applied to problem solving. In essence, he quantifies the intuition you can improve low-quality things with a lot less search time than it takes to improve high-quality things.
These are precisely the types of spaces in which coordination/efficiency is counterproductive.
I’d be interested in more resources regarding the “low-hanging fruit” theory as related to the structure of ideaspace and how/whether nk space applies to that. Any good resources-for-beginners on Kauffman’s work?
I read “At Home In The Universe: The Search for the Laws of Self Organization and Complexity” which is a very accessible and fun read—I am not a physicist/mathematician/biologist, etc, and it all made sense to me. The book talks about evolution, both biological and technological.
And the model described in that book has been quite commonly adapted by social scientists to study problem solving, so it’s been socially validated as a good framework for thinking about scientific research.