New perspectives in computational intelligence: nothing so intelligent as randomness, nothing so effective as asymmetry

Leaving the expert systems framework of the 80s and the early connectionist paradigm of the 90s, the scientific community is now drawn by social computing paradigms, where a huge number of agents individually do an elementary job and jointly give rise to a sophisticated functionality. There is no doubt that the complexity of this functionality is connected to the randomness of the agents’ work. What comes increasingly clear is that this randomness is a guarantee of success, not a drawback, provided we avoid falling in the ordinary Gaussian phenomenology in the province of the central limit theorem. We envisage a jointly biased asymmetry of the agents’ actions to be the main feature distinguishing them from the molecules of a gas in Brownian motion, and toss this idea in the paper through specific statistical models we elaborated in recent works.