Quantifying the role of complexity in a system’s performance
In this work we studied the relationship between a system’s complexity and its performances in solving a given task. Although complexity is generally assumed to play a key role in an agent’s performance, its influence has not been deeply investigated in the past. To this aim we analysed a predator–prey scenario where a prey had to develop several strategies to counter an increasingly skilled predator. The predator has several advantages over the prey, thus requiring the prey to develop more and more complex strategies. The prey is driven by a fully recurrent neural network trained using genetic algorithms.We conducted several experiments measuring the prey’s complexity using Kolmogorov algorithmic complexity. Our finding is that, in accordance to what was believed in literature, complexity is indeed necessary to solve non-trivial tasks. The main contribution of this work lies in having proved the necessity of complexity to solve non-trivial tasks. This has been made possible by blending together a goal oriented system with a complex one. An experiment is provided to distinguish between the complexity of a chaotic system and the complexity of a random one.