Arguments for the Effectiveness of Human Problem Solving
This work provides insights for cognitive psychology and artificial intelligence, but it is incremental as it builds on existing theoretical frameworks without introducing new empirical data or methods.
The paper tackles the problem of understanding the effectiveness of human problem solving by analyzing its sources and cognitive mechanisms, arguing that these mechanisms operate similarly to the optimal Solomonoff strategy from general problem solving systems.
The question of how humans solve problem has been addressed extensively. However, the direct study of the effectiveness of this process seems to be overlooked. In this paper, we address the issue of the effectiveness of human problem solving: we analyze where this effectiveness comes from and what cognitive mechanisms or heuristics are involved. Our results are based on the optimal probabilistic problem solving strategy that appeared in Solomonoff paper on general problem solving system. We provide arguments that a certain set of cognitive mechanisms or heuristics drive human problem solving in the similar manner as the optimal Solomonoff strategy. The results presented in this paper can serve both cognitive psychology in better understanding of human problem solving processes as well as artificial intelligence in designing more human-like agents.