Transactions on Computational Collective Intelligence XX

Abstract

Abstract. The dual-process theory of human cognition proposes the existence of two systems for decision-making: a slower, deliberative, problemsolving system and a quicker, reactive, pattern-recognition system. We alter the balance of these systems in a number of computational simulations using three types of agent equipped with a novel, hybrid, humanlike cognitive architecture. These agents are situated in the stochastic, multi-agent Tileworld domain, whose complexity can be precisely controlled and widely varied. We explore how agent performance is affected by different balances of problem-solving and pattern-recognition, and conduct a sensitivity analysis upon key pattern-recognition system variables. Results indicate that pattern-recognition improves agent performance by as much as 36.5% and, if a balance is struck with particular pattern-recognition components to promote pattern-recognition use, performance can be further improved by up to 3.6%. This research is of interest for studies of expert behaviour in particular, and AI in general.