What Can Deep Neural Networks Teach Us About Embodied Bounded Rationality

Rationality in Simon's bounded rationality is the principle that humans make decisions on the basis of step-by-step (algorithmic) reasoning using systematic rules of logic to maximize utility. Bounded rationality is the observation that the ability of a human brain to handle algorithmic complexity and large quantities of data is limited. Bounded rationality, in other words, treats a decision maker as a machine carrying out computations with limited resources. Under the principle of embodied cognition, a cognitive mind is an interactive machine. Turing-Church computations are not interactive, and interactive machines can accomplish things that no Turing-Church computation can accomplish. Hence, if rationality is computation, and bounded rationality is computation with limited complexity, then embodied bounded rationality is both more limited than computation and more powerful. By embracing interaction, embodied bounded rationality can accomplish things that Turing-Church computation alone cannot. Deep neural networks, which have led to a revolution in artificial intelligence, are both interactive and not fundamentally algorithmic. Hence, their ability to mimic some cognitive capabilities far better than prior algorithmic techniques based on symbol manipulation provides empirical evidence for the principle of embodied bounded rationality.

Automated summary

Rationality in Simon's bounded rationality refers to the idea that humans use step-by-step reasoning and logical rules to make decisions in order to maximize utility. Bounded rationality observes that the human brain has limitations in handling algorithmic complexity and large amounts of data. Under the principle of embodied cognition, a cognitive mind is seen as an interactive machine. Unlike Turing-Church computations, interactive machines can achieve tasks that cannot be accomplished by Turing-Church computations. Therefore, embodied bounded rationality, which is computation with limited complexity and interaction, is more limited than traditional computation but also more powerful. Deep neural networks, which are interactive and not fundamentally algorithmic, provide empirical evidence for the principle of embodied bounded rationality by mimicking cognitive capabilities better than previous symbol manipulation-based algorithmic techniques.