What Can Deep Neural Networks Teach Us About Embodied Bounded Rationality
Author(s): Edward A. Lee
Citation
Edward A. Lee. "What Can Deep Neural Networks Teach Us About Embodied Bounded Rationality". Frontiers in Psychology, 25, April 2022.
Abstract
'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 tomimic some cognitive capabilities far better than prior algorithmic techniques based on symbol manipulation provides empirical evidence for the principle of embodied bounded rationality.
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Edward A. Lee. "<a href="https://www.icyphy.org/publications/2022_Lee/">What Can Deep Neural Networks Teach Us About Embodied Bounded Rationality</a>". <i>Frontiers in Psychology</i>, 25, April 2022. -
Plain Text
Edward A. Lee. "What Can Deep Neural Networks Teach Us About Embodied Bounded Rationality". Frontiers in Psychology, 25, April 2022. -
BibTeX
@article{Lee:22:BoundedRationality, author = {Edward A. Lee}, title = {What Can Deep Neural Networks Teach Us About Embodied Bounded Rationality},
journal = {Frontiers in Psychology},
volume = {25},
month = {April},
year = {2022},
doi = {10.3389/fpsyg.2022.761808},
abstract = {'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 tomimic some cognitive capabilities far better than prior algorithmic techniques based on symbol manipulation provides empirical evidence for the principle of embodied bounded rationality.}, URL = {https://www.icyphy.org/publications/2022_Lee/} }