Human Decision-Making under Limited Time
This addresses the problem of understanding real-world human choices under resource limitations for psychology and decision science, but it is incremental as it builds on existing bounded-rationality frameworks.
The study tackled the problem of human decision-making under time constraints by testing a bounded-rationality model based on statistical mechanics and information theory, finding that it accounts well for data from combinatorial puzzles and reveals insights into information capacity limits.
Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations. We found that our bounded-rational model accounts well for the data. The decomposition of the fitted model parameter into the subjects' expected utility function and resource parameter provide interesting insight into the subjects' information capacity limits. Our results confirm that humans gradually fall back on their learned prior choice patterns when confronted with increasing resource limitations.