The evolutionary origin of Bayesian heuristics and finite memory
Bayes' rule is a fundamental principle that has been applied across multiple disciplines. However, few studies have addressed its origin as a cognitive strategy or the underlying basis for generalization from a small sample. Using a simple binary choice model subject to natural selection, we de...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2022
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| Online Access: | https://hdl.handle.net/1721.1/144200 |
| _version_ | 1826200080927948800 |
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| author | Lo, Andrew W Zhang, Ruixun |
| author2 | Sloan School of Management. Laboratory for Financial Engineering |
| author_facet | Sloan School of Management. Laboratory for Financial Engineering Lo, Andrew W Zhang, Ruixun |
| author_sort | Lo, Andrew W |
| collection | MIT |
| description | Bayes' rule is a fundamental principle that has been applied across multiple disciplines. However, few studies have addressed its origin as a cognitive strategy or the underlying basis for generalization from a small sample. Using a simple binary choice model subject to natural selection, we derive Bayesian inference as an adaptive behavior under certain stochastic environments. Such behavior emerges purely through the forces of evolution, despite the fact that our population consists of mindless individuals without any ability to reason, act strategically, or accurately encode or infer environmental states probabilistically. In addition, three specific environments favor the emergence of finite memory-those that are Markov, nonstationary, and environments where sampling contains too little or too much information about local conditions. These results provide an explanation for several known phenomena in human cognition, including deviations from the optimal Bayesian strategy and finite memory beyond resource constraints. |
| first_indexed | 2024-09-23T11:30:53Z |
| format | Article |
| id | mit-1721.1/144200 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:30:53Z |
| publishDate | 2022 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1442002023-04-20T19:18:54Z The evolutionary origin of Bayesian heuristics and finite memory Lo, Andrew W Zhang, Ruixun Sloan School of Management. Laboratory for Financial Engineering Sloan School of Management Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Bayes' rule is a fundamental principle that has been applied across multiple disciplines. However, few studies have addressed its origin as a cognitive strategy or the underlying basis for generalization from a small sample. Using a simple binary choice model subject to natural selection, we derive Bayesian inference as an adaptive behavior under certain stochastic environments. Such behavior emerges purely through the forces of evolution, despite the fact that our population consists of mindless individuals without any ability to reason, act strategically, or accurately encode or infer environmental states probabilistically. In addition, three specific environments favor the emergence of finite memory-those that are Markov, nonstationary, and environments where sampling contains too little or too much information about local conditions. These results provide an explanation for several known phenomena in human cognition, including deviations from the optimal Bayesian strategy and finite memory beyond resource constraints. 2022-08-03T17:32:40Z 2022-08-03T17:32:40Z 2021 2022-08-03T17:29:08Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144200 Lo, Andrew W and Zhang, Ruixun. 2021. "The evolutionary origin of Bayesian heuristics and finite memory." iScience, 24 (8). en 10.1016/J.ISCI.2021.102853 iScience Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Lo, Andrew W Zhang, Ruixun The evolutionary origin of Bayesian heuristics and finite memory |
| title | The evolutionary origin of Bayesian heuristics and finite memory |
| title_full | The evolutionary origin of Bayesian heuristics and finite memory |
| title_fullStr | The evolutionary origin of Bayesian heuristics and finite memory |
| title_full_unstemmed | The evolutionary origin of Bayesian heuristics and finite memory |
| title_short | The evolutionary origin of Bayesian heuristics and finite memory |
| title_sort | evolutionary origin of bayesian heuristics and finite memory |
| url | https://hdl.handle.net/1721.1/144200 |
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