Abstrakti
The framework of cognitively bounded rationality treats problem solving as fundamentally rational, but emphasises that it is constrained by cognitive architecture and the task environment. This paper investigates a simple decision making heuristic, Take The Best (TTB), within that framework. We formulate TTB as a likelihood-based probabilistic model, where the decision strategy arises by probabilistic inference based on the training data and the model constraints. The strengths of the probabilistic formulation, in addition to providing a bounded rational account of the learning of the heuristic, include natural extensibility with additional cognitively plausible constraints and prior information, and the possibility to embed the heuristic as a subpart of a larger probabilistic model. We extend the model to learn cue discrimination thresholds for continuous-valued cues and experiment with using the model to account for biased preference feedback from a boundedly rational agent in a simulated interactive machine learning task.
Alkuperäiskieli | Englanti |
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Otsikko | CogSci 2018 Proceedings |
Kustantaja | COGNITIVE SCIENCE SOCIETY |
Sivut | 2214-2219 |
ISBN (elektroninen) | 978-0-9911967-8-4 |
Tila | Julkaistu - 2018 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | Annual Meeting of the Cognitive Science Society - Madison, Yhdysvallat Kesto: 25 heinäkuuta 2018 → 28 heinäkuuta 2018 Konferenssinumero: 40 |
Conference
Conference | Annual Meeting of the Cognitive Science Society |
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Lyhennettä | CogSci |
Maa | Yhdysvallat |
Kaupunki | Madison |
Ajanjakso | 25/07/2018 → 28/07/2018 |