Literature DB >> 29149998

Computational Complexity and Human Decision-Making.

Peter Bossaerts1, Carsten Murawski2.   

Abstract

The rationality principle postulates that decision-makers always choose the best action available to them. It underlies most modern theories of decision-making. The principle does not take into account the difficulty of finding the best option. Here, we propose that computational complexity theory (CCT) provides a framework for defining and quantifying the difficulty of decisions. We review evidence showing that human decision-making is affected by computational complexity. Building on this evidence, we argue that most models of decision-making, and metacognition, are intractable from a computational perspective. To be plausible, future theories of decision-making will need to take into account both the resources required for implementing the computations implied by the theory, and the resource constraints imposed on the decision-maker by biology.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  artificial intelligence; computational modelling; metacognition; rationality

Mesh:

Year:  2017        PMID: 29149998     DOI: 10.1016/j.tics.2017.09.005

Source DB:  PubMed          Journal:  Trends Cogn Sci        ISSN: 1364-6613            Impact factor:   20.229


  11 in total

1.  Neuroscience for architecture: The evolving science of perceptual meaning.

Authors:  Sergei Gepshtein; Joseph Snider
Journal:  Proc Natl Acad Sci U S A       Date:  2019-07-05       Impact factor: 11.205

2.  Suboptimality in Perceptual Decision Making.

Authors:  Dobromir Rahnev; Rachel N Denison
Journal:  Behav Brain Sci       Date:  2018-02-27       Impact factor: 12.579

3.  Divergent Strategies for Learning in Males and Females.

Authors:  Cathy S Chen; R Becket Ebitz; Sylvia R Bindas; A David Redish; Benjamin Y Hayden; Nicola M Grissom
Journal:  Curr Biol       Date:  2020-10-29       Impact factor: 10.834

4.  Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.

Authors:  Fernando Suarez Saiz; Corey Sanders; Rick Stevens; Robert Nielsen; Michael Britt; Leemor Yuravlivker; Anita M Preininger; Gretchen P Jackson
Journal:  JCO Clin Cancer Inform       Date:  2021-01

5.  Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science.

Authors:  Iris van Rooij; Giosuè Baggio
Journal:  Perspect Psychol Sci       Date:  2021-01-06

6.  Uncertainty and computational complexity.

Authors:  Peter Bossaerts; Nitin Yadav; Carsten Murawski
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-02-18       Impact factor: 6.237

Review 7.  Decision Support Systems in Oncology.

Authors:  Seán Walsh; Evelyn E C de Jong; Janna E van Timmeren; Abdalla Ibrahim; Inge Compter; Jurgen Peerlings; Sebastian Sanduleanu; Turkey Refaee; Simon Keek; Ruben T H M Larue; Yvonka van Wijk; Aniek J G Even; Arthur Jochems; Mohamed S Barakat; Ralph T H Leijenaar; Philippe Lambin
Journal:  JCO Clin Cancer Inform       Date:  2019-02

Review 8.  Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing.

Authors:  Sergey Budaev; Tore S Kristiansen; Jarl Giske; Sigrunn Eliassen
Journal:  R Soc Open Sci       Date:  2020-12-23       Impact factor: 2.963

9.  Decision-making psychology and method under zero-knowledge context.

Authors:  Neng-Gang Xie; Meng Wang; Ya-Yun Dai; Ye Ye; Joel Weijia Lai; Lu Wang; Kang Hao Cheong
Journal:  Sci Rep       Date:  2022-02-24       Impact factor: 4.379

10.  Task-independent metrics of computational hardness predict human cognitive performance.

Authors:  Juan Pablo Franco; Karlo Doroc; Nitin Yadav; Peter Bossaerts; Carsten Murawski
Journal:  Sci Rep       Date:  2022-07-28       Impact factor: 4.996

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.