{"title":"Stochastic choice with bounded processing capacity","authors":"Thierry Marchant , Arunava Sen","doi":"10.1016/j.jmp.2023.102771","DOIUrl":null,"url":null,"abstract":"<div><p>We propose and characterize a class of stochastic decision functions for a decision-maker who has a capacity for processing at most <span><math><mi>k</mi></math></span>-alternatives at a time. When faced with a menu containing more than <span><math><mi>k</mi></math></span> alternatives, she randomly chooses a sub-menu of size <span><math><mi>k</mi></math></span> with uniform probability and selects the best alternative according to a strict ordering <span><math><mo>≻</mo></math></span>. For smaller menus, she chooses the best alternative according to <span><math><mo>≻</mo></math></span>.</p></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"114 ","pages":"Article 102771"},"PeriodicalIF":2.2000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mathematical Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022249623000275","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 2
Abstract
We propose and characterize a class of stochastic decision functions for a decision-maker who has a capacity for processing at most -alternatives at a time. When faced with a menu containing more than alternatives, she randomly chooses a sub-menu of size with uniform probability and selects the best alternative according to a strict ordering . For smaller menus, she chooses the best alternative according to .
期刊介绍:
The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome.
Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation.
The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology.
Research Areas include:
• Models for sensation and perception, learning, memory and thinking
• Fundamental measurement and scaling
• Decision making
• Neural modeling and networks
• Psychophysics and signal detection
• Neuropsychological theories
• Psycholinguistics
• Motivational dynamics
• Animal behavior
• Psychometric theory