Elementary probabilistic operations: a framework for probabilistic reasoning

被引:0
|
作者
Macho, Siegfried [1 ]
Ledermann, Thomas [2 ]
机构
[1] Univ Fribourg, Dept Psychol, Fribourg, Switzerland
[2] Florida State Univ, Dept Human Dev & Family Sci, Tallahassee, FL USA
关键词
Elementary probabilistic operations; Computational analysis; Problem space representation; Quantitative inference schemas; Bayesian reasoning; DUAL-PROCESS THEORIES; MENTAL-MODEL-THEORY; INDIVIDUAL-DIFFERENCES; ECOLOGICAL RATIONALITY; NATURAL FREQUENCIES; INFORMATION; PERFORMANCE; NUMERACY; REPRESENTATION; SOLVE;
D O I
10.1080/13546783.2023.2259541
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
The framework of elementary probabilistic operations (EPO) explains the structure of elementary probabilistic reasoning tasks as well as people's performance on these tasks. The framework comprises three components: (a) Three types of probabilities: joint, marginal, and conditional probabilities; (b) three elementary probabilistic operations: combination, marginalization, and conditioning, and (c) quantitative inference schemas implementing the EPO. The formal part of the EPO framework is a computational level theory that provides a problem space representation and a classification of elementary probabilistic problems based on computational requirements for solving a problem. According to the EPO framework, current methods for improving probabilistic reasoning are of two kinds: First, reduction of Bayesian problems to a type of probabilistic problems requiring less conceptual and procedural competencies. Second, enhancing people's utilization competence by fostering the application of quantitative inference schemas. The approach suggests new applications, including the teaching of probabilistic reasoning, using analogical problem solving in probabilistic reasoning, and new methods for analyzing errors in probabilistic problem solving.
引用
收藏
页码:259 / 300
页数:42
相关论文
共 50 条
  • [1] A Framework for Probabilistic Reasoning on Knowledge Graphs
    Bellomarini, Luigi
    Benedetto, Davide
    Laurenza, Eleonora
    Sallinger, Emanuel
    [J]. BUILDING BRIDGES BETWEEN SOFT AND STATISTICAL METHODOLOGIES FOR DATA SCIENCE, 2023, 1433 : 48 - 56
  • [2] A probabilistic framework for memory-based reasoning
    Kasif, S
    Salzberg, S
    Waltz, D
    Rachlin, J
    Aha, DW
    [J]. ARTIFICIAL INTELLIGENCE, 1998, 104 (1-2) : 287 - 311
  • [4] Probabilistic reactor design in the framework of elementary process functions
    Kaiser, Nicolas M.
    Flassig, Robert J.
    Sundmacher, Kai
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2016, 94 : 45 - 59
  • [5] Comparing approximate reasoning and probabilistic reasoning using the Dempster-Shafer framework
    Yager, Ronald R.
    [J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2009, 50 (05) : 812 - 821
  • [6] Probabilistic reasoning by neurons
    Tianming Yang
    Michael N. Shadlen
    [J]. Nature, 2007, 447 : 1075 - 1080
  • [7] Probabilistic reasoning by neurons
    Yang, Tianming
    Shadlen, Michael N.
    [J]. NATURE, 2007, 447 (7148) : 1075 - U2
  • [8] Reasoning with Probabilistic Ontologies
    Riguzzi, Fabrizio
    Bellodi, Elena
    Lamma, Evelina
    Zese, Riccardo
    [J]. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 4310 - 4316
  • [9] Probabilistic constraint reasoning
    Elsa Carvalho
    [J]. Constraints, 2015, 20 (4) : 509 - 510
  • [10] PROBABILISTIC DEFAULT REASONING
    PAASS, G
    [J]. LECTURE NOTES IN COMPUTER SCIENCE, 1991, 521 : 76 - 85