Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Why use quantum theory for cognition and decision? Some compelling reasons
- 2 What is quantum theory? An elementary introduction
- 3 What can quantum theory predict? Predicting question order effects on attitudes
- 4 How to apply quantum theory? Accounting for human probability judgment errors
- 5 Quantum-inspired models of concept combinations
- 6 An application of quantum theory to conjoint memory recognition
- 7 Quantum-like models of human semantic space
- 8 What about quantum dynamics? More advanced principles
- 9 What is the quantum advantage? Applications to decision making
- 10 How to model human information processing using quantum information theory
- 11 Can quantum systems learn? Quantum updating
- 12 What are the future prospects for quantum cognition and decision?
- Appendices
- References
- Index
4 - How to apply quantum theory? Accounting for human probability judgment errors
Published online by Cambridge University Press: 05 August 2012
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Why use quantum theory for cognition and decision? Some compelling reasons
- 2 What is quantum theory? An elementary introduction
- 3 What can quantum theory predict? Predicting question order effects on attitudes
- 4 How to apply quantum theory? Accounting for human probability judgment errors
- 5 Quantum-inspired models of concept combinations
- 6 An application of quantum theory to conjoint memory recognition
- 7 Quantum-like models of human semantic space
- 8 What about quantum dynamics? More advanced principles
- 9 What is the quantum advantage? Applications to decision making
- 10 How to model human information processing using quantum information theory
- 11 Can quantum systems learn? Quantum updating
- 12 What are the future prospects for quantum cognition and decision?
- Appendices
- References
- Index
Summary
How can we use quantum theory to model other phenomena of interest to researchers in cognition and decision making? Quantum theory is not easy for researchers in cognition and decision making to accept. In fact, quantum mechanics was not easy for physicists to accept either, but it was forced on them by several paradoxical findings that could not be explained using classical physics. We have a similar problem in cognition and decision making – there are numerous paradoxical findings that just seem irrational according to classic probability theory. For example, under some conditions, people judge the probability of event A and B to be greater than the probability of event B, which is called the conjunction fallacy (Tversky & Kahneman, 1983). Also, under the same conditions, they judge the probability of A or B to be less than the probability of event A (Carlson & Yates, 1989), which is called the disjunction fallacy. In this chapter we examine how quantum probability theory explains these and other puzzling results from human probability judgment research. This chapter has two main parts. In the first part, we use a quantum model to derive qualitative predictions for conjunction errors, disjunction errors, and other closely related findings. The first section provides a general set of predictions that do not depend on specific assumptions about the features used to represent events, and the predictions are parameter free. In the second part, we examine the quantitative predictions of the quantum model for a Bayesian inference task, which we use to explain order effects on inference.
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- Chapter
- Information
- Quantum Models of Cognition and Decision , pp. 117 - 142Publisher: Cambridge University PressPrint publication year: 2012