- •Preface
- •Contents
- •Contributors
- •Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
- •1 Introduction
- •2 The Double-Slit Experiment
- •3 Interrogative Processes
- •4 Modeling the QWeb
- •5 Adding Context
- •6 Conclusion
- •Appendix 1: Interference Plus Context Effects
- •Appendix 2: Meaning Bond
- •References
- •1 Introduction
- •2 Bell Test in the Problem of Cognitive Semantic Information Retrieval
- •2.1 Bell Inequality and Its Interpretation
- •2.2 Bell Test in Semantic Retrieving
- •3 Results
- •References
- •1 Introduction
- •2 Basics of Quantum Probability Theory
- •3 Steps to Build an HSM Model
- •3.1 How to Determine the Compatibility Relations
- •3.2 How to Determine the Dimension
- •3.5 Compute the Choice Probabilities
- •3.6 Estimate Model Parameters, Compare and Test Models
- •4 Computer Programs
- •5 Concluding Comments
- •References
- •Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval
- •1 Introduction
- •3 Quantum Mathematics
- •3.1 Hermitian Operators in Hilbert Space
- •3.2 Pure and Mixed States: Normalized Vectors and Density Operators
- •4 Quantum Mechanics: Postulates
- •5 Compatible and Incompatible Observables
- •5.1 Post-Measurement State From the Projection Postulate
- •6 Interpretations of Quantum Mechanics
- •6.1 Ensemble and Individual Interpretations
- •6.2 Information Interpretations
- •7 Quantum Conditional (Transition) Probability
- •9 Formula of Total Probability with the Interference Term
- •9.1 Växjö (Realist Ensemble Contextual) Interpretation of Quantum Mechanics
- •10 Quantum Logic
- •11 Space of Square Integrable Functions as a State Space
- •12 Operation of Tensor Product
- •14 Qubit
- •15 Entanglement
- •References
- •1 Introduction
- •2 Background
- •2.1 Distributional Hypothesis
- •2.2 A Brief History of Word Embedding
- •3 Applications of Word Embedding
- •3.1 Word-Level Applications
- •3.2 Sentence-Level Application
- •3.3 Sentence-Pair Level Application
- •3.4 Seq2seq Application
- •3.5 Evaluation
- •4 Reconsidering Word Embedding
- •4.1 Limitations
- •4.2 Trends
- •4.4 Towards Dynamic Word Embedding
- •5 Conclusion
- •References
- •1 Introduction
- •2 Motivating Example: Car Dealership
- •3 Modelling Elementary Data Types
- •3.1 Orthogonal Data Types
- •3.2 Non-orthogonal Data Types
- •4 Data Type Construction
- •5 Quantum-Based Data Type Constructors
- •5.1 Tuple Data Type Constructor
- •5.2 Set Data Type Constructor
- •6 Conclusion
- •References
- •Incorporating Weights into a Quantum-Logic-Based Query Language
- •1 Introduction
- •2 A Motivating Example
- •5 Logic-Based Weighting
- •6 Related Work
- •7 Conclusion
- •References
- •Searching for Information with Meet and Join Operators
- •1 Introduction
- •2 Background
- •2.1 Vector Spaces
- •2.2 Sets Versus Vector Spaces
- •2.3 The Boolean Model for IR
- •2.5 The Probabilistic Models
- •3 Meet and Join
- •4 Structures of a Query-by-Theme Language
- •4.1 Features and Terms
- •4.2 Themes
- •4.3 Document Ranking
- •4.4 Meet and Join Operators
- •5 Implementation of a Query-by-Theme Language
- •6 Related Work
- •7 Discussion and Future Work
- •References
- •Index
- •Preface
- •Organization
- •Contents
- •Fundamentals
- •Why Should We Use Quantum Theory?
- •1 Introduction
- •2 On the Human Science/Natural Science Issue
- •3 The Human Roots of Quantum Science
- •4 Qualitative Parallels Between Quantum Theory and the Human Sciences
- •5 Early Quantitative Applications of Quantum Theory to the Human Sciences
- •6 Epilogue
- •References
- •Quantum Cognition
- •1 Introduction
- •2 The Quantum Persuasion Approach
- •3 Experimental Design
- •3.1 Testing for Perspective Incompatibility
- •3.2 Quantum Persuasion
- •3.3 Predictions
- •4 Results
- •4.1 Descriptive Statistics
- •4.2 Data Analysis
- •4.3 Interpretation
- •5 Discussion and Concluding Remarks
- •References
- •1 Introduction
- •2 A Probabilistic Fusion Model of Trust
- •3 Contextuality
- •4 Experiment
- •4.1 Subjects
- •4.2 Design and Materials
- •4.3 Procedure
- •4.4 Results
- •4.5 Discussion
- •5 Summary and Conclusions
- •References
- •Probabilistic Programs for Investigating Contextuality in Human Information Processing
- •1 Introduction
- •2 A Framework for Determining Contextuality in Human Information Processing
- •3 Using Probabilistic Programs to Simulate Bell Scenario Experiments
- •References
- •1 Familiarity and Recollection, Verbatim and Gist
- •2 True Memory, False Memory, over Distributed Memory
- •3 The Hamiltonian Based QEM Model
- •4 Data and Prediction
- •5 Discussion
- •References
- •Decision-Making
- •1 Introduction
- •1.2 Two Stage Gambling Game
- •2 Quantum Probabilities and Waves
- •2.1 Intensity Waves
- •2.2 The Law of Balance and Probability Waves
- •2.3 Probability Waves
- •3 Law of Maximal Uncertainty
- •3.1 Principle of Entropy
- •3.2 Mirror Principle
- •4 Conclusion
- •References
- •1 Introduction
- •4 Quantum-Like Bayesian Networks
- •7.1 Results and Discussion
- •8 Conclusion
- •References
- •Cybernetics and AI
- •1 Introduction
- •2 Modeling of the Vehicle
- •2.1 Introduction to Braitenberg Vehicles
- •2.2 Quantum Approach for BV Decision Making
- •3 Topics in Eigenlogic
- •3.1 The Eigenlogic Operators
- •3.2 Incorporation of Fuzzy Logic
- •4 BV Quantum Robot Simulation Results
- •4.1 Simulation Environment
- •5 Quantum Wheel of Emotions
- •6 Discussion and Conclusion
- •7 Credits and Acknowledgements
- •References
- •1 Introduction
- •2.1 What Is Intelligence?
- •2.2 Human Intelligence and Quantum Cognition
- •2.3 In Search of the General Principles of Intelligence
- •3 Towards a Moral Test
- •4 Compositional Quantum Cognition
- •4.1 Categorical Compositional Model of Meaning
- •4.2 Proof of Concept: Compositional Quantum Cognition
- •5 Implementation of a Moral Test
- •5.2 Step II: A Toy Example, Moral Dilemmas and Context Effects
- •5.4 Step IV. Application for AI
- •6 Discussion and Conclusion
- •Appendix A: Example of a Moral Dilemma
- •References
- •Probability and Beyond
- •1 Introduction
- •2 The Theory of Density Hypercubes
- •2.1 Construction of the Theory
- •2.2 Component Symmetries
- •2.3 Normalisation and Causality
- •3 Decoherence and Hyper-decoherence
- •3.1 Decoherence to Classical Theory
- •4 Higher Order Interference
- •5 Conclusions
- •A Proofs
- •References
- •Information Retrieval
- •1 Introduction
- •2 Related Work
- •3 Quantum Entanglement and Bell Inequality
- •5 Experiment Settings
- •5.1 Dataset
- •5.3 Experimental Procedure
- •6 Results and Discussion
- •7 Conclusion
- •A Appendix
- •References
- •Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
- •1 Introduction
- •2 Quantifying Relevance Dimensions
- •3 Deriving a Bell Inequality for Documents
- •3.1 CHSH Inequality
- •3.2 CHSH Inequality for Documents Using the Trace Method
- •4 Experiment and Results
- •5 Conclusion and Future Work
- •A Appendix
- •References
- •Short Paper
- •An Update on Updating
- •References
- •Author Index
- •The Sure Thing principle, the Disjunction Effect and the Law of Total Probability
- •Material and methods
- •Experimental results.
- •Experiment 1
- •Experiment 2
- •More versus less risk averse participants
- •Theoretical analysis
- •Shared features of the theoretical models
- •The Markov model
- •The quantum-like model
- •Logistic model
- •Theoretical model performance
- •Model comparison for risk attitude partitioning.
- •Discussion
- •Authors contributions
- •Ethical clearance
- •Funding
- •Acknowledgements
- •References
- •Markov versus quantum dynamic models of belief change during evidence monitoring
- •Results
- •Model comparisons.
- •Discussion
- •Methods
- •Participants.
- •Task.
- •Procedure.
- •Mathematical Models.
- •Acknowledgements
- •New Developments for Value-based Decisions
- •Context Effects in Preferential Choice
- •Comparison of Model Mechanisms
- •Qualitative Empirical Comparisons
- •Quantitative Empirical Comparisons
- •Neural Mechanisms of Value Accumulation
- •Neuroimaging Studies of Context Effects and Attribute-Wise Decision Processes
- •Concluding Remarks
- •Acknowledgments
- •References
- •Comparison of Markov versus quantum dynamical models of human decision making
- •CONFLICT OF INTEREST
- •Endnotes
- •FURTHER READING
- •REFERENCES
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86 A. Wichert and C. Moreira
Fig. 3. Probability waves for the experiments described in Tables 1 and 2. In plots (a) - (d) the waves p(¬y, θ¬ ) are around p(¬y), (for (e) see Fig. 2). In the plots (i) - (iv) the waves p(y, θ) are around p(y). Additionally the values psub (¬y) and psub (y) are indicated by a line. Note that the curves in the plots (i) – (iv) overlap.
3 Law of Maximal Uncertainty
How can we choose the phase θ for a probability value that reflects the case of not knowing what the correct value is and without losing information about the probability waves? We answer this question by proposing the law of maximal uncertainty is based on two principles, the principle of entropy and the mirror principle.
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3.1Principle of Entropy
For the case in which both interval do not overlap
Iy ∩ I¬y = |
(38) |
the values of the waves that are closest to the equal distribution are chosen. By doing so the uncertainty is maximised and the information about the probability wave is not lost. The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge is the one with largest entropy [11–13]. In the case of a binary event the highest entropy corresponds to an equal distribution
H = −p(y) · log2 p(y) − p(¬y) · log2 p(¬y) = −log2 · 0.5 = 1 bit. (39)
For p(y) ≤ p(¬y) the closest values to the equal distribution are for θ = 0, the ends of the interval for
pq (y) = p(y) + 2 · |
p(y, x) · p(y, ¬x) |
≈ 2 · p(y) |
(40) |
||
and |
|
||||
pq (¬y) = 1 − pq (y). |
(41) |
||||
For p(¬y) ≤ p(y) the closest values to the equal distribution are |
|
||||
p(¬y)q = p(¬y) + 2 · |
|
≈ 2 · p(¬y) |
(42) |
||
p(¬y, x) · p(¬y, ¬x) |
|||||
and |
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||||
pq (y) = 1 − pq (¬y). |
(43) |
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3.2 Mirror Principle |
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For the case where the intervals overlap |
|
||||
Iy ∩ I¬y = |
(44) |
an equal distribution maximises the uncertainty but loses the information about the probability wave. To avoid the loss we do not change the entropy of the system we use the positive interference as defined by the law of balance. When the intervals overlap the positive interference is approximately of the size of smaller probability value since the arithmetic and geometric means approach each other, see Eq. 26. We increase uncertainty by mirror the “probability values”. For the case p(y) ≤ p(¬y) we assume
pq (¬y) = 2 · |
p(y, x) · p(y, ¬x) |
≈ p(y) |
(45) |
and |
|
||
pq (y) = 1 − pq (¬y). |
(46) |
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For p(¬y) ≤ p(¬y) |
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|||
|
pq (y) = 2 · |
|
≈ p(¬y) |
(47) |
|
p(¬y, x) · p(¬y, ¬x) |
|||
and |
pq (¬y) = 1 − pq (y). |
(48) |
||
|
Table 3 summarises the intervals that describe the probability waves, the resulting probabilities pq that are based on the law of maximal uncertainty, the subjective probability and the classical probability values. We compare the results that
Table 3. Probability waves, the resulting probabilities pq that are based on the law of maximal uncertainty, the subjective probability and the classical probability values. Entries (a)–(e) are based on the principle of entropy and entries (i)–(iv) are based mirror principle.
Experiment |
I¬y |
psub (¬y) |
pq (¬y) |
p(¬y) |
(a) |
[0.84, 0.97] |
0.63 |
0.84 |
0.91 |
|
|
|
|
|
(b) |
[0.59, 1] |
0.72 |
0.59 |
0.79 |
|
|
|
|
|
(c) |
[0.76,1] |
0.66 |
076 |
0.88 |
|
|
|
|
|
(d) |
[0.90, 1] |
0.88 |
0.90 |
0.95 |
|
|
|
|
|
(e) Average |
[0.77, 0.99] |
0.72 |
0.77 |
0.88 |
|
|
|
|
|
Experiment |
Iy |
psub (y) |
pq (y) |
p(y) |
(i) |
[0.27, 0.98] |
0.37 |
0.36 |
0.64 |
|
|
|
|
|
(ii) |
[0.20, 0.98] |
0.48 |
0.39 |
0.59 |
|
|
|
|
|
(iii) |
[0.09, 0.99] |
0.41 |
0.45 |
0.54 |
|
|
|
|
|
(vi) Average |
[0.19, 0.99] |
0.42 |
0.40 |
0.59 |
|
|
|
|
|
are based on probability waves and the law of maximal uncertainty with previous works that deal with predictive quantum-like models for decision making, see Table 4. The dynamic heuristic [18] is used quantum-like bayesian networks. Its parameters are determined by examples from a domain. On the other hand In the Quantum Prospect Decision Theory the values need not to be adapted to a domain. The quantum interference term is determined by the Interference Quarter Law. The quantum interference term of total probability is simply fixed to a value equal to 0.25 [22].