- •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
suai.ru/our-contacts |
quantum machine learning |
104 C. Moreira and A. Wichert
all possible maximum expected utilities that the player can achieve by varying the quantum interference term θ in Eq. 20 for a personal strategy of confessing (defecting) or remaining silent (cooperating), respectively. On the left of Fig. 4, it is represented all the values of θ that satisfy the condition that EU [Cooperate] > EU [Def ect], i.e., all the values of the quantum interference parameter θ that will maximise the utility of cooperation rather than defect. One can note that, for experiment of Shafir and Tversky (1992) (as well as in the remaining works of the literature analysed in this work), one can maximise the expected utility of Cooperation when the utilities are negative. This is in accordance with the previous study of Moreira and Wichert (2016) in which the authors found that violations to the Sure Thing Principle imply destructive (or negative) quantum interference e ects. As we will see in the next section, the quantum parameters found that are used to maximise the expected utility of a cooperate action lead to destructive quantum interferences and can exactly explain the probability distributions observed in the experiments.
7.1Results and Discussion
Although there are several quantum parameters that satisfy the relationship that shows that participants can maximise the utility of a cooperate action, only a few parameters are able to accommodate both the paradoxical probability distributions reported in the several works in the literature and to maximise the expected utility of cooperating. The previous work of Moreira and Wichert (2016) shows how the quantum parameters are sensitive to accommodate the violations of the Sure Thing Principle in terms of the probability distributions. The slight variation of the quantum parameter θ in the quantum-like Bayesian network can lead to completely di erent probability distributions which di er from the ones observed in the di erence experimental scenarios reported in the literature. These probability distributions will influence the utilities computed by the expected utility framework.
In Table 2, it is presented the quantum parameters that lead to the quantum interference term that is necessary to fully explain and accommodate the violations to the Sure Thing Principle reported over several works of the literature.
For this reason, we decided to test if the quantum-like parameters used to accommodate the violations to the Sure Thing Principle were su cient and if they could also lead to a maximisation of expected utility of cooperation. We performed simulations of the di erent works in the literature and we concluded that the quantum interference e ects that can accommodate violations to the violations of the Sure Thing Principle in the quantum-like Bayesian network alone, also explain a higher preference of the cooperative action over defect. Table 3 presents the results.
In Table 3, we present the Maximum Expected Utility (MEU) computed for each work in the literature using the classical approach for the player’s di erent strategies: either remain silent (CL silent) or confess the crime (CL confess). The classical MEU shows that the optimal strategy is to conf ess and engage on a def ect strategy independently of what action the opponent chose. Of course
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quantum machine learning |
Introducing Quantum-Like Influence Diagrams for Violations |
105 |
Table 2. Experimental results reported for the Prisoner’s Dilemma game. The entries highlighted correspond to games that are not violating the Sure Thing Principle.
|
Prob of defect |
Prob of coop- |
Classical |
Experim prob |
Quantum interference |
|
(Known to defect) |
erate |
prob |
(Unknown |
θ param |
|
|
(Known to |
(Unknown |
condition) |
|
|
|
cooperate) |
condition) |
|
|
|
|
|
|
|
|
Shafir and |
0.9700 |
0.8400 |
0.9050 |
0.6300 |
2.8151 |
Tversky (1992) |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.7333 |
0.6670 |
0.7000 |
0.6000 |
3.0170 |
(2002) G1 |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.8000 |
0.7667 |
0.7833 |
0.6300 |
3.0758 |
(2002) G2 |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.9000 |
0.8667 |
0.8834 |
0.8667 |
2.8052 |
(2002) G3 |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.8333 |
0.8000 |
0.8167 |
0.7000 |
3.2313 |
(2002) G4 |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.8333 |
0.7333 |
0.7833 |
0.7000 |
2.8519 |
(2002) G5 |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.7667 |
0.8333 |
0.8000 |
0.8000 |
1.5708 |
(2002) G6 |
|
|
|
|
|
|
|
|
|
|
|
Li and Taplin |
0.8667 |
0.7333 |
0.8000 |
0.7667 |
3.7812 |
(2002) G7 |
|
|
|
|
|
|
|
|
|
|
|
these results go against the experimental works of the literature which say that a significant percentage of individuals, when under uncertainty, the engage more on cooperative strategies.
In opposition, when we use the quantum-like influence diagram, we take advantage of the quantum interference terms that will disturb the probabilistic outcomes of the quantum-like Bayesian networks. Since the utility function depends on the outcomes of the quantum-like Bayesian network, then it is straightforward that quantum interference e ects influence indirectly the outcomes of the MEU allowing us to favour a di erent strategy predicted by the classical MEU.
Table 3. Inferences in the quantum-like influence diagram for di erent works of the literature reporting violations of the Sure Thing Principle in the Prisoner’s Dilemma Game. One can see that the Quantum-Like Influence, presented in Eq. 20 (QL Infl) was changed to favour a Cooperate strategy using the quantum interference e ects of the Quantum-Like Bayesian Network. In the payo s, d corresponds to def ect and c to cooperate. The first payo corresponds to player 1 and the second to player 2.
|
|
|
|
|
|
|
|
Li and Taplin (2002) |
|
|
|
|
|
|
|||
|
Shafir and Tversky (1992) |
Game 1 |
Game 2 |
Game 3 |
Game 4 |
|
Game 5 |
Game 6 |
Game 7 |
||||||||
|
QL Infl |
QL Infl |
QL Infl QL Infl |
QL Infl QL Infl |
QL Infl QL Infl |
QL Infl QL Infl |
QL Infl QL Infl |
QL Infl QL Infl |
QL Infl QL Infl |
||||||||
|
(coop) |
(def) |
(coop) (def) |
(coop) (def) |
(coop) |
(def) |
(coop) |
(def) |
|
(coop) |
(def) |
MEU |
MEU |
(coop) |
(def) |
||
CL confess |
43.63 |
50.25 |
34.19 |
39.35 |
38.75 |
61.78 |
26.85 |
50.33 |
65.70 |
67.33 |
|
16.27 |
34.50 |
17.58 |
36.50 |
16.43 |
35.00 |
CL silent |
6.38 |
7.25 |
15.82 |
18.15 |
11.25 |
17.22 |
3.65 |
26.85 |
14.80 |
15.17 |
|
5.23 |
10.5 |
3.92 |
8.50 |
5.07 |
10.00 |
QL confess |
-1559.46 |
-2129.94 |
-1263.63 -1730.21 |
-1422.69 -4787.28 |
-702.24 -2075.58 |
-5198.14 -5462.41 |
|
-221.05 |
-1313.94 |
28.83 |
36.49 |
-184.75 |
-1116.33 |
||||
QL silent |
116.66 |
-160.08 |
-538.62 |
-735.89 |
-392.89 |
-1320.22 |
-94.44 |
-270.75 |
-1162.55 -1221.47 |
|
-61.44 |
-353.22 |
3.91 |
8.50 |
-44.86 |
-262.30 |
|
QL Interf |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
θ1 − θ2 |
2.815 |
2.815 |
3.017 |
3.017 |
3.0758 |
3.0758 |
2.805 |
2.805 |
3.23 |
3.23 |
|
2.8519 |
2.8519 |
1.5708 |
1.5708 |
3.78 |
3.78 |
Payo |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
dd dc |
30 |
25 |
30 |
25 |
73 |
25 |
30 |
25 |
80 |
78 |
|
43 |
10 |
30 |
10 |
30 |
10 |
cd cc |
85 |
75 |
85 |
75 |
85 |
75 |
85 |
36 |
85 |
83 |
|
85 |
46 |
60 |
33 |
60 |
33 |