Choice Computing: Machine Learning and Systemic Economics for Choosing
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer
2022
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Intelligent Systems Reference Library
v.225 |
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (254 Seiten) |
| ISBN: | 9789811940590 |
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| 505 | 8 | |a Intro -- Foreword -- Preface: Embarking on the Journey of Choice... -- In Gratitude -- Praise for Choice Computing: Machine Learning and Systemic Economics for Choosing -- Contents -- About the Author -- 1 Introduction: Choosing-What is a Great Deal? -- 1.1 Choosing -- 1.2 Choosing and Learning -- 1.3 Organization of Book -- 1.4 Before Moving Ahead -- 1.5 Knowing What is It About -- References -- 2 Choice Modelling: Where Choosing Meets Computing -- 2.1 Introduction-Unfolding Choice Economics and Choice Computing -- 2.2 Mathematics of Choosing -- 2.3 Economic Impact of Choosing -- 2.4 Choice Paths -- 2.4.1 Three Pillars of Choosing -- 2.5 Rational Choices -- 2.6 Basics of Artificial Intelligence (AI) and Machine Learning (ML) -- 2.6.1 Traditional Algorithms to Reinforcement Learning -- 2.7 Bio-inspired Machine Learning -- 2.8 Choosing Inspired Machine Learning -- 2.9 Philosophy of Choosing -- 2.10 Context-Based ML -- 2.11 Choosing: Manifestation of Freedom, Youthfulness and Intelligence -- 2.11.1 When We Choose Versus When We Select -- 2.11.2 Voluntary Activities -- 2.11.3 Sapiens' Choice Making Resulting in Survival and Supremacy -- 2.11.4 Choosy Innovators -- 2.11.5 Choosing to Become Successful (Goal-Driven Systems) -- 2.12 Empowering Others to Choose -- 2.13 Cost Associated with Choosing -- 2.14 Choose, Let Others Choose and Empower Them to Choose -- 2.15 Choice Architects -- 2.16 Choice Models-Looking at Choosing as a Constraint Satisfaction Problem -- 2.17 Dynamic and Static Choice Models -- 2.18 Uncertainty and Choosing -- 2.18.1 Choice Experiments -- 2.18.2 Top of Mountain and Hazy Glass Theory -- 2.18.3 Seed-Based Exploration -- 2.19 Summary -- References -- 3 ML of Choosing: Architecting Intelligent Choice Framework -- 3.1 Who is a Choice Architect? -- 3.2 Stories Choice Architects -- 3.3 Those Who Help You to Choose | |
| 505 | 8 | |a 3.4 Architecting Choice Routes -- 3.5 Choice Flow -- 3.6 Choice Architecture to Revolutionize Thinking -- 3.7 Mastering Choice Architecting: Associating Algorithms -- 3.8 Creating Logical Choices for Customers: -- 3.9 Making Customers to Choose What You Would Love to Choose Them -- 3.10 Context-Based Choice Making and Scenario Analysis -- 3.11 Systemic Choice Architect -- 3.12 Summary -- References -- 4 Machine Learning of Choice Economics -- 4.1 ML of Choice Economics -- 4.2 Learning Based on the Impact of Choosing -- 4.3 Creating Experiential Bias or Availability Bias for Learning -- 4.4 Event Anchoring-Based Learning -- 4.4.1 Event Sequencing -- 4.5 First Movers ... -- 4.6 Creation of Legal Choices and Learning by Choice Elimination -- 4.7 Choice Impact-Based Learning -- 4.8 Learning to Set Target for Choosing -- 4.8.1 Leverage Point-Based Learning -- 4.9 Learning Based on Impact of Choosing -- 4.9.1 Rules for Choosing-Based ML -- 4.10 Choice Evolution -- 4.10.1 Evolutionary Choice Systems -- 4.10.2 Choice-Driven Crossover -- 4.10.3 Choice Association -- 4.10.4 Competitive Greedy Choosing -- 4.11 Choice Making in Uncertain Scenario -- 4.12 Core Choices and Supporting Choices (Decision About Learning Points) -- 4.13 Choice Projections-Connecting Peaks of Mountains (Multi-goal Architecture) -- 4.13.1 Choice Intelligence and Choice Processing -- 4.13.2 Societal Choice Computing -- 4.14 Conformation Choice Computing -- 4.14.1 Machine Learning Models -- 4.15 Summary -- References -- 5 Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- 5.1 Introduction -- 5.2 Co-operative Choosing (Choosing Together) -- 5.3 Choice Co-operation -- 5.3.1 Learning to Choose -- 5.4 Cognitive Choice Models -- 5.5 Competitive, Ranking and Hybrid Models in Co-operative Choosing -- 5.6 Utility Theory for Choosing | |
| 505 | 8 | |a 5.7 Co-operative Greedy Choice Traversal -- 5.8 Choice Models -- 5.8.1 Causal Cognition -- 5.8.2 Unifying Cognition -- 5.8.3 Binary Choice Instinct -- 5.8.4 Data, Average, Spread and Instinct: Decoding Mean, Median and Distribution of Choosing -- 5.8.5 Associative Choice Models -- 5.8.6 Entropy-Based Choice Models -- 5.8.7 Discrete Choice Models -- 5.8.8 Weighted Additive Choice Models -- 5.8.9 Inter Temporal Choice Models -- 5.8.10 Random Utility Choice Models -- 5.8.11 Hierarchical Choice Models -- 5.8.12 Co-operative Choice Models -- 5.8.13 Randomness in Choosing -- 5.9 Co-operative Choosing to Escape from Noise and Still Preserving Diversity -- 5.10 Summary -- References -- 6 Choice Architecture-Machine Learning Framework -- 6.1 Choice Architecture -- 6.2 Choice Catalyst Algorithm -- 6.3 Choice Architecture and Machine Learning -- 6.4 Identifying Chance Maximization Point -- 6.5 Option Eliminator-Learning to Eliminate -- 6.6 Embedding Emotions, Kansei Engineering (Emotional Computing for Choosing) -- 6.7 State Transitions to 'Choice-State' -- 6.8 Choice Learning Models -- 6.9 Behavioural System and Choosing -- 6.10 Choice Learning-Based Recommender System -- 6.11 Multi Choice Scenarios -- 6.12 Summary -- References -- 7 Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- 7.1 Introduction -- 7.2 Decoding Consciousness of Choosing -- 7.3 Artificial Conscious Choice Agent -- 7.4 Designing a Conscious Choice Agent (CCA) -- 7.5 Heuristic Choice Strategies -- 7.6 Exploratory Consciousness -- 7.7 Choice Architecting and Recommending Products or Services -- 7.8 Conscious Choice Architecting -- 7.9 Conscious Choice and Evolutionary Learning -- 7.10 Reinforcement and Deep Reinforcement Learning -- 7.11 Dealing with Local Maxima and Minima -- 7.12 Summary -- References -- 8 Choice Computing and Creativity | |
| 505 | 8 | |a 8.1 Introduction-Creative Contributions: Human Choosing and Machine Choosing -- 8.2 Human Creative Choosing Process -- 8.3 Concept Learning and Verbal Learning for Choosing -- 8.4 What is the Difference Between Choice and a Creative Choice? -- 8.5 Creative Choosing Machines -- 8.6 Creative Choice Agents -- 8.7 Discrimination Learning and Creative Choosing -- 8.8 Unconscious Blind Choosing to Unconscious Effective Choosing -- 8.9 Creative Choosing Models -- 8.10 Concept Maps for Choosing -- 8.11 Human Learning Inspired Creative Machine-Choosing Models -- 8.11.1 Reinforcement Choice Models -- 8.12 Creative Choice Learning Models -- 8.13 Creativity Moments and Creativity Points -- 8.14 Creative Agents and Creative Collaborative Intelligence -- 8.15 Summary -- References -- 9 Experimental Choice Computing and Choice Learning Through Real-Life Stories -- 9.1 Summary -- 9.2 In Education -- 9.3 Health Care -- 9.4 Social Good -- 9.5 Finance -- 9.6 Miscellaneous -- 9.7 Other Applications Can Be Thought of -- 9.8 Summary -- References -- 10 Choice Computing and Beyond -- Index | |
| 650 | 4 | |a Computer vision | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Kulkarni, Parag |t Choice Computing: Machine Learning and Systemic Economics for Choosing |d Singapore : Springer,c2022 |z 9789811940583 |
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| contents | Intro -- Foreword -- Preface: Embarking on the Journey of Choice... -- In Gratitude -- Praise for Choice Computing: Machine Learning and Systemic Economics for Choosing -- Contents -- About the Author -- 1 Introduction: Choosing-What is a Great Deal? -- 1.1 Choosing -- 1.2 Choosing and Learning -- 1.3 Organization of Book -- 1.4 Before Moving Ahead -- 1.5 Knowing What is It About -- References -- 2 Choice Modelling: Where Choosing Meets Computing -- 2.1 Introduction-Unfolding Choice Economics and Choice Computing -- 2.2 Mathematics of Choosing -- 2.3 Economic Impact of Choosing -- 2.4 Choice Paths -- 2.4.1 Three Pillars of Choosing -- 2.5 Rational Choices -- 2.6 Basics of Artificial Intelligence (AI) and Machine Learning (ML) -- 2.6.1 Traditional Algorithms to Reinforcement Learning -- 2.7 Bio-inspired Machine Learning -- 2.8 Choosing Inspired Machine Learning -- 2.9 Philosophy of Choosing -- 2.10 Context-Based ML -- 2.11 Choosing: Manifestation of Freedom, Youthfulness and Intelligence -- 2.11.1 When We Choose Versus When We Select -- 2.11.2 Voluntary Activities -- 2.11.3 Sapiens' Choice Making Resulting in Survival and Supremacy -- 2.11.4 Choosy Innovators -- 2.11.5 Choosing to Become Successful (Goal-Driven Systems) -- 2.12 Empowering Others to Choose -- 2.13 Cost Associated with Choosing -- 2.14 Choose, Let Others Choose and Empower Them to Choose -- 2.15 Choice Architects -- 2.16 Choice Models-Looking at Choosing as a Constraint Satisfaction Problem -- 2.17 Dynamic and Static Choice Models -- 2.18 Uncertainty and Choosing -- 2.18.1 Choice Experiments -- 2.18.2 Top of Mountain and Hazy Glass Theory -- 2.18.3 Seed-Based Exploration -- 2.19 Summary -- References -- 3 ML of Choosing: Architecting Intelligent Choice Framework -- 3.1 Who is a Choice Architect? -- 3.2 Stories Choice Architects -- 3.3 Those Who Help You to Choose 3.4 Architecting Choice Routes -- 3.5 Choice Flow -- 3.6 Choice Architecture to Revolutionize Thinking -- 3.7 Mastering Choice Architecting: Associating Algorithms -- 3.8 Creating Logical Choices for Customers: -- 3.9 Making Customers to Choose What You Would Love to Choose Them -- 3.10 Context-Based Choice Making and Scenario Analysis -- 3.11 Systemic Choice Architect -- 3.12 Summary -- References -- 4 Machine Learning of Choice Economics -- 4.1 ML of Choice Economics -- 4.2 Learning Based on the Impact of Choosing -- 4.3 Creating Experiential Bias or Availability Bias for Learning -- 4.4 Event Anchoring-Based Learning -- 4.4.1 Event Sequencing -- 4.5 First Movers ... -- 4.6 Creation of Legal Choices and Learning by Choice Elimination -- 4.7 Choice Impact-Based Learning -- 4.8 Learning to Set Target for Choosing -- 4.8.1 Leverage Point-Based Learning -- 4.9 Learning Based on Impact of Choosing -- 4.9.1 Rules for Choosing-Based ML -- 4.10 Choice Evolution -- 4.10.1 Evolutionary Choice Systems -- 4.10.2 Choice-Driven Crossover -- 4.10.3 Choice Association -- 4.10.4 Competitive Greedy Choosing -- 4.11 Choice Making in Uncertain Scenario -- 4.12 Core Choices and Supporting Choices (Decision About Learning Points) -- 4.13 Choice Projections-Connecting Peaks of Mountains (Multi-goal Architecture) -- 4.13.1 Choice Intelligence and Choice Processing -- 4.13.2 Societal Choice Computing -- 4.14 Conformation Choice Computing -- 4.14.1 Machine Learning Models -- 4.15 Summary -- References -- 5 Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- 5.1 Introduction -- 5.2 Co-operative Choosing (Choosing Together) -- 5.3 Choice Co-operation -- 5.3.1 Learning to Choose -- 5.4 Cognitive Choice Models -- 5.5 Competitive, Ranking and Hybrid Models in Co-operative Choosing -- 5.6 Utility Theory for Choosing 5.7 Co-operative Greedy Choice Traversal -- 5.8 Choice Models -- 5.8.1 Causal Cognition -- 5.8.2 Unifying Cognition -- 5.8.3 Binary Choice Instinct -- 5.8.4 Data, Average, Spread and Instinct: Decoding Mean, Median and Distribution of Choosing -- 5.8.5 Associative Choice Models -- 5.8.6 Entropy-Based Choice Models -- 5.8.7 Discrete Choice Models -- 5.8.8 Weighted Additive Choice Models -- 5.8.9 Inter Temporal Choice Models -- 5.8.10 Random Utility Choice Models -- 5.8.11 Hierarchical Choice Models -- 5.8.12 Co-operative Choice Models -- 5.8.13 Randomness in Choosing -- 5.9 Co-operative Choosing to Escape from Noise and Still Preserving Diversity -- 5.10 Summary -- References -- 6 Choice Architecture-Machine Learning Framework -- 6.1 Choice Architecture -- 6.2 Choice Catalyst Algorithm -- 6.3 Choice Architecture and Machine Learning -- 6.4 Identifying Chance Maximization Point -- 6.5 Option Eliminator-Learning to Eliminate -- 6.6 Embedding Emotions, Kansei Engineering (Emotional Computing for Choosing) -- 6.7 State Transitions to 'Choice-State' -- 6.8 Choice Learning Models -- 6.9 Behavioural System and Choosing -- 6.10 Choice Learning-Based Recommender System -- 6.11 Multi Choice Scenarios -- 6.12 Summary -- References -- 7 Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- 7.1 Introduction -- 7.2 Decoding Consciousness of Choosing -- 7.3 Artificial Conscious Choice Agent -- 7.4 Designing a Conscious Choice Agent (CCA) -- 7.5 Heuristic Choice Strategies -- 7.6 Exploratory Consciousness -- 7.7 Choice Architecting and Recommending Products or Services -- 7.8 Conscious Choice Architecting -- 7.9 Conscious Choice and Evolutionary Learning -- 7.10 Reinforcement and Deep Reinforcement Learning -- 7.11 Dealing with Local Maxima and Minima -- 7.12 Summary -- References -- 8 Choice Computing and Creativity 8.1 Introduction-Creative Contributions: Human Choosing and Machine Choosing -- 8.2 Human Creative Choosing Process -- 8.3 Concept Learning and Verbal Learning for Choosing -- 8.4 What is the Difference Between Choice and a Creative Choice? -- 8.5 Creative Choosing Machines -- 8.6 Creative Choice Agents -- 8.7 Discrimination Learning and Creative Choosing -- 8.8 Unconscious Blind Choosing to Unconscious Effective Choosing -- 8.9 Creative Choosing Models -- 8.10 Concept Maps for Choosing -- 8.11 Human Learning Inspired Creative Machine-Choosing Models -- 8.11.1 Reinforcement Choice Models -- 8.12 Creative Choice Learning Models -- 8.13 Creativity Moments and Creativity Points -- 8.14 Creative Agents and Creative Collaborative Intelligence -- 8.15 Summary -- References -- 9 Experimental Choice Computing and Choice Learning Through Real-Life Stories -- 9.1 Summary -- 9.2 In Education -- 9.3 Health Care -- 9.4 Social Good -- 9.5 Finance -- 9.6 Miscellaneous -- 9.7 Other Applications Can Be Thought of -- 9.8 Summary -- References -- 10 Choice Computing and Beyond -- Index |
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-- 3.2 Stories Choice Architects -- 3.3 Those Who Help You to Choose</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.4 Architecting Choice Routes -- 3.5 Choice Flow -- 3.6 Choice Architecture to Revolutionize Thinking -- 3.7 Mastering Choice Architecting: Associating Algorithms -- 3.8 Creating Logical Choices for Customers: -- 3.9 Making Customers to Choose What You Would Love to Choose Them -- 3.10 Context-Based Choice Making and Scenario Analysis -- 3.11 Systemic Choice Architect -- 3.12 Summary -- References -- 4 Machine Learning of Choice Economics -- 4.1 ML of Choice Economics -- 4.2 Learning Based on the Impact of Choosing -- 4.3 Creating Experiential Bias or Availability Bias for Learning -- 4.4 Event Anchoring-Based Learning -- 4.4.1 Event Sequencing -- 4.5 First Movers ... -- 4.6 Creation of Legal Choices and Learning by Choice Elimination -- 4.7 Choice Impact-Based Learning -- 4.8 Learning to Set Target for Choosing -- 4.8.1 Leverage Point-Based Learning -- 4.9 Learning Based on Impact of Choosing -- 4.9.1 Rules for Choosing-Based ML -- 4.10 Choice Evolution -- 4.10.1 Evolutionary Choice Systems -- 4.10.2 Choice-Driven Crossover -- 4.10.3 Choice Association -- 4.10.4 Competitive Greedy Choosing -- 4.11 Choice Making in Uncertain Scenario -- 4.12 Core Choices and Supporting Choices (Decision About Learning Points) -- 4.13 Choice Projections-Connecting Peaks of Mountains (Multi-goal Architecture) -- 4.13.1 Choice Intelligence and Choice Processing -- 4.13.2 Societal Choice Computing -- 4.14 Conformation Choice Computing -- 4.14.1 Machine Learning Models -- 4.15 Summary -- References -- 5 Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- 5.1 Introduction -- 5.2 Co-operative Choosing (Choosing Together) -- 5.3 Choice Co-operation -- 5.3.1 Learning to Choose -- 5.4 Cognitive Choice Models -- 5.5 Competitive, Ranking and Hybrid Models in Co-operative Choosing -- 5.6 Utility Theory for Choosing</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.7 Co-operative Greedy Choice Traversal -- 5.8 Choice Models -- 5.8.1 Causal Cognition -- 5.8.2 Unifying Cognition -- 5.8.3 Binary Choice Instinct -- 5.8.4 Data, Average, Spread and Instinct: Decoding Mean, Median and Distribution of Choosing -- 5.8.5 Associative Choice Models -- 5.8.6 Entropy-Based Choice Models -- 5.8.7 Discrete Choice Models -- 5.8.8 Weighted Additive Choice Models -- 5.8.9 Inter Temporal Choice Models -- 5.8.10 Random Utility Choice Models -- 5.8.11 Hierarchical Choice Models -- 5.8.12 Co-operative Choice Models -- 5.8.13 Randomness in Choosing -- 5.9 Co-operative Choosing to Escape from Noise and Still Preserving Diversity -- 5.10 Summary -- References -- 6 Choice Architecture-Machine Learning Framework -- 6.1 Choice Architecture -- 6.2 Choice Catalyst Algorithm -- 6.3 Choice Architecture and Machine Learning -- 6.4 Identifying Chance Maximization Point -- 6.5 Option Eliminator-Learning to Eliminate -- 6.6 Embedding Emotions, Kansei Engineering (Emotional Computing for Choosing) -- 6.7 State Transitions to 'Choice-State' -- 6.8 Choice Learning Models -- 6.9 Behavioural System and Choosing -- 6.10 Choice Learning-Based Recommender System -- 6.11 Multi Choice Scenarios -- 6.12 Summary -- References -- 7 Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- 7.1 Introduction -- 7.2 Decoding Consciousness of Choosing -- 7.3 Artificial Conscious Choice Agent -- 7.4 Designing a Conscious Choice Agent (CCA) -- 7.5 Heuristic Choice Strategies -- 7.6 Exploratory Consciousness -- 7.7 Choice Architecting and Recommending Products or Services -- 7.8 Conscious Choice Architecting -- 7.9 Conscious Choice and Evolutionary Learning -- 7.10 Reinforcement and Deep Reinforcement Learning -- 7.11 Dealing with Local Maxima and Minima -- 7.12 Summary -- References -- 8 Choice Computing and Creativity</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.1 Introduction-Creative Contributions: Human Choosing and Machine Choosing -- 8.2 Human Creative Choosing Process -- 8.3 Concept Learning and Verbal Learning for Choosing -- 8.4 What is the Difference Between Choice and a Creative Choice? 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| id | DE-604.BV049408539 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:05:37Z |
| indexdate | 2024-07-10T10:06:16Z |
| institution | BVB |
| isbn | 9789811940590 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034735623 |
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| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (254 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| record_format | marc |
| series2 | Intelligent Systems Reference Library |
| spelling | Kulkarni, Parag Verfasser aut Choice Computing Machine Learning and Systemic Economics for Choosing 1st ed Singapore Springer 2022 ©2022 1 Online-Ressource (254 Seiten) txt rdacontent c rdamedia cr rdacarrier Intelligent Systems Reference Library v.225 Description based on publisher supplied metadata and other sources Intro -- Foreword -- Preface: Embarking on the Journey of Choice... -- In Gratitude -- Praise for Choice Computing: Machine Learning and Systemic Economics for Choosing -- Contents -- About the Author -- 1 Introduction: Choosing-What is a Great Deal? -- 1.1 Choosing -- 1.2 Choosing and Learning -- 1.3 Organization of Book -- 1.4 Before Moving Ahead -- 1.5 Knowing What is It About -- References -- 2 Choice Modelling: Where Choosing Meets Computing -- 2.1 Introduction-Unfolding Choice Economics and Choice Computing -- 2.2 Mathematics of Choosing -- 2.3 Economic Impact of Choosing -- 2.4 Choice Paths -- 2.4.1 Three Pillars of Choosing -- 2.5 Rational Choices -- 2.6 Basics of Artificial Intelligence (AI) and Machine Learning (ML) -- 2.6.1 Traditional Algorithms to Reinforcement Learning -- 2.7 Bio-inspired Machine Learning -- 2.8 Choosing Inspired Machine Learning -- 2.9 Philosophy of Choosing -- 2.10 Context-Based ML -- 2.11 Choosing: Manifestation of Freedom, Youthfulness and Intelligence -- 2.11.1 When We Choose Versus When We Select -- 2.11.2 Voluntary Activities -- 2.11.3 Sapiens' Choice Making Resulting in Survival and Supremacy -- 2.11.4 Choosy Innovators -- 2.11.5 Choosing to Become Successful (Goal-Driven Systems) -- 2.12 Empowering Others to Choose -- 2.13 Cost Associated with Choosing -- 2.14 Choose, Let Others Choose and Empower Them to Choose -- 2.15 Choice Architects -- 2.16 Choice Models-Looking at Choosing as a Constraint Satisfaction Problem -- 2.17 Dynamic and Static Choice Models -- 2.18 Uncertainty and Choosing -- 2.18.1 Choice Experiments -- 2.18.2 Top of Mountain and Hazy Glass Theory -- 2.18.3 Seed-Based Exploration -- 2.19 Summary -- References -- 3 ML of Choosing: Architecting Intelligent Choice Framework -- 3.1 Who is a Choice Architect? -- 3.2 Stories Choice Architects -- 3.3 Those Who Help You to Choose 3.4 Architecting Choice Routes -- 3.5 Choice Flow -- 3.6 Choice Architecture to Revolutionize Thinking -- 3.7 Mastering Choice Architecting: Associating Algorithms -- 3.8 Creating Logical Choices for Customers: -- 3.9 Making Customers to Choose What You Would Love to Choose Them -- 3.10 Context-Based Choice Making and Scenario Analysis -- 3.11 Systemic Choice Architect -- 3.12 Summary -- References -- 4 Machine Learning of Choice Economics -- 4.1 ML of Choice Economics -- 4.2 Learning Based on the Impact of Choosing -- 4.3 Creating Experiential Bias or Availability Bias for Learning -- 4.4 Event Anchoring-Based Learning -- 4.4.1 Event Sequencing -- 4.5 First Movers ... -- 4.6 Creation of Legal Choices and Learning by Choice Elimination -- 4.7 Choice Impact-Based Learning -- 4.8 Learning to Set Target for Choosing -- 4.8.1 Leverage Point-Based Learning -- 4.9 Learning Based on Impact of Choosing -- 4.9.1 Rules for Choosing-Based ML -- 4.10 Choice Evolution -- 4.10.1 Evolutionary Choice Systems -- 4.10.2 Choice-Driven Crossover -- 4.10.3 Choice Association -- 4.10.4 Competitive Greedy Choosing -- 4.11 Choice Making in Uncertain Scenario -- 4.12 Core Choices and Supporting Choices (Decision About Learning Points) -- 4.13 Choice Projections-Connecting Peaks of Mountains (Multi-goal Architecture) -- 4.13.1 Choice Intelligence and Choice Processing -- 4.13.2 Societal Choice Computing -- 4.14 Conformation Choice Computing -- 4.14.1 Machine Learning Models -- 4.15 Summary -- References -- 5 Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- 5.1 Introduction -- 5.2 Co-operative Choosing (Choosing Together) -- 5.3 Choice Co-operation -- 5.3.1 Learning to Choose -- 5.4 Cognitive Choice Models -- 5.5 Competitive, Ranking and Hybrid Models in Co-operative Choosing -- 5.6 Utility Theory for Choosing 5.7 Co-operative Greedy Choice Traversal -- 5.8 Choice Models -- 5.8.1 Causal Cognition -- 5.8.2 Unifying Cognition -- 5.8.3 Binary Choice Instinct -- 5.8.4 Data, Average, Spread and Instinct: Decoding Mean, Median and Distribution of Choosing -- 5.8.5 Associative Choice Models -- 5.8.6 Entropy-Based Choice Models -- 5.8.7 Discrete Choice Models -- 5.8.8 Weighted Additive Choice Models -- 5.8.9 Inter Temporal Choice Models -- 5.8.10 Random Utility Choice Models -- 5.8.11 Hierarchical Choice Models -- 5.8.12 Co-operative Choice Models -- 5.8.13 Randomness in Choosing -- 5.9 Co-operative Choosing to Escape from Noise and Still Preserving Diversity -- 5.10 Summary -- References -- 6 Choice Architecture-Machine Learning Framework -- 6.1 Choice Architecture -- 6.2 Choice Catalyst Algorithm -- 6.3 Choice Architecture and Machine Learning -- 6.4 Identifying Chance Maximization Point -- 6.5 Option Eliminator-Learning to Eliminate -- 6.6 Embedding Emotions, Kansei Engineering (Emotional Computing for Choosing) -- 6.7 State Transitions to 'Choice-State' -- 6.8 Choice Learning Models -- 6.9 Behavioural System and Choosing -- 6.10 Choice Learning-Based Recommender System -- 6.11 Multi Choice Scenarios -- 6.12 Summary -- References -- 7 Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- 7.1 Introduction -- 7.2 Decoding Consciousness of Choosing -- 7.3 Artificial Conscious Choice Agent -- 7.4 Designing a Conscious Choice Agent (CCA) -- 7.5 Heuristic Choice Strategies -- 7.6 Exploratory Consciousness -- 7.7 Choice Architecting and Recommending Products or Services -- 7.8 Conscious Choice Architecting -- 7.9 Conscious Choice and Evolutionary Learning -- 7.10 Reinforcement and Deep Reinforcement Learning -- 7.11 Dealing with Local Maxima and Minima -- 7.12 Summary -- References -- 8 Choice Computing and Creativity 8.1 Introduction-Creative Contributions: Human Choosing and Machine Choosing -- 8.2 Human Creative Choosing Process -- 8.3 Concept Learning and Verbal Learning for Choosing -- 8.4 What is the Difference Between Choice and a Creative Choice? -- 8.5 Creative Choosing Machines -- 8.6 Creative Choice Agents -- 8.7 Discrimination Learning and Creative Choosing -- 8.8 Unconscious Blind Choosing to Unconscious Effective Choosing -- 8.9 Creative Choosing Models -- 8.10 Concept Maps for Choosing -- 8.11 Human Learning Inspired Creative Machine-Choosing Models -- 8.11.1 Reinforcement Choice Models -- 8.12 Creative Choice Learning Models -- 8.13 Creativity Moments and Creativity Points -- 8.14 Creative Agents and Creative Collaborative Intelligence -- 8.15 Summary -- References -- 9 Experimental Choice Computing and Choice Learning Through Real-Life Stories -- 9.1 Summary -- 9.2 In Education -- 9.3 Health Care -- 9.4 Social Good -- 9.5 Finance -- 9.6 Miscellaneous -- 9.7 Other Applications Can Be Thought of -- 9.8 Summary -- References -- 10 Choice Computing and Beyond -- Index Computer vision Erscheint auch als Druck-Ausgabe Kulkarni, Parag Choice Computing: Machine Learning and Systemic Economics for Choosing Singapore : Springer,c2022 9789811940583 |
| spellingShingle | Kulkarni, Parag Choice Computing Machine Learning and Systemic Economics for Choosing Intro -- Foreword -- Preface: Embarking on the Journey of Choice... -- In Gratitude -- Praise for Choice Computing: Machine Learning and Systemic Economics for Choosing -- Contents -- About the Author -- 1 Introduction: Choosing-What is a Great Deal? -- 1.1 Choosing -- 1.2 Choosing and Learning -- 1.3 Organization of Book -- 1.4 Before Moving Ahead -- 1.5 Knowing What is It About -- References -- 2 Choice Modelling: Where Choosing Meets Computing -- 2.1 Introduction-Unfolding Choice Economics and Choice Computing -- 2.2 Mathematics of Choosing -- 2.3 Economic Impact of Choosing -- 2.4 Choice Paths -- 2.4.1 Three Pillars of Choosing -- 2.5 Rational Choices -- 2.6 Basics of Artificial Intelligence (AI) and Machine Learning (ML) -- 2.6.1 Traditional Algorithms to Reinforcement Learning -- 2.7 Bio-inspired Machine Learning -- 2.8 Choosing Inspired Machine Learning -- 2.9 Philosophy of Choosing -- 2.10 Context-Based ML -- 2.11 Choosing: Manifestation of Freedom, Youthfulness and Intelligence -- 2.11.1 When We Choose Versus When We Select -- 2.11.2 Voluntary Activities -- 2.11.3 Sapiens' Choice Making Resulting in Survival and Supremacy -- 2.11.4 Choosy Innovators -- 2.11.5 Choosing to Become Successful (Goal-Driven Systems) -- 2.12 Empowering Others to Choose -- 2.13 Cost Associated with Choosing -- 2.14 Choose, Let Others Choose and Empower Them to Choose -- 2.15 Choice Architects -- 2.16 Choice Models-Looking at Choosing as a Constraint Satisfaction Problem -- 2.17 Dynamic and Static Choice Models -- 2.18 Uncertainty and Choosing -- 2.18.1 Choice Experiments -- 2.18.2 Top of Mountain and Hazy Glass Theory -- 2.18.3 Seed-Based Exploration -- 2.19 Summary -- References -- 3 ML of Choosing: Architecting Intelligent Choice Framework -- 3.1 Who is a Choice Architect? -- 3.2 Stories Choice Architects -- 3.3 Those Who Help You to Choose 3.4 Architecting Choice Routes -- 3.5 Choice Flow -- 3.6 Choice Architecture to Revolutionize Thinking -- 3.7 Mastering Choice Architecting: Associating Algorithms -- 3.8 Creating Logical Choices for Customers: -- 3.9 Making Customers to Choose What You Would Love to Choose Them -- 3.10 Context-Based Choice Making and Scenario Analysis -- 3.11 Systemic Choice Architect -- 3.12 Summary -- References -- 4 Machine Learning of Choice Economics -- 4.1 ML of Choice Economics -- 4.2 Learning Based on the Impact of Choosing -- 4.3 Creating Experiential Bias or Availability Bias for Learning -- 4.4 Event Anchoring-Based Learning -- 4.4.1 Event Sequencing -- 4.5 First Movers ... -- 4.6 Creation of Legal Choices and Learning by Choice Elimination -- 4.7 Choice Impact-Based Learning -- 4.8 Learning to Set Target for Choosing -- 4.8.1 Leverage Point-Based Learning -- 4.9 Learning Based on Impact of Choosing -- 4.9.1 Rules for Choosing-Based ML -- 4.10 Choice Evolution -- 4.10.1 Evolutionary Choice Systems -- 4.10.2 Choice-Driven Crossover -- 4.10.3 Choice Association -- 4.10.4 Competitive Greedy Choosing -- 4.11 Choice Making in Uncertain Scenario -- 4.12 Core Choices and Supporting Choices (Decision About Learning Points) -- 4.13 Choice Projections-Connecting Peaks of Mountains (Multi-goal Architecture) -- 4.13.1 Choice Intelligence and Choice Processing -- 4.13.2 Societal Choice Computing -- 4.14 Conformation Choice Computing -- 4.14.1 Machine Learning Models -- 4.15 Summary -- References -- 5 Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- 5.1 Introduction -- 5.2 Co-operative Choosing (Choosing Together) -- 5.3 Choice Co-operation -- 5.3.1 Learning to Choose -- 5.4 Cognitive Choice Models -- 5.5 Competitive, Ranking and Hybrid Models in Co-operative Choosing -- 5.6 Utility Theory for Choosing 5.7 Co-operative Greedy Choice Traversal -- 5.8 Choice Models -- 5.8.1 Causal Cognition -- 5.8.2 Unifying Cognition -- 5.8.3 Binary Choice Instinct -- 5.8.4 Data, Average, Spread and Instinct: Decoding Mean, Median and Distribution of Choosing -- 5.8.5 Associative Choice Models -- 5.8.6 Entropy-Based Choice Models -- 5.8.7 Discrete Choice Models -- 5.8.8 Weighted Additive Choice Models -- 5.8.9 Inter Temporal Choice Models -- 5.8.10 Random Utility Choice Models -- 5.8.11 Hierarchical Choice Models -- 5.8.12 Co-operative Choice Models -- 5.8.13 Randomness in Choosing -- 5.9 Co-operative Choosing to Escape from Noise and Still Preserving Diversity -- 5.10 Summary -- References -- 6 Choice Architecture-Machine Learning Framework -- 6.1 Choice Architecture -- 6.2 Choice Catalyst Algorithm -- 6.3 Choice Architecture and Machine Learning -- 6.4 Identifying Chance Maximization Point -- 6.5 Option Eliminator-Learning to Eliminate -- 6.6 Embedding Emotions, Kansei Engineering (Emotional Computing for Choosing) -- 6.7 State Transitions to 'Choice-State' -- 6.8 Choice Learning Models -- 6.9 Behavioural System and Choosing -- 6.10 Choice Learning-Based Recommender System -- 6.11 Multi Choice Scenarios -- 6.12 Summary -- References -- 7 Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- 7.1 Introduction -- 7.2 Decoding Consciousness of Choosing -- 7.3 Artificial Conscious Choice Agent -- 7.4 Designing a Conscious Choice Agent (CCA) -- 7.5 Heuristic Choice Strategies -- 7.6 Exploratory Consciousness -- 7.7 Choice Architecting and Recommending Products or Services -- 7.8 Conscious Choice Architecting -- 7.9 Conscious Choice and Evolutionary Learning -- 7.10 Reinforcement and Deep Reinforcement Learning -- 7.11 Dealing with Local Maxima and Minima -- 7.12 Summary -- References -- 8 Choice Computing and Creativity 8.1 Introduction-Creative Contributions: Human Choosing and Machine Choosing -- 8.2 Human Creative Choosing Process -- 8.3 Concept Learning and Verbal Learning for Choosing -- 8.4 What is the Difference Between Choice and a Creative Choice? -- 8.5 Creative Choosing Machines -- 8.6 Creative Choice Agents -- 8.7 Discrimination Learning and Creative Choosing -- 8.8 Unconscious Blind Choosing to Unconscious Effective Choosing -- 8.9 Creative Choosing Models -- 8.10 Concept Maps for Choosing -- 8.11 Human Learning Inspired Creative Machine-Choosing Models -- 8.11.1 Reinforcement Choice Models -- 8.12 Creative Choice Learning Models -- 8.13 Creativity Moments and Creativity Points -- 8.14 Creative Agents and Creative Collaborative Intelligence -- 8.15 Summary -- References -- 9 Experimental Choice Computing and Choice Learning Through Real-Life Stories -- 9.1 Summary -- 9.2 In Education -- 9.3 Health Care -- 9.4 Social Good -- 9.5 Finance -- 9.6 Miscellaneous -- 9.7 Other Applications Can Be Thought of -- 9.8 Summary -- References -- 10 Choice Computing and Beyond -- Index Computer vision |
| title | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_auth | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_exact_search | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_exact_search_txtP | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_full | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_fullStr | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_full_unstemmed | Choice Computing Machine Learning and Systemic Economics for Choosing |
| title_short | Choice Computing |
| title_sort | choice computing machine learning and systemic economics for choosing |
| title_sub | Machine Learning and Systemic Economics for Choosing |
| topic | Computer vision |
| topic_facet | Computer vision |
| work_keys_str_mv | AT kulkarniparag choicecomputingmachinelearningandsystemiceconomicsforchoosing |