This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products – help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.
Author(s): Parag Kulkarni
Series: Intelligent Systems Reference Library, 225
Publisher: Springer
Year: 2022
Language: English
Pages: 253
City: Singapore
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
