Digital designs for money, markets, and social dilemmas

Preface
Contents
1 A Step Forward to the Future Society
	1.1 A View on the Current Situation Beyond the Older Economics
		1.1.1 The Harsh Reality and Its Dominance of the Fast Track Path to the Future
			1.1.1.1 Econocentrism Containing Monetary Exchange
		1.1.2 The Limits of Older Economics
			1.1.2.1 A Note on Quantum Financial System (QFS)
			1.1.2.2 How the Contemporary System Differs Much from the Image of Classical Economics
		1.1.3 Some Instances of Using Blockchain
			1.1.3.1 Currency Exchange
			1.1.3.2 A Simple Auction Design on the Ethereum Platform
			1.1.3.3 Turing Completeness and Smart Contract
	1.2 Some Fundamental Changes of the Style of Production and the Landscape of the Socio-Economic System
		1.2.1 The Short-Run Production Function and Its Aggregation Form
		1.2.2 An Alternative Production Set of Zonotope-Basis
		1.2.3 The Effect of Financial Industry on the Real Economy
		1.2.4 The Landscape of Exchange Systems
			1.2.4.1 The Hyper-Speed Domain: Relaxed Static Stability
			1.2.4.2 The Slow Speed Domain: Distributive System of P2P Exchange Network
			1.2.4.3 The Difference Between the Traditional Transaction and the P2P Transaction
			1.2.4.4 P2P Transactions Also Generate a Rapid Growth of Information
		1.2.5 Transductive/Symbolic Reasoning
			1.2.5.1 Transductive Learning
			1.2.5.2 Example of k-NN Classification
		1.2.6 Science and Ethics
	1.3 The Time Series in View of Horizontal Visibility Graph
		1.3.1 The Visibility Graph
			1.3.1.1 The Shortest Path
			1.3.1.2 Fully Random, Rule-Based Iterated Cellular Automata (FRICA)
		1.3.2 Exponential Distributions Reflecting Events Recurring ``at Random in Time\'\'
		1.3.3 Contriving a Simple Market to be Manipulated
			1.3.3.1 The Minimal Nakajima-Mori Agent Set in the U-Mart Futures Price Formation
		1.3.4 Characterizing the Even Matching in the Market Transaction
			1.3.4.1 Triangle Formation in the Market Transaction
			1.3.4.2 A Breaking Point on the Shortest Path
			1.3.4.3 The Shortest Path of Nikkei225
	1.4 The Construction of This Book Project
		1.4.1 The Composition of Chapters Briefly Looked
			1.4.1.1 The Evolution of Money and Thinking Complexities in the AI Era
			1.4.1.2 The Detailed Composition of Chapters
	References
Part I Evolution of Money and Thinking Complexities in the AI Era
	2 “Good Money Drives Out Bad” Among Diversifying e-Moneys: Cryptocurrency, Stablecoin, and Digital Community Currency
		2.1 Introduction
		2.2 From Cashless Economies to Evolution and Diversity of Modern Money
			2.2.1 Backward Cashless Economies in Japan
			2.2.2 Japanese Government\'s Policy to Promote Cashless Payment and Rapidly Diversifying QR Code Mobile Payment Systems
			2.2.3 The Rise of Utilization Rate of Cashless Payment Under the Spread of New Coronavirus and the Deregulations for Mobile Payroll System
			2.2.4 Evolution and Diversity of Modern Private Moneys: Cryptocurrencies and Digital Community Currencies With/Without Public/Private Blockchains
		2.3 Commonality Between Cryptocurrency and Fiat Legal Tender as Modern Money: Purely Informational “Ideational Money” or “Symbolic Money”
			2.3.1 Violent Price Movements of Bitcoin and Altcoins
			2.3.2 Amazing Growth in Bitcoin\'s Long-Term Value
			2.3.3 Facebook\'s Stablecoin Libra or Diem: Glocal Digital Community Currency Beyond National Borders
			2.3.4 Dematerialization of Money: “Dematerialization of Monetary Substance” and “Demonetization of Monetary Media”
			2.3.5 A Tree Diagram of Money with Four Stages: Primitive Money, Material and Credit Money, Cash and Deposit Money, and Various Non-national Moneys
			2.3.6 Plurality of Monetary Exchanges in History and the Evolution of Money Through Self-Organization, Replication, Variation, and Selection
			2.3.7 Reconsidering the Nature of Legal Tender as National Currency
			2.3.8 Bank of Japan Notes Are Not “Certificates of Obligation” but “Equity Securities”
		2.4 What Is Good Money? Hayek\'s Principle of Choice in Currency in Terms of “Quality” Realize That “Good Money Drives Out Bad”
			2.4.1 Gresham\'s Law
			2.4.2 Hayek\'s Denationalization of Money and the Principle of ``Choice in Currency\'\'
			2.4.3 The Precondition of Good Money: Ordinary People in an Actual Socioeconomy
			2.4.4 Can Japanese DCCs Such as Sarubobo Coin and Aqua Coin Become Good Money?
				2.4.4.1 Sarubobo Coin
				2.4.4.2 Aqua Coin
		2.5 Conclusion
		References
	3 Practical Case Study About US: Doreming—Establishing the Practice of the Measures to Create a New Paradigm of “Revenue Share Finance”
		3.1 A Short History of Doreming
		3.2 Help the Poor by Giving Them Real-Time Access to Their Wages Through “MySalary”
			3.2.1 Summary of the Current Situation
			3.2.2 Measures to Help the Poor
			3.2.3 Practice of the Measures to Help the Poor
		3.3 Accelerate to Go Cashless with “MySalary” to Create a Safe and Secure Society
			3.3.1 Summary of the Current Situation
			3.3.2 Measures to Accelerate to Go Cashless
			3.3.3 Practice of the Measures to Accelerate to Go Cashless
		3.4 Support the Acceleration of the Spread of Digital Money with “MySalary”
			3.4.1 Summary of the Current Situation
			3.4.2 Measures to Support the Acceleration of the Spread
			3.4.3 Practice of the Measures to Accelerate to Go Cashless
		3.5 Support the Government\'s Securing of Financial Resources Through Automatic Tax Collection or Automatic Borrowing with “Revenue Share Finance”
			3.5.1 Summary of the Current Situation
			3.5.2 Measures to Support the Government\'s Securing of Financial Resources
			3.5.3 Practice of the Measures to Support the Government\'s Securing of Financial Resources
		3.6 Provide a Society with Dreams and Hopes to the Poor Through “Revenue Share Finance”
			3.6.1 Summary of the Current Situation
			3.6.2 Measures to Provide a Society with Dreams and Hopes
			3.6.3 Practice of the Measures to Provide a Society with Dreams and Hopes
Part II Goods Market and the Future of Labor Market
	4 Model Structure of Agent-Based Artificial Economic System Responsible for Reproducing Fundamental Economic Behavior of Goods Market
		4.1 Introduction
		4.2 The Model
			4.2.1 Outline of the Model
			4.2.2 Sequence of Actions
			4.2.3 Outline of Agent\'s Decision-Making Rules
				4.2.3.1 Behavioral Rules of Consumers
				4.2.3.2 Behavioral Rules of Producers
				4.2.3.3 Behavioral Rules of the Bank
				4.2.3.4 Behavioral Rules of Government
		4.3 Simulation Conditions
			4.3.1 Simulation Conditions for Reproducing Price Equilibrium
			4.3.2 Simulation Conditions for Reproducing the Effect of Supply Chain
			4.3.3 Simulation Conditions for Reproducing Business Cycles
			4.3.4 Simulation Conditions for Reproducing the Effect of Income Tax Reduction
			4.3.5 Simulation Conditions for Reproducing the Effect of Corporate Tax Reduction
		4.4 Simulation Results
			4.4.1 The Necessary Model Structure for Reproducing Price Equilibrium
			4.4.2 Necessary Model Structure for Reproducing the Effect of Supply Chain
			4.4.3 The Necessary Model Structure for Reproducing Business Cycles
			4.4.4 The Necessary Model Structure for Reproducing the Positive Influence of an Income Tax Reduction on GDP
			4.4.5 Model Structure Necessary for Reproducing the Positive Influence of Corporate Tax Reduction on GDP
		4.5 Discussions
			4.5.1 The Validity of the Model in ABM
			4.5.2 The Causal Mechanism of Business Cycles
		4.6 Conclusion
		Appendix: Overview, Design Concepts, and Details Protocol
			Purpose
			Entities, State Variables, and Scales
			Process Overview and Scheduling
			Design Concepts
				Basic Principles
				Emergence
				Adaptation
				Objectives
				Prediction
				Sensing
				Interaction
				Stochasticity
				Observation
			Initialization
			Input Data
			Submodels
				Funds Circulation Submodel
				Price Equilibrium Submodel
				Investment Submodel
		References
	5 AI and the Future of the Labor Market: The Advent of a New Paradigm?
		5.1 Impact of Technology on the Economy and Labor
		5.2 Characteristics of the Twenty-First Century Economy
		5.3 Impact of AI on Labor
			5.3.1 Decrease in Employment
			5.3.2 AI and Employment Scenarios
		5.4 Market Changes Brought About by AI
			5.4.1 Thorough Marketization
			5.4.2 What AI-Substituted Workers Choose
			5.4.3 Contraction of the Monetary Economy
		5.5 Pathways to a New Society
			5.5.1 Ideal Conversion
			5.5.2 Role of Government
			5.5.3 Will There Be a Shift in Values?
		5.6 Conclusion
		References
Part III Computational Social Approaches to Social Dilemmas, Smart City, Cryptographics
	6 Mathematical Framework to Quantify Social Dilemmas
		6.1 Symmetric Two-Player and Two-Strategy Games
		6.2 Social Viscosity
		6.3 Social Dilemma and Its Mathematical Quantification
		6.4 Concept of a Social Efficiency Deficit
			6.4.1 Donor and Recipient Game
			6.4.2 PD with the Social Viscosity by Network Reciprocity
		6.5 Application of Social Efficiency Deficit
			6.5.1 Derivation of SED
		References
	7 Agent-Based Simulation for Service and Social Systems and Large-Scale Social Simulation Framework
		7.1 Introduction
		7.2 Market and Auction Simulation
		7.3 International Emissions Trading Simulation and Gaming
		7.4 Agent-Based Simulation Framework XASDI
		7.5 IBM Mega Traffic Simulator
		7.6 Pedestrian and Shopping Simulation
		7.7 Conclusion
		References
	8 Characterization of XRP Crypto-Asset Transactions from Networks Scientific Approach
		8.1 Introduction
		8.2 Crypto-Asset
			8.2.1 Blockchain Technology and the Crypto-Asset Bitcoin
			8.2.2 Using Blockchain Technology to Solve Global Issues
			8.2.3 Ripple\'s Crypto-Asset XRP
		8.3 Methods
			8.3.1 Centralities in Complex Network
			8.3.2 Signal Noise Separation
			8.3.3 Motif Analysis
		8.4 Results
			8.4.1 Basic Characteristics of Transaction Network
			8.4.2 Overall Features and Individual Movements
			8.4.3 Statistically Significant Triangular Motifs
		8.5 Conclusions
		References
Part IV Artificial Market Experiments
	9 The Emergence of Markets and Artificial Market Experiments
		9.1 Introduction
		9.2 The Emergence of the Markets
			9.2.1 Reason for Exchange
			9.2.2 Premise of Exchange
			9.2.3 Universality of Exchange
			9.2.4 Market as a Spontaneous Order
		9.3 Artificial Market
			9.3.1 Financial Markets and Financial Education
			9.3.2 Features of U-Mart System
			9.3.3 Open Experiment: Joint Seminar of Chuo University Aruga Seminar and Kindai University Taniguchi Seminar
			9.3.4 Learning of Experimental Participants
				9.3.4.1 Methods for Predicting Stock Price Fluctuations and Zero-Sum Games
				9.3.4.2 Example of Trading Strategy (1)
				9.3.4.3 Example of Trading Strategy (2)
		9.4 Conclusions
		References
	10 Trading Agents for Artificial Futures Markets
		10.1 Introduction
		10.2 Market and Price Formation
			10.2.1 Classical Understanding of Market
			10.2.2 Electric Power Market
			10.2.3 Financial Market and Uncertainty
			10.2.4 Diversity of Traders and Market
		10.3 Artificial Futures Market U-Mart
			10.3.1 Simulation of Market
			10.3.2 Artificial Futures Market U-Mart
			10.3.3 Organization of U-Mart
		10.4 Trading Strategies in Financial Markets
			10.4.1 Fundamental Analysis and Technical Analysis
			10.4.2 Market Follower and Contrarian
			10.4.3 Moving Average Method
			10.4.4 Arbitrage
			10.4.5 Risk Avoidance
		10.5 Trading Strategies in the U-Mart
			10.5.1 The Standard Agent Set
			10.5.2 Trading Strategies of the Standard Agent Set
		10.6 Agents and Behavior of Market
		10.7 Study of Thin Market and Market Makers
		10.8 Conclusion
		References
	11 Default Agent Set for Artificial Futures Market Simulation
		11.1 Introduction
			11.1.1 The “Origin” Problem
			11.1.2 What Is the U-Mart Project?
		11.2 U-Mart System
			11.2.1 Overview of the U-Mart System
			11.2.2 Setting, Logging, and Analyzing
		11.3 Three Activities of the U-Mart Project
			11.3.1 Overview of Activities
			11.3.2 Open Experiment (Competition)
		11.4 Experimental Environment
			11.4.1 Standard Agent Set
			11.4.2 Time Series
		11.5 Conclusion
		References
	12 Programmed Trading Agents and Market Microstructure in an Artificial Futures Market
		12.1 Introduction
		12.2 System Modeling at Tokyo Tech
			12.2.1 Overview
			12.2.2 Tutorial
			12.2.3 Trading Contest
		12.3 Results
			12.3.1 How Are Agents Created?
			12.3.2 Market Dynamics
			12.3.3 Market Microstructure
			12.3.4 Classification of the Submitted Trading Agents
			12.3.5 Discussion
		12.4 Concluding Remarks
		References
	13 Artificial Intelligence (AI) for Financial Markets: A Good AI for Designing Better Financial Markets and a Bad AI for Manipulating Markets
		13.1 Introduction
		13.2 Artificial Market Model: An Agent-Based Model for a Financial Market
			13.2.1 Importance and Difficulty of Market Design
			13.2.2 An Agent-Based Model Explaining a Complex System
			13.2.3 An Artificial Market Model = An Agent-Based Model for a Financial Market
			13.2.4 Suitable Complexity, Advantages, and Disadvantages
			13.2.5 Validation of Artificial Market Model
			13.2.6 Case Study: Tick Size Reduction
				13.2.6.1 Tick Size Reduction
				13.2.6.2 Model
				13.2.6.3 Simulation Results
				13.2.6.4 Summary
		13.3 A Trade-Execution AI Learning in an Artificial Market
		13.4 Bad AI Performing Market Manipulation
			13.4.1 Model
				13.4.1.1 Artificial Market Simulation and Normal Agent (NA)
				13.4.1.2 Genetic Algorithm
			13.4.2 Simulation Results
			13.4.3 Summary
		13.5 Conclusion
		References
Part V The Randomness and High Frequencies in the Financial Data
	14 Possible Relationship of the Randomness and the Stock Performance
		14.1 Introduction: Is There a Rule to Govern the Performance of Stock Prices?
		14.2 Randomness as a Label of a Bulk Data
		14.3 Methodology of the RMT Test
			14.3.1 Qualitative Version of the RMT Test
			14.3.2 Quantitative Version of the RMT Test
		14.4 Application to the Stock Performance
		14.5 Sudden Decrease of Randomness Predicts Future Decline of Stock Index
		14.6 Summary
		References
	15 Random Matrix Theory (RMT) Application on Financial Data
		15.1 Introduction
		15.2 Reviews on Important Previous Papers
			15.2.1 Random Matrix Theory (RMT)
			15.2.2 RMT Application on Financial Data
			15.2.3 RMT Application on Japanese Financial Data
		15.3 Numerical Experiments
			15.3.1 Marchenko–Pastur Distribution, Motzkin Number, and Narayana Number
			15.3.2 RMT Application on the Financial Data
		15.4 Conclusion
		References
	16 How Does the Entropy Function Explain the Distribution of High-Frequency Data?
		16.1 Introduction
		16.2 Modelling Transaction Prices
			16.2.1 Minimum Price Increments and Price Process
			16.2.2 The Sum of Squared Price Increments
			16.2.3 Product and Probability Measures
			16.2.4 The Law of Large Numbers and Exponential Decay of Probability
			16.2.5 The Cylinder Set
			16.2.6 Distribution Obtained by Fixing u
			16.2.7 Distribution Obtained by Fixing
		16.3 Testing the Large Deviation Property
			16.3.1 Data
			16.3.2 Basic Properties of the Data
			16.3.3 Bacsk-Test Process
		16.4 Results
			16.4.1 All Data
			16.4.2 Data Classified by
				16.4.2.1 ≤100
				16.4.2.2 100<≤350
				16.4.2.3 350<
			16.4.3 Interpretation of the Results
		References
Part VI Other Trading Strategy Issues and the Effects of AI Usage
	17 The Emergence of Periodic Properties of Ordering Strategies Under Disruption in the Beer Game
		17.1 Introduction
		17.2 Minimal Model of Supply Chain Network
			17.2.1 The Beer Game
			17.2.2 Bullwhip Effect
		17.3 Implementation of the Beer Game by Agent-Based Modeling
		17.4 Genetic Algorithm
			17.4.1 Fitness Function
			17.4.2 Coding and Crossover Operator
			17.4.3 Generation Alternation Model
		17.5 Computer Simulation
			17.5.1 Baseline Result
			17.5.2 Adaptation of Ordering Policy Corresponding to Disruption
				17.5.2.1 Disruption in Early Stage
				17.5.2.2 Disruption in the Middle Stage
			17.5.3 Trend of Agents\' Parameters
			17.5.4 Cases in Longer Supply Chain
		17.6 Conclusion
		References
	18 Network of Investment-Oriented Social Media
		18.1 Introduction
		18.2 Review of Related Literature
		18.3 Data
			18.3.1 Data Source
			18.3.2 Sample
			18.3.3 Fundamental Network Stats
				18.3.3.1 Number of Links and Degree of Centrality
				18.3.3.2 Scale-Free Feature of the Distribution
			18.3.4 Network Statistics
				18.3.4.1 Network Density, Average Cluster Coefficient, and Average Path Length
				18.3.4.2 PageRank
		18.4 Network Structure
			18.4.1 Network Graph
			18.4.2 Subnetwork
			18.4.3 Network Statistic of Subnetwork
		18.5 Relation Between Subnetwork and Return, Stock Selection
			18.5.1 Relation Between Return and Subnetwork Formation
			18.5.2 Relation Between Stock and Subnetwork Formation
			18.5.3 Analysis
		18.6 Conclusion
		References
	19 Student Learning in the Age of AI
		19.1 Introduction
		19.2 Economic Analysis of Education
		19.3 Economic Analysis of Students\' Learning Behavior
		19.4 A Survey of Thought-Saving Learners and Their Learning Outcomes
			19.4.1 Methods
			19.4.2 Results and Discussion
		19.5 Conclusions
		References
Index