Applications of Data-Centric Science to Social Design

The intention behind this book is to illustrate the deep relation among human behavior, data-centric science, and social design. In fact, these three issues have been independently developing in different fields, although they are, of course, deeply interrelated to one another. Specifically, fundamental understanding of human behavior should be employed for investigating our human society and designing social systems. Insights and both quantitative and qualitative understandings of collective human behavior are quite useful when social systems are designed. Fundamental principles of human behavior, theoretical models of human behavior, and information cascades are addressed as aspects of human behavior. Data-driven investigation of human nature, social behavior, and societal systems are developed as aspects of data-centric science. As design aspects, how to design social systems from heterogeneous memberships is explained. There is also discussion of these three aspectshuman behavior, data-centric science, and social designindependently and with regard to the relationships among them.

Enables learning of fundamental theory of design processes and how to create value from experiences Discusses how to conduct data-driven decision making in design processes from the Deming cycle (PDCA cycle) of data-centric science Explains the theoretical contribution of collective human behavior to deepening qualitative and quantitative understanding of human interaction

Autorentext
Aki-Hiro Sato, Yokohama City University

Inhalt
Part A: Methods for Data Analysis and Design.- Chapter 1: How we design our society from data-centric point of view.- Chapter 2: Practical methods of data analysis.- Chapter 3: How to design data products with heterogeneous stakeholders.- Chapter 4: Designing Human-Machine Systems Focusing on Benefits of Inconvenience.- Part B: Mathematical Foundation of Collective Human Behavior.- Chapter 5: A simple model for information cascade and Two kinds of phase transitions.- Chapter 6: Information cascade model on networks I .- Chapter 7: Information cascade on networks II.- Chapter 8: Correlation function for generalized Polya urns: Finite-size scaling analysis.- Chapter 9: Pitman distribution and Big data analysis of Japanese election.- Chapter 10: Exact Scale Invariance in Mixing of Binary Candidates: Big data analysis.- Chapter 11: Phase transition in a voting experiment of two-choice quiz.- Chapter 12: Phase transition in generalized Polya urn in Information cascade experiment.