Economic Foundations for Social Complexity Science Theory, Sentiments, and Empirical Laws

Today, we daily encounter a situation where we use a large volume of information as we engage in various social activities. For instance, when we do online shopping, we decide which item to purchase by referring to recommendations provided by analyzing an enormous quantity of data on purchases made by a multitude of shoppers. Likewise, prices of goods are determined by estimating demand based on historical sales data, information on raw materials, and weather forecasts. All the socio-economic phenomena in this world are related with each other through information.
 
In consideration of the present state as described above, this book proposes a new approach to analyzing socio-economic phenomena. A socio-economic phenomenon is defined as a phenomenon arising from interactions between or among multiple individuals, organizations, or systems having different sets of behavioral principles and decision-making criteria as they come together and influence each other. Behavioral rules of individuals, organizations, and systems change with the passage of time to evolve into those more adapted to the environment by gathering more information and accumulating experiences. This entails in continual change in the behavior of socio-economic phenomena. Techniques for analyzing such socio-economic phenomena based on a large volume of real-world data as well as research on the theoretical modeling of socio-economic systems as seen from the perspective of a complex adaptive system are introduced through a series of chapters.
 
Chapter 1, authored by Alan Kirman, a mathematical economist who is considered the first advocate of and leading expert in complexity economics, provides an overview of this field of study and explains basic concepts concerned. Written in an easy-to-understand manner, the chapter is a must-read for those who are unfamiliar with this field of study.
 
The rest chapters are structured into three parts. Part I introduces studies on theoretical models of socio-economic phenomena using a complex adaptive system approach. While each chapter deals with a different economic phenomenon, all the theoretical models presented therein are common in that they are constructed from combinations of multiple heterogeneous behavioral factors to explain economic phenomena. In this light, there is a study using agent-based models (ABM), in which a targeted phenomenon is modeled as a collection of multiple computer programs. There is also a chapter presenting an econophysics study, in which a model of economic phenomena is constructed based on a theory of physics.
 
Part II introduces studies that attempt to explain economic phenomena by analyzing big data. In one of these studies, economic news reports are automatically analyzed using machine learning techniques, while in another, the relationship between listed stock issues is analyzed using high-frequency data on stock orders observed every fraction—such as one thousandth or one millionth—of a second. Each of these studies aims to explain the relationships between factors underlying a target economic phenomenon from an enormous volume of data, and it is fair to say that this is the area of research being developed precisely because of greater big data accessibility today.
 
Part III introduces studies that analyze observed data, focusing on financial systems. These include empirical studies using stock market data and corporate financial data.
 
As such, this book covers a broad spectrum of latest research methodologies ranging from theoretical foundations to empirical studies and the utilization of big data. It would be our great pleasure if this book could prompt readers to take interest in this field of study, find a specific kind of research of their interest, and strive to expand it on their own.
 

(Written by Kiyoshi Izumi, Professor, School of Engineering / 2018)