Chapter 2. Silent Genocide

Table of Contents

Complexity Economics
The weather map and the double farm
Technology monopoly or oligopoly
How to hire a programmer

Complexity Economics

Believing in past theories and not verifying them encourages superstition in science. Although subjectivity is possible in hypothesis making, subjectivity that undermines objectivity is dangerous in hypothesis testing. In economics, arbitrary models are a blight itself. Researchers at the Santa Fe Institute emphasized agent decision-making in their Agent Based Simulation of the Artificial Stock Market (ASM). It was at odds with the traditional model of decision-making that assumed the economic rationality of market participants.

I pointed out in my previous article that it is pointless for pension fund managers and central banks to prop up stock prices, but a simulation of an artificial stock market written by researchers at the Santa Fe Institute to run on Nextstep reproduced the rapid rise and fall of stock prices. They pointed out that market participants sometimes behave in ways that are not rational. People who only know the work of complexity economics from the documents they cite, who cannot understand the programming and simulations, or who have not read the researchers' papers in the first place, are stingy with what they argue is increasing returns in complex economics and deny the results, but they are mistaken. In particular, it is important to note that the combination of Genetic Algorithm (GA) and Classifier System, now called Learning Classifier System (LCS), has reproduced decision making. GA decision making is a simulation of a combination of conditions and actions, while Genetic Programming (GP), which came later, is more programmable in if-then-else. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2008 was awarded to Paul Robin Krugman, who studied increasing returns separately from William Brian Arthur of the Santa Fe Institute.[4]

There may be pros and cons to Moore's Law and Ray Kurzweil's the law of accelerating returns, but Brian Arthur's work on the added value of economics is important. This has nothing to do with the stock price of a company. When I was a graduate student, complex systems were all the rage, and when I went to bookstores, There were piles of books on the spine that said a complex system, but most of them were not worth reading. Those who do not know what kind of research field complex economics is can criticize it as a personal opinion, but it should not be stated as if all economists are in denial. It is subjective to formulate hypotheses, but objectivity is required for hypothesis testing. I dropped out of school without completing my Ph.D. and was cut off from being a researcher, but I know that much. In addition, complex systems are related to research fields such as Artificial Life because of the involvement of Mr. John H. Holland. There is also research that combines these.

Mr. Paul Michael Romer, winner of the 2018 Economics Prize, studied innovation and increasing returns. According to Mr. Romer's paper, in a normal, in increasing returns where innovation occurs, There are markets that have not been infiltrated by monopolies and oligopolies. In other words, a monopoly or oligopoly is an abnormal economy with diminishing returns that is subject to price competition and must be avoided as a matter of economic policy. It may be considered a contributing factor to Japan's long deflationary economy.

In his lecture Contemporary Economics (' 19) at the Open University of Japan, Mr. Takanori Yoda explains the Alfred Nobel Memorial Prize of the National Bank of Sweden in Economics, and he also mentions behavioral economics, one of his fields of expertise. Although I am not a student of the Open University of Japan, I watched the program with great interest. In behavioral economics, analysis is attempted by applying psychology to decision making. My idea of a Pigouvian tax on corporate tax rates can also be regarded as a method of encouraging changes in the decision-making of market participants through environmental economics. When building economic models and employing computer simulations in these fields, I recommend the Classifier System for Complex Systems and Deep Learning. In behavioral economics and positive economics, it is necessary to examine what incentives are effective in inducing decision-making on economic problems, especially the oligopoly of labor and distribution, and utilize them in policies. The achievement of the SDGs is really the internalization of external diseconomies. Environmental economics and behavioral economics can help to solve the problems of human survival.

For example, in determining the Pigovian tax rate, it would be necessary to seek Pareto optimization that simultaneously minimizes external diseconomies and maximizes corporate profits. Multi-objective optimization such as Non-dominated Sorting Genetic Algorithm (NSGA) is suitable for finding such an optimal solution. In deep learning, chess research is being attempted. These are decision-making studies themselves. AlphaZero, which is a combination of MCTS and ResNet, and Chess Transformer, which trains GPT-2 to train human game records as a PGN data set, can be applied to economic simulation as they are.

Faulty economic policies based on faulty doctrine lead to misery. The Japanese government hires only those who are quick to fill in the mark sheet as civil servants. We need to change the way we recruit. They should hire people with master's degrees and PhDs as civil servants and work with think tanks. The Nihon Keizai Shimbun and TV Tokyo have reported on companies underestimating the abilities of those who have completed graduate school, but showing the public that they are given preferential treatment in the national civil service will have a positive impact on the hiring of companies.