Japanese agricultural sector is facing several risks such as "the aging farming population", "theshortage of successors", "the increasing abandoned farmland" and "low profitability ". Japanesegovernment and farmers have been deploying various efforts to eliminate these risks such as changingThe Agricultural Land Act and investing Infrastructure investment. There are cases of improving sales byconstructing new irrigation facilities and developing means of transportation and sales channels. In thispaper, authors made simple plant selection models of farmers and governments and try to check theimpact to planting of farmers by MAS (multi agent simulation).

In foreign-exchange (FX) dealing, FX brokers basically cancel out the orders fromtheir customers to prevent the price fluctuation risk by cover transactions with global megabankscalled Counter Party (CP). Each CP has huge amount of money to play a role of market reader,and might have proprietary know-how to foresee future price movements. From this viewpoint,we try to extract their knowledge by a machine learning approach, and therefore we apply thestacking method that aggregates some predictors to extract the ensemble knowledge. If CP's pricequotations are decided by foreseeing future price possibilities, their quotations can be consideredas predictors. From this concept, we apply the stacking method to their quotations and obtain theensemble knowledge from them. Through some simulations using real price data, we could con-firm that the given ensemble knowledge improves the prediction accuracy of FX price movementscompared to the machine learning using a single CP's price quotation.

As business environment changes rapidly, companies must respond to the change. As one ofcountermeasures against big change, divestment by M&A is becoming one of the options. In this paper, weclassify the industries by using FCM in order to reflect the trend in industrial classification that businessstructure changes drastically, we analyze what kind of companies divest business. As a result of analyzingduring 15 years from 2002 to 2016, we found that companies with higher debt ratio would divest thebusiness. Analyzing the effectiveness of industrial classification by fuzzy c means is one of our future works.

This research aims to improve the services of the food-service industry so it can lead to bettermanagement.The labor productivity of the food-service industry in Japan is very low, has long suffered fromlack of personnel and continues to struggle maintaining talent. Therefore, as a measure to solve this problem, weanalyzed the POS data of a luxury barbeque shop with a high customer unit price, making a hypothesis that theimprovement per customer price per customer is effective.And clarifying the purchasing behavior of eachproduct target using Machine Learming.

Buyers of used cars have to predict their dealing prices at auto auction held afterabout one month, but it is very difficult to predict them because each condition of used cars iscompletely different such as mileage, model year, body color, etc. For this reason, we proposetwo prediction methods: as the first one, we consider the median of dealing prices in each carmodel as a base price and predict its future price by time-series models: ARIMA and SARIMA.After that, the predicted bace price is converted into the individual price of each used car by themachine learning method that learned the relationship between the condition of used cars includingindividual prices and the bace price. As the second method, we adopt the deep learning approachto directly predict individual future prices of used cars without using the base price, but using allthe information attached to each used car as explanatory variables. To verify the usefulness of ourproposed methods for a used car assessment system, we performed some prediction tests using thereal auto-auction price data.

We propose a formal model for describing managerial decision processes in cases of businessinnovation. This formal model named Managerial Decision-making Description Model (MDDM) consistsof three kinds of components, an environment, a business structure and an agent's decision. The businessinnovation is represented as a business structure transition, in which the agents redefine theobjective-resources pairs in the business structure. We show that the MDDM describes actual businesscases and compares their similarities and differences. We also consider the MDDM application to casebased analysis on organizational simulation and business gaming.

The paper proposes a method of building a sentiment dictionary using news and stock prices inChina markets by textual analysis in finance. In order to obtain the amount of word polarities, weassociated the frequencies of the news' one-hot wordlist to the abnormal change rate of stock prices onthe publish date which is calculated by the method of the event study. We conducted the support vectorregression (SVR) and build a sentiment dictionary with polarity data from learners. Furthermore, weattempt to predict the news polarities by using the sentiment dictionary.

Company executives play an important role in corporate activities. In this research, we focus onthe characteristics of the top executive of each company, who make the important decisions. We attemptto measure the facial emotional score of these executives from the photographs published on their firm'sannual report. In addition, we create a narcissism index from the number of times these executives appearon their reports. Finally, we analyze how these emotional scores or narcissism indexes relate with theircompany's performance and behavior.