In this paper, we propose a framework for discovering characteristic causal relation-ships for each person or thing, such as each individual customer in marketing and each individualdefective product in manufacturing. Our method enumerates all correlations from a given set ofsamples and discovers characteristic causalities by applying a causal discovery method to all sub-sets of samples identified by conditions derived from the correlations. After that, we can obtain acharacteristic causality for each new sample by identifying a condition that the new sample satis-fies. We also report experimental results on real marketing data to show the effectiveness of ourmethod.

In recent years, Japanese organizations have faced issues such as individual isolation andorganizational division as the diversity of employment patterns, age groups, genders, nationalities, etc.progresses. Since the working population is expected to decrease, it is necessary to realize innovation anddigital transformation with the aim of improving the productivity of each person, but there are manyclaims that the division of the organization is hindering it. .. In this research, we focus on humanresources who bridge various organizations, and by clarifying new working styles to improve theperformance of organizations by social network analysis, conventional uniform evaluation of humanresources and human resources The purpose is to obtain suggestions for reviewing the evaluation index oftraining. For verification, use the transmission / reception log of the internal currency that evaluatesaltruistic behavior.

In online food delivery, which is expanding all over the world, the increasing number of traffic accidentsduring delivery is becoming problematic. To ensure the safety of delivery workers and the residents, it isnecessary to understand the incentives of workers for behavior choices. While many existing sharingplatforms are pulling workers into the online labor process by such incentives as on-peak/off-peaksurcharges, workers try to accomplish more than their goals within a limited time. In this paper, to preventover-speeding during delivery, we compare and evaluate some task assignment methods and incentiveschemes by multi-agent simulation. We model worker's rational choices of behaviors based onreinforcement learning considering the profit and over-speeding of delivery workers.

In this paper, we present our concept on the analysis of the coordination of crowd-workers by multi-agent systems. The results inferred from the current analysis and the future plan of the analysis are presented.

The spread of COVID-19 has changed people's lifestyle, which caused great loss in variousindustries including the flower industry. Under the circumstance, new online shopping services forindividuals are attracting attention. However, it is not easy for those who have not purchased flowers oftenbefore to find "a right flower for them." Therefore, this study (1) defines some features of flower imagesand creates a hyperspace representing the similarity among flower images, and (2) proposes and evaluatesan interactiveevolutionary computation system for recommending a flower that reflects theuser's potentialtaste from a large number of candidates according only to a relatively small number of intuitive flowerevaluation madeby a user.

In this study, we tried to predict the brand switching behavior (variety-seeking behavior) forstimulating customer's purchases at the store in real-time. We used customer purchase data from Japaneseretail stores for evaluating our proposed method. Our proposed method includes some additionalexplanatory variables from existing studies, which considers purchasing pattern in a time series. The resultsshowed that our proposed method increased the prediction accuracy of brand switching behavior. Ourproposed method includes some additional explanatory variables from existing studies, which considerspurchasing pattern in a time series. The results showed that our proposed method increased the predictionaccuracy of brand switching behavior. Furthermore, our proposed method can be used for various storesand products because experimental results showed that our proposed method's effectiveness is independentof stores and products.

This study examines the effectiveness of pricing strategies in business hotels. Through theanalysis, we will attempt to identify the impact on the lease period and sales of business hotels, and how itchanges with the introduction of policy simulation. In this study, pricing strategy is considered as a policy.Through Agent-Based Simulation, we will match borrower and lender agents and analyze the results ofthe balance between supply and demand, and we will consider pricing strategy in our analysis. We will bemeasured the policy effects from individual strategies, using the framework created by previous studiesthat considered prices. A bottom-up policy evaluation will be conducted, and exploratory studies will beconducted to determine that it is an effective tool.

In this research, for companies that have introduced the free address system, we extract featuresof various work styles from employee location data and explore how they relate to performance. By doingso, we considered models that increase freedom of work environment and employee performance. Themethod adopted to model the relationship between work styles and performance is a least-squaresregression model using satisfaction data synthesized by sparse principal component analysis. From theresults, we proved the initial hypothesis that the performance of employees who have face-to-faceconnections with many employees regardless of their departments is high in a quick meeting area. Besides,as a new finding, it is the communication that occurs in a quick meeting area, not the planned meeting heldin the meeting room, that affects employees performance. In the relationship of employees who have similarwork contents, it was found that the performance of employees who bridge between different groups ishigher than that of employees who have a lot of face-to-face communication among employees.

Following the COVID-19 outbreak, unlike the plunge in traditional mainstream assets, crypto assets haveperformed very well. Indices that reflect changes in the crypto assets market have also grown in recent years. However,in terms of cryptocurrency index-related studies, there are not many, and the period analyzed is mostly three years. Inthis study, we analyze the impact of crypto assets on portfolio construction and attempt to clarify the risk-returncharacteristics of portfolios that include crypto assets as investment targets. We analyze historical data from July 2014to April 2020 based on the cryptocurrency index CRIX and six other traditional mainstream assets. By using theDCC-GARCH model, this study finds out the low dynamic correlation between the crypto assets and traditional ones.By using the mean-variance model, Cornish-fisher expansion, and T-copula CVaR approach to check the frontier lineand portfolio performance, we find out that crypto assets have the potential to improve the risk-return characteristics oftraditional portfolios.

Companies are now able to gather a lot of business process's execution data (calledevent log) by managing them with information systems. In addition, they seek to analyze theirprocess by leveraging event log to improve their processes (e.g. reducing waiting time and costs).However, there are many cases where process management systems record work suspending, butdo not record specific reason for that suspending. Hence, we propose the extracting representativeworkers' activity patterns method for understanding the activity of workers during work suspend-ing. The method takes into account the order of work with Damerau ? Levenshtein distance.Analysts are able to understand the cause of the work stoppage by comparing the extracted pat-terns with the process flow. We could find the workers' activity patterns which is along work flowof the process in the empirical evaluation.

Gas and electric power are one of the essential social infrastructure for social and economicactivities. The purpose of this research is to construct a gas' consumption model suitable for thedistribution and sales market of LP gas in Japan by utilizing temperature. As a method of predictingdemand, we would like to first create a model of power consumption and then apply it to LP gas. Analysisof customers' gas consumption can infer from what standpoint they were consuming public goods.Through this, we would like to offer users' reasonable price and usage to expand their choices andcontribute to the efficient use of social resources. Furthermore, analysis of LP gas usage through deeplearning is possible.

In general, baking companies develop a large number of new products, but only some of them becomeestablished as standard products, and the production of new products may result in a large amount of wasteloss due to unpredictable sales. Therefore, it is necessary to predict the future order volume based on theshort-term order data of new products. In this paper, we propose a method for forecasting and evaluationusing a combination of multiple explanatory variables and models, using shipment data from bakeries fora short period of time, such as one month after the start of sales. In the proposed method, the most accurateresults were obtained by constructing a model with variables that took into account the characteristics ofrandom forests and bread making, and explanatory variables that were modified for the low number ofstores.

Since the Japanese electricity retail market's liberalization started in 2016, the numbers ofpower generation companies and retailers joining the market are increasing. Power generation companiesand retailers must pay imbalance charges, which depend on the amount of error between planned supplyand demand volume and real supply and demand volume. Imbalance unit prices are calculated from spotprices and day-ahead market prices in Japan and volatile because imbalance unit prices are influenced bymany parameters like the imbalance between supply and demand, climate changes, etc. Forecastingimbalance unit prices are essential for risk management. Analyzing and forecasting imbalance unit pricesusing generalized additive models (GAM) and future work to improve the price forecasting estimation areshown in this study.