駐輪ステーション間を自由に往来できるシェアサイクル事業の運営では、自転車の偏在による機会損失を解消するため、トラックによる再配車を行なっている。本研究では、変動型インセンティブを提示することにより、自転車の偏在を解消し、従来のシェアサイクル事業の運営とは抜本的に異なる運営方法の検証を目的とする。検証には、千葉県幕張エリアのシェアサイクル実証データを利用して週末の需要を再現し、利用者と駐輪ステーションをエージェントとするマルチエージェント・シミュレーションを用いる。駐輪ステーションエージェントには、自律的な学習による有効な方策を確認するため、強化学習を利用する。
The number of M&A and IPO implementations has been increasing year by year, and the capitalmarket has become more active. Along with those situations, when a manager examines M&A and IPO, thecorporate value calculation is an important decision-making index. This paper attempts to improve theaccuracy of selecting similar companies in the process of comparable company analysis and DCF methods.Specifically, Sparse Composite Document Vector (SCDV) was created based on published patentdocuments. Next, similar companies were selected by calculating the distance between companies from thedocument vector made by SCDV.
The influence of SNS or the impact of influencers is expanding on our society. This studyanalyzes the relationships between tweets and news text of influencers in the Japanese stock market. Roleof social media in the financial market is also examined by extracting the data related to the tweetsinformation.
Recently, science and technology have become more sophisticated and complex, and it is s veryimportant issue to find and promote how to accumulate and combine knowledge to develop science andrealize innovation. In this research, we construct collaborative researcher network through collaborativeresearch relationship of KAKEN database, and analyze it from the viewpoints of knowledgeaccumulation/transfer and knowledge combination/fusion. We made genealogy maps of researchers foreach research category of Kakenhi, and visualized flows of the knowledge. And we also extractedresearchers who mediate and promote the flow of knowledge, what we call "Gatekeepers", and analyzedfeatures of them.
For the development of science and the realization of innovation, the analysis ofresearchers who contribute them, especially "Star Scientists" who have made remarkableachievements in various aspects including academics, is important to make science andtechnology policies. In this paper, researchers with highly cited papers are extracted fromClarivate Analytics Web of Science, and their characteristics are analyzed from the viewpointof interdisciplinary research in each research field. Furthermore, after clarifying thecharacteristics of researchers with highly cited papers and the characteristics of starscientists with outstandingly highly cited papers, we will try to obtain implications for scienceand technology innovation policies and corporate technology management strategies.
Investors refer to a variety of information when making investment decisions: The amount ofavailable information is increasing day by day due to the spread of the Internet, and it is difficult to graspall information such as causality. In this study, we attempt to construct an understanding support model thatcan visualize news through Attention-Based Bi-LSTM model using Reuters News as an analysis target.
本邦では,社会の高齢化・長寿化が進展にするにつれ,資産の形成や取崩し,承継領域に係る論点について関心が高まっている.しかし、退職後の資産取崩しを前提とした資産形成・運用に関して,必ずしも理論や基本的な考え方が整備されているとは言い難い.そこで本稿では,著者らが提案している資産形成や取り崩しに係るエージェントモデルを拡張する.外生的に与える各種シナリオにおいて,個人の意思決定が資産枯渇に与える影響を分析する.
Economic fluctuations have a great impact on business management. In this study, we explorethe method to improve the accuracy of the prediction model of the economic fluctuation using the economicwatcher survey reported by the Cabinet Office, the Government of Japan. The economic watcher surveyincludes interviewees' answers in the text and the economic business trend tag for each answer. Theclassification was attempted through the LSTM model. As a result of the analysis, we found the predictionaccuracy could be improved by using texts generated by GPT-2. Further examination of the classificationmodel will be planned.
近年、画像解析の分野をはじめとして深層学習モデルへの期待が高まる一方、複雑なブラックボックスモデルであることに対する懸念も指摘されてきており、モデルの解釈性・説明性に関する研究が盛んに行われている。本研究では、深層学習モデルのパラメータ推計に実数値GAを適用し、実数値GAの遺伝子の分散を活用した変数選択手法を活かして深層学習モデル内におけるインプットデータの重要度を定量的に評価できる仕組みを構築し、モデルの解釈性工場を目指す。
In recent years, the labor share has been declining in Japan, causing a hollowing out of the realeconomy. The purpose of this study is to clarify the relationship between labor shares and payout ratios. Weplan to examine whether the relationship between the labor share and payout ratio can be demonstrated byusing nonlinear analysis such as machine learning methods.
Japan is suffering from a serious labor shortage due to the economic expansion anddemographic change in the non-regular employment of the labor market. In particular, non-regularemployees account for 80% of employees in the food-service industry is a critical issue. The purpose ofthe present study is to analyze a mechanism of job matching focusing on non-regular employmentthrough agent-based modeling.
Recently Japanese government plans to introduce new schemes of PDS (Personal Data Store),data bank and data trading market to accelerate secure and smooth data circulation. While it becomes afocus of attentions as one of emerging market, there are some consumers who are not proactive to utilizetheir own personal data through PDS and data bank. As a result, it has not been clear how data tradingmarket grows yet. In this research, authors create an agent-based model (ABM) which simulates thebehaviors of consumers (data generators), company (servicers), platformers (existing online platformersand emerging data brokers) in data trading market and evaluate the impact of several measures toaccelerate secure and smooth data circulation on data trading market.