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複雑な社会・産業システムをデザインする
複雑な社会・産業システムをデザインする
社会や産業が直面する課題は、技術だけでは解決できません。新しい技術が社会に導入されるとき、そこには人、組織、制度、政策、市場、環境など多くの要素が関わり、それらの相互作用によって想定外の結果が生じます。また、将来を正確に予測することが難しい中で、安全性や持続可能性を確保しながら意思決定を行う必要があります。
本研究室では、こうした複雑で不確実な社会・産業システムを対象として、システムズアプローチ、モデリング、シミュレーション、データ分析を基盤に、よりよいシステムの設計、意思決定、社会実装を研究しています。
研究グループ
研究グループ
本研究室では、システムデザインやシステム思考を基盤として、複雑で不確実な産業・社会システムの理解、設計、意思決定、社会実装に関する研究を行っています。定性的・定量的なモデリング、シミュレーション、データ分析を組み合わせ、企業戦略、社会政策、システム設計における意思決定を支援します。また、革新的なシステムの実現に必要なステークホルダー間の理解・合意形成やHuman-AI協働、さらに将来の不確実性や複雑な相互作用から生じるリスクを考慮した、頑健・適応的で安全なシステムや戦略の設計にも取り組んでいます。
こうした研究を、以下の3つのResearch Groupを中心に展開しています。
Systems Design & Decision Making / システムデザインと意思決定
Sustainable Industry Strategy / 持続可能な産業戦略 (稗方/中島)
Social Systems & Policy / 社会システムと政策 (中島/稗方)Knowledge & Human-AI Collaboration / 知識とHuman-AI協働 (稗方)
Uncertainty & Risk Management / 不確実性とリスクのマネジメント (中島)
3つの研究グループはそれぞれ異なる視点を持ちながら、システム思考、モデリング、シミュレーション、データ分析などの方法論を共有し、実際の社会・産業課題に対してグループを越えた研究を行っています。
Designing Complex Social and Industrial Systems
Designing Complex Social and Industrial Systems
Today's social and industrial challenges cannot be addressed by technology alone. When new technologies are introduced into society, their outcomes emerge through interactions among people, organizations, institutions, policies, markets, technologies, and the environment. At the same time, decisions must often be made without being able to predict the future accurately.
Our laboratory studies how complex and uncertain social and industrial systems can be better designed, managed, and implemented. Our research builds on systems approaches, modeling, simulation, and data analysis to support system design and decision-making in real-world contexts.
Research Groups
Research Groups
Our laboratory studies the understanding, design, decision-making, and implementation of complex and uncertain industrial and social systems based on systems design and systems-thinking approaches. We combine qualitative and quantitative modeling, simulation, and data analysis to support decision-making in areas such as corporate strategy, public policy, and system design. We also study stakeholder understanding and consensus building, Human-AI collaboration, and the design of robust, adaptive, and safe systems and strategies that explicitly account for uncertainty and systemic risk.
Our research is organized around the following three closely connected Research Groups:
Systems Design & Decision Making (GL: Hiekata / Nakashima)
Sustainable Industry Strategy — Hiekata / Nakashima
Social Systems & Policy — Nakashima / HiekataKnowledge & Human-AI Collaboration (GL: Hiekata)
Uncertainty & Risk Management (GL: Nakashima)
The three Research Groups address different aspects of complex systems while sharing common approaches such as systems thinking, modeling, simulation, and data analysis. Many of our research projects therefore span multiple groups in response to real-world industrial and societal challenges.
RG1: Systems Design & Decision Making / システムデザインと意思決定 (担当: 稗方/中島)
RG1: Systems Design & Decision Making / システムデザインと意思決定 (担当: 稗方/中島)
Keywords: Systems Design / Systems Engineering / Decision Making / System Dynamics / Agent-Based Modeling / Social Simulation / Socio-technical Systems / Data Analysis
現実の社会や産業では、多くのステークホルダー、技術、組織、市場、制度、環境が相互に影響しています。そのため、ある技術や施策を導入した結果が、当初の想定とは異なることも少なくありません。
本グループでは、システム思考やシステムズエンジニアリングを基盤として対象システムを定性的・定量的にモデル化し、System Dynamics、Agent-Based Modeling、Social Simulation、データ分析などを用いて、システムの振る舞い、ステークホルダー間の相互作用、意思決定のトレードオフを明らかにします。
企業戦略、産業変革、脱炭素、モビリティ、都市・地域政策、気候変動適応などを対象として、複雑な環境における意思決定とシステムデザインを支援します。
Real-world systems involve interactions among multiple stakeholders, technologies, organizations, markets, institutions, and environmental conditions. As a result, the consequences of introducing a technology or policy may differ substantially from what was initially expected.
Building on systems thinking and systems engineering, we develop qualitative and quantitative models and apply methods including System Dynamics, Agent-Based Modeling, Social Simulation, and data analysis. These approaches help reveal system behavior, stakeholder interactions, and trade-offs among alternative decisions.
Our applications include corporate and industrial strategy, industrial transformation, decarbonization, mobility, urban and regional policy, and climate-change adaptation.
RG1-1 Sustainable Industry Strategy / 持続可能な産業戦略 (稗方/中島)
RG1-1 Sustainable Industry Strategy / 持続可能な産業戦略 (稗方/中島)
シミュレーションやデータ駆動型モデルを用いて複雑なシステムの挙動を予測し、企業の意思決定やシステムデザインを支援します。
Predicting the behavior of complex systems using simulation and data-driven models to support corporate decision-making and system design.
企業組織などにおいて、マーケットの不確実性や組織内外のインタラクションのために、組織自体のデザインやオペレーションによりパフォーマンスがどのように変化するのかを予測することは困難です。また新技術によるインパクトを見積もることも困難を伴います。
本テーマでは、システム思考やシステムズエンジニアリングで利用される手法やアプローチにより、ステークホルダーの期待をとらえ、システムの挙動を定性・定量の両面から推定し、システムデザインのトレードスペースを示すことで、複雑なビジネスシステムのあるべき姿を検討します。
Within corporate organizations, it is difficult to predict how performance will change based on the organization's own design and operations due to market uncertainty and interactions both within and outside the organization.
This theme examines the ideal state of complex systems by employing methodologies and approaches used in systems thinking and systems engineering. It captures stakeholder expectations, estimates system behavior from both qualitative and quantitative perspectives, and identifies the trade-off space in system design.
複雑なビジネスコンテクストの分析/
Analysis of Complex Business Systems
複雑なビジネスコンテクストの分析/
Analysis of Complex Business Systems
産業界や社会を情報技術が相互につなげることで、人の暮らしはこれまでになく便利になりました。同時に、システム化された多種の技術や組織の複雑さのために、解決の難しい問題が数多く生まれました。例えば、研究室で取り組んでいる脱炭素の推進や自動化技術の導入には社会と技術の両面からの取り組みが必要です。 本研究室のこのような問題への取り組みでは、定性的なモデル化手法で対象システムのコンテクストや挙動を記述して理解を深め、曖昧な問題意識を解決可能な課題として定義することから研究テーマをデザインすることから始めます。
By connecting information technology to industry and society, people's lives have become more convenient than ever. At the same time, the complexities of the various systems and organizations that have been systematized have created many difficult problems to solve. For example, the promotion of decarbonization and the introduction of automation technology that we are working on in our laboratory require social and technological efforts.In our laboratory's approach to such problems, the research theme is to describe the context and behavior of the systems using a qualitative modeling method to deepen understanding and define ambiguous problem awareness as a solvable problem.
Chapter 2 of "Ichinose, Y., Hayashi, M., Nomura, S., Moser, B., & Hiekata, K. (2022). Sustainable Data Centers in Southeast Asia: Offshore, Nearshore, and Onshore Systems for Integrated Data and Power. Sustainable Cities and Society, 81, 103867. https://doi.org/10.1016/J.SCS.2022.103867"
By connecting information technology to industry and society, people's lives have become more convenient than ever. At the same time, the complexities of the various systems and organizations that have been systematized have created many difficult problems to solve. For example, the promotion of decarbonization and the introduction of automation technology that we are working on in our laboratory require social and technological efforts.In our laboratory's approach to such problems, the research theme is to describe the context and behavior of the systems using a qualitative modeling method to deepen understanding and define ambiguous problem awareness as a solvable problem.
Chapter 2 of "Ichinose, Y., Hayashi, M., Nomura, S., Moser, B., & Hiekata, K. (2022). Sustainable Data Centers in Southeast Asia: Offshore, Nearshore, and Onshore Systems for Integrated Data and Power. Sustainable Cities and Society, 81, 103867. https://doi.org/10.1016/J.SCS.2022.103867"
ゼロエミッションデータセンターのコンセプトデザイン
Social Systems Design towards Zero Emission: Data Center Case
ゼロエミッションデータセンターのコンセプトデザイン
Social Systems Design towards Zero Emission: Data Center Case
システム思考の方法論とシミュレーションを活用して、脱炭素社会に向けた社会や産業の基盤システムに関する意思決定の支援を行っている。右図の例では、再生可能エネルギーにより運用するデータセンターのコンセプトをシステム思考的なアプローチで検討した例であり、シンガポールの沿岸域や海上のデータセンターを実現すると二酸化炭素排出量を50%削減できる可能性を示した。このほかの社会や産業界の課題についても、シミュレーションによるシステムデザインに基づいた提言を行っている。Using system thinking methodologies and simulations, we support decision-making on social and industrial infrastructure systems towards a zero emission society. In the example shown in the figure on the right, the concept of a data center operated by renewable energy was examined using a system-thinking approach, and the possibility of reducing carbon dioxide emissions by 50% was shown by realizing coastal and offshore data centers in Singapore. We also make proposals based on system design by simulation on other social and industrial problems.
Ichinose, Y., Hayashi, M., Nomura, S., Moser, B., & Hiekata, K. (2022). Sustainable Data Centers in Southeast Asia: Offshore, Nearshore, and Onshore Systems for Integrated Data and Power. Sustainable Cities and Society, 81, 103867. https://doi.org/10.1016/J.SCS.2022.103867
Ichinose, Y., Hayashi, M., Nomura, S., Moser, B., & Hiekata, K. (2022). Sustainable Data Centers in Southeast Asia: Offshore, Nearshore, and Onshore Systems for Integrated Data and Power. Sustainable Cities and Society, 81, 103867. https://doi.org/10.1016/J.SCS.2022.103867
システムダイナミクスによるソフトウェア開発プロジェクトのマネジメント
A System Dynamics Approach towards Design and Management for Software Development Projects
システムダイナミクスによるソフトウェア開発プロジェクトのマネジメント
A System Dynamics Approach towards Design and Management for Software Development Projects
ソフトウェアプロジェクトは、挙動が動的で複雑かつ不確実であることから、時間、コストを想定の範囲に収めることが困難である。プロジェクトを成功させるために、プロジェクトマネージャーは、雇用、残業、納期変更などのオプションを適切に実行する。本研究では、動的かつ不確実な状況をモデル化し、雇用と残業状況をソフトウェア開発プロジェクトマネジメントのオプションとして、複数のシナリオに対して納期やコストを予測するシステムダイナミクスのシミュレーションモデルを開発した。その結果、コストと時間を考慮したプロジェクト開発における実行可能な計画を選択および設計するのに有用である。グラフはシミュレーションモデルの因果ループ図。Software projects are often very difficult to proceed within time, budget and specific scope due to dynamic, complex and uncertain behavior. Hence to make a project success, several control actions such as hiring, working overtime, extending deadline are taken by project manager. Considering the dynamic and uncertain situations, a simulation model has been developed to generate multiple scenarios as outputs by taking project manager’s options, hiring and overtime, as input parameters for software development project management. These results would support to choose and design a feasible plan for project development considering cost and time. The Causal Loop Diagram (CLD) represents the relationship among different variables while using project manager's options to measure project performance.
Mst Taskia Khatun, Kazuo Hiekata, Yutaka Takahashi & Isaac Okada (2022). Design and management of software development projects under rework uncertainty: a study using system dynamics. Journal of Decision Systems, 1-24. https://doi.org/10.1080/12460125.2021.2023257
Mst Taskia Khatun, Kazuo Hiekata, Yutaka Takahashi & Isaac Okada (2022). Design and management of software development projects under rework uncertainty: a study using system dynamics. Journal of Decision Systems, 1-24. https://doi.org/10.1080/12460125.2021.2023257
自動運航技術導入を加速する政策のデザイン
Policy Design for the Introduction of Automatic Operation Technology
自動運航技術導入を加速する政策のデザイン
Policy Design for the Introduction of Automatic Operation Technology
政策決定者や海運会社、造船所・メーカーといった海事産業のステークホルダーの関係性をモデル化した産業シミュレーターを構築し、自動運航船の導入を加速するステークホルダーの意思決定の組合せを探る世界でも先駆的な検討を行いました。3種類の自動運航技術の成熟度や、その組み合わせによる12タイプの自動運航船の経済性、安全性、またそれらに産業活動が及ぼす影響についてのモデルを作成してシミュレーションを行いました。その結果、様々な選択肢がある中で、研究開発と実証事業への補助金に規制緩和を組み合わせることで自動運航船の導入時期を早めることがわかりました。提案したシミュレーションを政策決定者や企業の意思決定に活用することで、新しい技術の早期導入に向けた円滑な合意形成プロセスを実現することが期待されます。
We built an industrial simulator that models the relationships among stakeholders in the maritime industry, including policy makers, shipping companies, and shipyards and manufacturers, and conducted a world-leading study to explore the combination of stakeholder decision making that will accelerate the introduction of automated vessels. The study modeled and simulated the economic feasibility and safety of 12 types of automated vessels in combination, and the impact of industrial activity on these factors. The results showed that, among the various options available, deregulation combined with subsidies for R&D and demonstration projects would accelerate the timing of the introduction of automated vessels. It is hoped that the proposed simulations can be used by policy makers and companies in their decision-making to achieve a smooth consensus-building process for the early introduction of new technologies.
Nakashima, T., Moser, B., & Hiekata, K. (2023). Accelerated adoption of maritime autonomous vessels by simulating the interplay of stakeholder decisions and learning. Technological Forecasting and Social Change, 194, 122710. https://doi.org/10.1016/J.TECHFORE.2023.122710
適切な意思決定で自動運航の実現が大きく加速 ― シミュレーションが明らかにするステークホルダーの協力の効果 ― https://www.k.u-tokyo.ac.jp/information/category/press/10269.html
We built an industrial simulator that models the relationships among stakeholders in the maritime industry, including policy makers, shipping companies, and shipyards and manufacturers, and conducted a world-leading study to explore the combination of stakeholder decision making that will accelerate the introduction of automated vessels. The study modeled and simulated the economic feasibility and safety of 12 types of automated vessels in combination, and the impact of industrial activity on these factors. The results showed that, among the various options available, deregulation combined with subsidies for R&D and demonstration projects would accelerate the timing of the introduction of automated vessels. It is hoped that the proposed simulations can be used by policy makers and companies in their decision-making to achieve a smooth consensus-building process for the early introduction of new technologies.
Nakashima, T., Moser, B., & Hiekata, K. (2023). Accelerated adoption of maritime autonomous vessels by simulating the interplay of stakeholder decisions and learning. Technological Forecasting and Social Change, 194, 122710. https://doi.org/10.1016/J.TECHFORE.2023.122710
適切な意思決定で自動運航の実現が大きく加速 ― シミュレーションが明らかにするステークホルダーの協力の効果 ― https://www.k.u-tokyo.ac.jp/information/category/press/10269.html
RG1-2 Social Systems & Policy / 社会システムと政策 (中島/稗方)
RG1-2 Social Systems & Policy / 社会システムと政策 (中島/稗方)
ステークホルダー間の利害対立を伴うニーズや解決策に対し、シミュレーションおよびデータ駆動型モデルを用いて、透明性・説明性の高い合意形成や意思決定、システムデザインの支援を行います。
Support for consensus building, decision-making, and system design with high transparency and accountability, using simulation and data-driven models to address conflicting needs and solutions among stakeholders.
社会システムにおいては、経済だけではない様々な要求を持つ利害関係者を対象に、望ましくかつ包摂的なデザインポイントを探索することになります。相反する利害関係があっても経済性などの共通の目的が存在する産業界とは異なり、利害やニーズをより深く理解して社会システムをデザインする方法について研究を行います。
In social systems, we explore desirable and inclusive design points for stakeholders with diverse demands beyond just economic ones. Unlike industry, where conflicting interests coexist with shared objectives like economic efficiency, we research methods for designing social systems based on a deeper understanding of stakeholders' interests and needs.
2004年ころから研究室で検討を開始したオンデマンド交通サービスは、基盤情報システムの開発ののち現在では50以上の自治体でサービスを提供している。運行計画を生成するアルゴリズムの高度化に取り組んだのち、現在は高齢者や過疎地の課題解決に向けた蓄積された利用データの活用検討をシステム設計方法論に基づいて行っている。図は利用データを地図上に表示した例。 We developed information systems for on-demand transportation service. The system is serving more than 50 municipality in Japan to solve problems of super aging society. We adopt systems design method to explore the utilization of trip data. The graph is a sample of visualization of trip data.
Kota Tsubouchi, Hiroyuki Yamato, Kazuo Hiekata, Innovative on-demand bus system in Japan, IET Intelligent Transportation System, 4(4), pp. 270-279, 2010.12
オンデマンド交通に関する研究
「日本一わかりやすいMaaS&CASE――ストーリーで理解する」中村 尚樹 (著) プレジデント社 第二章で紹介されています
Kota Tsubouchi, Hiroyuki Yamato, Kazuo Hiekata, Innovative on-demand bus system in Japan, IET Intelligent Transportation System, 4(4), pp. 270-279, 2010.12
オンデマンド交通に関する研究
「日本一わかりやすいMaaS&CASE――ストーリーで理解する」中村 尚樹 (著) プレジデント社 第二章で紹介されています
高齢化と過疎化が進む地域では、公共交通機関の設計が課題となっている。本研究は、千葉県成田市を事例に、デマンド交通（DRT）の運行範囲調整やタクシー補助券導入などの政策シナリオを比較。その結果、DRTの利便性向上やタクシー補助はコストを増やすものの、移動の質を大幅に高めることが判明した。特に移動困難者への効果は高く、地域間の移動格差も浮き彫りになった。このことから、公共交通と都市計画を統合したアプローチの必要性が示唆される。
Designing effective public transportation is a major challenge in aging and depopulated regions. Using Narita City, Chiba Prefecture, as a case study, this research compared policy scenarios such as adjusting the service areas of demand-responsive transit (DRT) and introducing taxi vouchers. The findings indicate that while improving the convenience of DRT and providing taxi subsidies increases costs, they significantly enhance the overall quality of mobility. The impact was particularly high for those with mobility challenges, and the study also highlighted significant mobility disparities between different areas. This suggests the need for a more integrated approach that combines public transportation with urban planning.
Takuto Ojima, Kazuo Hiekata, Takuya Nakashima (2025). A cost-effective strategy for enhancing mobility in aging communities: the case of Narita City, Frontiers in Sustainability, Volume 6 - 2025, ISSN 2673-4524. https://doi.org/10.3389/frsus.2025.1578781
Designing effective public transportation is a major challenge in aging and depopulated regions. Using Narita City, Chiba Prefecture, as a case study, this research compared policy scenarios such as adjusting the service areas of demand-responsive transit (DRT) and introducing taxi vouchers. The findings indicate that while improving the convenience of DRT and providing taxi subsidies increases costs, they significantly enhance the overall quality of mobility. The impact was particularly high for those with mobility challenges, and the study also highlighted significant mobility disparities between different areas. This suggests the need for a more integrated approach that combines public transportation with urban planning.
Takuto Ojima, Kazuo Hiekata, Takuya Nakashima (2025). A cost-effective strategy for enhancing mobility in aging communities: the case of Narita City, Frontiers in Sustainability, Volume 6 - 2025, ISSN 2673-4524. https://doi.org/10.3389/frsus.2025.1578781
市民リテラシーが参加型都市計画に与える影響のシミュレーション
Simulation of the Impact of Citizen Literacy on Participatory Urban Planning
市民リテラシーが参加型都市計画に与える影響のシミュレーション
Simulation of the Impact of Citizen Literacy on Participatory Urban Planning
市民リテラシー、すなわち市民の新技術に対する理解は、都市計画における合意形成に影響を与える。シミュレーションに基づく手法により、共有型自動運転車に対する理解における偏見や不確実性の影響を評価する。市民リテラシーの差異は、合意形成の速度と質に顕著な影響を及ぼす。調査結果は、都市計画において市民教育と啓発政策を個別に設計する必要性を強調している。
Designing effective public transportation is a major challenge in aging and depopulated regions. Using Narita City, Chiba Prefecture, as a case study, this research compared policy scenarios such as adjusting the service areas of demand-responsive transit (DRT) and introducing taxi vouchers. The findings indicate that while improving the convenience of DRT and providing taxi subsidies increases costs, they significantly enhance the overall quality of mobility. The impact was particularly high for those with mobility challenges, and the study also highlighted significant mobility disparities between different areas. This suggests the need for a more integrated approach that combines public transportation with urban planning.
Shiyuan WANG, Kazuo HIEKATA, Takuya NAKASHIMA (2025). Simulation-based evaluation of the impact of civic literacy on participatory urban planning: A study of introducing Shared Autonomous Vehicles, Journal of Urban Management, 14(3), pp. 769-786. https://doi.org/10.1016/j.jum.2025.02.001
Designing effective public transportation is a major challenge in aging and depopulated regions. Using Narita City, Chiba Prefecture, as a case study, this research compared policy scenarios such as adjusting the service areas of demand-responsive transit (DRT) and introducing taxi vouchers. The findings indicate that while improving the convenience of DRT and providing taxi subsidies increases costs, they significantly enhance the overall quality of mobility. The impact was particularly high for those with mobility challenges, and the study also highlighted significant mobility disparities between different areas. This suggests the need for a more integrated approach that combines public transportation with urban planning.
Shiyuan WANG, Kazuo HIEKATA, Takuya NAKASHIMA (2025). Simulation-based evaluation of the impact of civic literacy on participatory urban planning: A study of introducing Shared Autonomous Vehicles, Journal of Urban Management, 14(3), pp. 769-786. https://doi.org/10.1016/j.jum.2025.02.001
RG2: Knowledge & Human-AI Collaboration / 知識とHuman-AI協働 (担当: 稗方)
RG2: Knowledge & Human-AI Collaboration / 知識とHuman-AI協働 (担当: 稗方)
Keywords: Knowledge Modeling / Knowledge Management / Human-AI Collaboration / Teamwork / Explainable AI / Decision Support / Consensus Building / Participatory Modeling
優れたシステムの設計案が得られても、それを社会や組織の中で実現するためには、異なる専門性や価値観を持つ人々の理解と合意が必要です。また、生成AIをはじめとする人工知能の発展によって、人とAIが共同して考え、判断する機会が急速に増えています。
本グループでは、専門家が持つ知識の抽出・モデル化・共有、人間同士のチームワーク、AIによる予測・意思決定支援、Human-AI Collaborationなどを対象として、人と人、人とAIの知的協働を支援する方法と環境を設計します。
さらに、参加型モデリングやシミュレーションを活用し、異なるステークホルダーの理解や合意形成を支援する方法について研究します。
Even an excellent system design cannot be successfully implemented without shared understanding and agreement among people with different expertise, interests, and values. At the same time, advances in artificial intelligence, particularly generative AI, are rapidly expanding opportunities for humans and AI to reason and make decisions together.
This group investigates how knowledge can be elicited, modeled, shared, and used collaboratively. We study human teamwork, AI-supported prediction and decision-making, and Human-AI Collaboration to design environments in which people and AI can work together effectively.
We also explore participatory modeling and simulation as tools for supporting mutual understanding and consensus building among multiple stakeholders.
チームワークの可視化：複雑なシステムの挙動の共通理解
Teamwork Environement for Understanding Systems Behaviors
チームワークの可視化：複雑なシステムの挙動の共通理解
Teamwork Environement for Understanding Systems Behaviors
複雑なシステムに関する意思決定など、専門性の異なるチームメンバーによる知的活動のダイナミクスを明らかにするため、参加者の発話やソフトウエアの操作履歴やシミュレーションソフトへの入力、ビデオデータなどをセンシングするコラボレーション環境を開発する。複数のセンサーから得られた異なる種類のデータから議論の流れや内容を分析・可視化し、意思決定や合意形成の高度化を目指す。 右図はタンカーの船隊構成変更に関する意思決定のデモンストレーションの例。We build and test a collaborative design environment with IoT sensors to help researchers study engineering teamwork behavior as it relates to design performance. As a demonstration, we use an instrumented environment to observe engineers designing a fleet of crude oil tankers with the assistance of an interactive computer simulation. Recorded observations include user-triggered software events, simulation inputs and results, conversation activity, and video data. We may answer research questions about the nature of teamwork behavior and design performance.
I. Winder, D. Delaporte, S. Wanaka and K. Hiekata, "Sensing Teamwork During Multi-objective Optimization," 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA, 2020, pp. 1-6, doi: 10.1109/WF-IoT48130.2020.9221086.
I. Winder, D. Delaporte, S. Wanaka and K. Hiekata, "Sensing Teamwork During Multi-objective Optimization," 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA, 2020, pp. 1-6, doi: 10.1109/WF-IoT48130.2020.9221086.
チームワークの可視化：コミュニケーションのパターン分析
Teamwork Visualization
チームワークの可視化：コミュニケーションのパターン分析
Teamwork Visualization
創造的なアイデアを創出し実装するには、作業プロセスの設計のみならず、プロセスを効果的に実践するためのチームワークのサポートが不可欠である。図はアイデア創出ワークショップにおけるコミュニケーションパターンの変化を可視化した図である。どのような議論をどのようなコミュニケーションモードで行ったかという情報は、第三者がチームに介入をする上で有効な情報でああるとともに、チームメンバー自身がチームワークの振り返りをする時にも役立つ。In order to generate and implement creative ideas, it is essential not only to design the work process, but also to support teamwork to effectively implement the process. The figure visualizes the changes in communication patterns during an idea generation workshop. The information on the communication mode and the type of discussion is useful for managers to intervene in the team, as well as for team members themselves to reflect on their teamwork.
Peng, S., Amakasu, T., Kawauchi, H., Horii, H., & Hiekata, K. (2021). Development of Method for Visualizing Behavioral States of Teams. In L. Newnes, S. Lattanzio, B. R. Moser, J. Stjepandić, & N. Wognum (Eds.), Advances in Transdisciplinary Engineering. IOS Press. https://doi.org/10.3233/ATDE210134
Peng, S., Amakasu, T., Kawauchi, H., Horii, H., & Hiekata, K. (2021). Development of Method for Visualizing Behavioral States of Teams. In L. Newnes, S. Lattanzio, B. R. Moser, J. Stjepandić, & N. Wognum (Eds.), Advances in Transdisciplinary Engineering. IOS Press. https://doi.org/10.3233/ATDE210134
RG3: Uncertainty & Risk Management / 不確実性とリスクのマネジメント (担当: 中島)
RG3: Uncertainty & Risk Management / 不確実性とリスクのマネジメント (担当: 中島)
Keywords: Uncertainty Management / Risk Management / DMDU / Robust Decision Making / Adaptive Strategies / Scenario Discovery / System Safety / Safety Assurance / Verification & Validation
将来の変化やシステム内部の相互作用によって生じる不確実性とリスクを捉え、安全で頑健かつ適応的な意思決定とシステム設計を支援する方法を研究します。
社会・産業システムの長期的な意思決定では、技術の進歩、政策、市場、気候、社会受容性などについて、将来を正確に予測できない場合があります。一方、自律化・知能化されたシステムでは、人間、AI、ソフトウェア、センサー、制御システムなどの相互作用から、個々の機器の故障だけでは説明できないリスクが生じます。
本グループでは、Decision Making under Deep Uncertainty（DMDU）を基盤としたScenario Discovery、Robust Decision Making、Dynamic Adaptive Policy Pathwaysなどにより、幅広い将来に対して頑健かつ適応的な戦略を設計します。
また、STPA（System-Theoretic Process Analysis）、モデルベース開発、安全論証などを用いて、複雑なHuman-AI・自律システムにおけるシステミックリスクを分析し、安全要求、検証シナリオ、Verification & Validationまでを一貫して扱う方法論を研究します。
自動運航船やeVTOLなどの自律システム、気候変動適応、新技術の社会実装などを対象として、「将来が読めないこと」と「システムの相互作用から危険が生じること」の双方を扱い、より安全でレジリエントな戦略とシステムの実現を目指します。
This group studies methods for managing uncertainty and risk arising from both future change and interactions within complex systems, with the aim of supporting safe, robust, and adaptive decision-making and system design.
Long-term decisions about social and industrial systems must often be made under deep uncertainty regarding technological progress, policy, markets, climate, and social acceptance. At the same time, increasingly autonomous and intelligent systems create systemic risks through interactions among humans, AI, software, sensors, and control systems—risks that cannot always be explained by individual component failures.
We develop robust and adaptive strategies using approaches from Decision Making under Deep Uncertainty (DMDU), including Scenario Discovery, Robust Decision Making, and Dynamic Adaptive Policy Pathways.
We also study systemic safety of complex Human-AI and autonomous systems using System-Theoretic Process Analysis (STPA), model-based development, and safety assurance. Our goal is to connect risk analysis with safety requirements, verification scenarios, and Verification & Validation.
Applications include autonomous ships, eVTOL and other autonomous systems, climate-change adaptation, and the deployment of emerging technologies.
システミックリスクの分析と自律システムの安全設計/
Systemic Risk Analysis and Safety Design of Autonomous Systems
システミックリスクの分析と自律システムの安全設計/
Systemic Risk Analysis and Safety Design of Autonomous Systems
自動運航船をはじめとする自律システムでは、人間、AI、センサー、制御システム、周囲環境などが複雑に相互作用するため、個々の機器の故障だけでは説明できないリスクが生じます。本研究室では、STPA（System-Theoretic Process Analysis）とODD（Operational Design Domain）を組み合わせ、こうしたシステミックリスクを構造的に抽出・評価し、安全要求や検証条件へとつなげる方法を研究しています。
Autonomous systems such as autonomous ships involve complex interactions among humans, AI, sensors, control systems, and operating environments. As a result, safety risks may emerge from system interactions rather than individual component failures. We develop methods integrating System-Theoretic Process Analysis (STPA) and Operational Design Domains (ODD) to systematically identify and evaluate such risks and connect them to safety requirements and verification conditions.
Nakashima, T., Kureta, R., & Khastgir, S. (2025). Addressing systemic risks in autonomous maritime navigation: A structured STPA and ODD-based methodology. Reliability Engineering & System Safety, 261, 111041.https://doi.org/10.1016/j.ress.2025.111041
Autonomous systems such as autonomous ships involve complex interactions among humans, AI, sensors, control systems, and operating environments. As a result, safety risks may emerge from system interactions rather than individual component failures. We develop methods integrating System-Theoretic Process Analysis (STPA) and Operational Design Domains (ODD) to systematically identify and evaluate such risks and connect them to safety requirements and verification conditions.
Nakashima, T., Kureta, R., & Khastgir, S. (2025). Addressing systemic risks in autonomous maritime navigation: A structured STPA and ODD-based methodology. Reliability Engineering & System Safety, 261, 111041.https://doi.org/10.1016/j.ress.2025.111041
深い不確実性下における適応的意思決定/
Adaptive Decision-Making under Deep Uncertainty
深い不確実性下における適応的意思決定/
Adaptive Decision-Making under Deep Uncertainty
気候変動や新技術の社会実装など、数十年先を考える意思決定では、将来の環境や社会条件だけでなく、将来何を重視するかさえ変化する可能性があります。本研究室では、Decision Making under Deep Uncertainty（DMDU）やDynamic Adaptive Policy Pathways（DAPP）を用いて、特定の将来予測に依存せず、状況の変化に応じて戦略を変更できる適応的な意思決定方法を研究しています。さらに、経路依存性やロックインを考慮しながら、長期的に選択肢を維持できる戦略設計を目指しています。
Long-term decisions on issues such as climate change and emerging technologies must account not only for uncertain future conditions but also for possible changes in societal priorities. We study adaptive decision-making using approaches from Decision Making under Deep Uncertainty (DMDU), including Dynamic Adaptive Policy Pathways (DAPP), to design strategies that can evolve as conditions change. Our research also considers path dependency and lock-in to preserve future options and support flexible long-term decision-making.
Nakashima, T., Oyama, T., Suzuki, K., & Wada, R. (2026). Dynamic Adaptive Pathways Planning with Changing Criteria for Cross-Sectoral Climate Adaptation. Procedia Computer Science, 288, 11–20.https://doi.org/10.1016/j.procs.2026.08.180
Long-term decisions on issues such as climate change and emerging technologies must account not only for uncertain future conditions but also for possible changes in societal priorities. We study adaptive decision-making using approaches from Decision Making under Deep Uncertainty (DMDU), including Dynamic Adaptive Policy Pathways (DAPP), to design strategies that can evolve as conditions change. Our research also considers path dependency and lock-in to preserve future options and support flexible long-term decision-making.
Nakashima, T., Oyama, T., Suzuki, K., & Wada, R. (2026). Dynamic Adaptive Pathways Planning with Changing Criteria for Cross-Sectoral Climate Adaptation. Procedia Computer Science, 288, 11–20.https://doi.org/10.1016/j.procs.2026.08.180
相互依存する気候リスクの構造化と適応戦略/
Understanding Interconnected Climate Risks for Adaptation
相互依存する気候リスクの構造化と適応戦略/
Understanding Interconnected Climate Risks for Adaptation
気候変動の影響は、水資源、農業、災害、インフラ、生態系など複数の分野にまたがり、一つの分野で生じた影響が別の分野へ連鎖する可能性があります。本研究室では、システムズエンジニアリングやネットワーク分析の手法を用いて、分野間の因果関係やカスケードする影響をモデル化し、どの課題を個別に対応できるのか、どこで分野横断的な協調が必要なのかを明らかにする方法を研究しています。
Climate change impacts are interconnected across sectors such as water, agriculture, disaster management, infrastructure, and ecosystems, allowing risks in one domain to cascade into others. We apply systems engineering and network-based methods to model these causal relationships and cascading impacts, helping identify which challenges can be managed within individual sectors and where cross-sectoral coordination is required.
Nakashima, T., Oyama, T., Masago, Y., Liu, F., & Aoyama, K. (2026). From silos to coordination: A multi-method systems engineering framework for cross-sector climate adaptation. Environmental Research Communications, 8, 065039.https://doi.org/10.1088/2515-7620/ae7a02
Climate change impacts are interconnected across sectors such as water, agriculture, disaster management, infrastructure, and ecosystems, allowing risks in one domain to cascade into others. We apply systems engineering and network-based methods to model these causal relationships and cascading impacts, helping identify which challenges can be managed within individual sectors and where cross-sectoral coordination is required.
Nakashima, T., Oyama, T., Masago, Y., Liu, F., & Aoyama, K. (2026). From silos to coordination: A multi-method systems engineering framework for cross-sector climate adaptation. Environmental Research Communications, 8, 065039.https://doi.org/10.1088/2515-7620/ae7a02
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