The development and diffusion of science innovative technologies is indispensable for modern society. However, despite its benefits, the development of science and technology is not without various risks and social problems. So far as we are going to make societal decisions for the use of science and technologies with diverse social implications that encompass both risks and benefits, sometimes involving values implications, there is a need for mechanisms of decision making and management of the development and utilization of science and technology. Decisions can be different depending on environmental, institutional and cultural conditions. In addition, innovative policy instruments/ mechanisms to deal with rapidly changing science and technology, including regulatory measures, are required for implementing decisions. This course will deal with wide range of issues from local to global levels faced at the interface areas between science, technology and public policy from comparative perspective of Japan, the US and Europe. It offers key theoretical issues surrounding Science and Technology and provides students with the tools and frameworks, such as risk assessment/ management and transition management, to analyze them. This course invites students from both natural science backgrounds (i.e. the graduate school of engineering, new frontier science and so on) and social science backgrounds (graduate school of public policy, law and politics, and economics and public policy). We expect students to acquire interdisciplinary perspective in addition to their primary major, which is one of the critical skill in analyzing complex social technical issues posed by science and technology.

The development and diffusion of science innovative technologies is indispensable for modern society. However, despite its benefits, the development of science and technology is not without various risks and social problems. So far as we are going to make societal decisions for the use of science and technologies with diverse social implications that encompass both risks and benefits, sometimes involving values implications, there is a need for mechanisms of decision making and management of the development and utilization of science and technology. Decisions can be different depending on environmental, institutional and cultural conditions. In addition, innovative policy instruments/ mechanisms to deal with rapidly changing science and technology, including regulatory measures, are required for implementing decisions. This course will deal with wide range of issues from local to global levels faced at the interface areas between science, technology and public policy from comparative perspective of Japan, the US and Europe. It offers key theoretical issues surrounding Science and Technology and provides students with the tools and frameworks, such as risk assessment/ management and transition management, to analyze them. This course invites students from both natural science backgrounds (i.e. the graduate school of engineering, new frontier science and so on) and social science backgrounds (graduate school of public policy, law and politics, and economics and public policy). We expect students to acquire interdisciplinary perspective in addition to their primary major, which is one of the critical skill in analyzing complex social technical issues posed by science and technology.

The goal of this course is to strengthen students’ ability to apply insights from the behavioral sciences to the design of policies and interventions that enhance the well-being of individuals and societies across diverse global contexts. We will pursue this goal by: - Introducing key origins and recent advances in behavioral science research, drawing from psychology and behavioral economics. - Critically examining the gaps that persist between research, evidence, and real-world practice. - Applying core behavioral science theories and strategies to the analysis and design of policies and interventions, with specific policy domains or institutional contexts. The applications we explore will span multiple domains, including public health, environment, management, education, business, politics, and international development. This class is not a comprehensive survey of behavioral economics or social psychology. Instead, it offers a curated selection of research and theories that are especially valuable for thinking about practical application.

THIS COURSE WELCOMES STUDENTS FROM ALL BACKGROUNDS INCLUDING THOSE WHO HAVE NEVER CODED OR USED STATISTICAL MODELS THIS COURSE IS ABOUT EDUCATING STUDENTS IN AI/LLM/AGENT USE FOR RESEARCH PURPOSES Research Design & Methods: Design reproducible research workflows using AI/Agents Leverage public datasets to analyze policy problems Create visualizations to communicate policy findings Communicate findings clearly to broad audience Technical Skills: Understand AI/Agents in research workflows Download, clean and analyze policy-relevant data Develop scalable and replicable data infrastructure Work collaboratively using professional data science tools (GitHub, Overleaf, Google Colab)

広く産学官にわたってグローバルに活躍するために必要な「俯瞰力」を養成することを目指す。物質科学の各分野について最先端の知識を修得し、自分の専門分野と周辺分野がどのように関連するか、あるいはし得るか、について深く考察するために、第一線で活躍する講師の方々にその分野の最前線を概観していただく。さらに、それらの講義を通して異分野間のコミュニケーションを円滑に進めるための具体的方法論を学ぶ。 This survey course is designed to enable students to develop the broad perspective that is required of global leaders working in and across industry, academia, and government. Students will gain knowledge and insight on advancements in each field of materials science research, given by leading researchers working on the frontline in those fields. This will allow students to consider how peripheral fields are related to their own area of expertise, and to consider the potential for forging bridges between related fields in the future. In addition, students will learn specific methodologies designed to facilitate smooth communication among different disciplines.

In this course you will review the main concepts of the Central Dogma of Molecular Biology (CDMB). In this course we will discuss the concepts actively in English. You will apply knowledge of the CDMB to analyze and evaluate practical scientific problems and discuss the answers in English. Process skills, also known as soft skills or professional skills, are mentioned by employers as critical for an effective workforce. Many of these skills are also important to be successful in graduate school as well as many jobs in the “real world”. By taking this class you will improve in the following areas: ●Teamwork ●Problem solving ●Critical thinking ●Oral communication skills – specifically, by taking this class you will feel comfortable talking about basic CDMB in English to another scientist.

In this course you will review the main concepts of the Central Dogma of Molecular Biology (CDMB). In this course we will discuss the concepts actively in English. You will apply knowledge of the CDMB to analyze and evaluate practical scientific problems and discuss the answers in English. Process skills, also known as soft skills or professional skills, are mentioned by employers as critical for an effective workforce. Many of these skills are also important to be successful in graduate school as well as many jobs in the “real world”. By taking this class you will improve in the following areas: ●Teamwork ●Problem solving ●Critical thinking ●Oral communication skills – specifically, by taking this class you will feel comfortable talking about basic CDMB in English to another scientist.

THIS COURSE WELCOMES STUDENTS FROM ALL BACKGROUNDS INCLUDING THOSE WHO HAVE NEVER CODED OR USED STATISTICAL MODELS THIS COURSE IS ABOUT EDUCATING STUDENTS IN AI/LLM/AGENT USE FOR RESEARCH PURPOSES Research Design & Methods: Design reproducible research workflows using AI/Agents Leverage public datasets to analyze policy problems Create visualizations to communicate policy findings Communicate findings clearly to broad audience Technical Skills: Understand AI/Agents in research workflows Download, clean and analyze policy-relevant data Develop scalable and replicable data infrastructure Work collaboratively using professional data science tools (GitHub, Overleaf, Google Colab)

THIS COURSE WELCOMES STUDENTS FROM ALL BACKGROUNDS INCLUDING THOSE WHO HAVE NEVER CODED OR USED STATISTICAL MODELS THIS COURSE IS ABOUT EDUCATING STUDENTS IN AI/LLM/AGENT USE FOR RESEARCH PURPOSES Research Design & Methods: Design reproducible research workflows using AI/Agents Leverage public datasets to analyze policy problems Create visualizations to communicate policy findings Communicate findings clearly to broad audience Technical Skills: Understand AI/Agents in research workflows Download, clean and analyze policy-relevant data Develop scalable and replicable data infrastructure Work collaboratively using professional data science tools (GitHub, Overleaf, Google Colab)

THIS COURSE WELCOMES STUDENTS FROM ALL BACKGROUNDS INCLUDING THOSE WHO HAVE NEVER CODED OR USED STATISTICAL MODELS THIS COURSE IS ABOUT EDUCATING STUDENTS IN AI/LLM/AGENT USE FOR RESEARCH PURPOSES Research Design & Methods: Design reproducible research workflows using AI/Agents Leverage public datasets to analyze policy problems Create visualizations to communicate policy findings Communicate findings clearly to broad audience Technical Skills: Understand AI/Agents in research workflows Download, clean and analyze policy-relevant data Develop scalable and replicable data infrastructure Work collaboratively using professional data science tools (GitHub, Overleaf, Google Colab)