• My research field is a data analytics for creation of social values by data science approaches. In modern society, we can observe various data sets about our daily life, business or community. I aim to create new services for it using such data set and methods of Bayesian modeling, data mining or machine learning.

Computational social science seeks to understand social phenomena and human behavior using advanced computational methods, including machine learning, simulations, and digital experiments. The field covers a wide range of topics—from opinion formation on social media to spatial social segregation, where different social groups are geographically separated—and actively explores new possibilities offered by innovative generative AI technologies, such as large language models. In this term, it aims to contribute both to establishing a new research framework and to solving practical social issues.

A key advantage of computational social science methods lies in their ability to dynamically and accurately capture complex social phenomena and human behavior, overcoming limitations of traditional social sciences that primarily rely on small-scale surveys and static data.

First, by directly analyzing the vast amounts of behavioral data generated daily, it becomes possible to understand large-scale, high-granularity social processes that were previously difficult to grasp. Second, the use of computational methods allows for the discovery of complex patterns and the structure of new social phenomena that might be overlooked by human intuition or conventional theories alone. Third, by combining simulations and digital experiments, the field has moved beyond mere description and correlation analysis to verifying causal mechanisms and future scenarios.

In this way, computational social science complements traditional methodologies and possesses the advantage of developing social science into a discipline that contributes to solving social problems.

• This field is positioned as a new research framework that integrates social science and information science. It establishes the subject and direction of analysis based on theoretical considerations from social science, then verifies and develops those theories through empirical analysis and simulations that utilize computational techniques and big data.

By leveraging information technology and big data to examine diverse social phenomena and human behavior, the field offers valuable knowledge and expertise for understanding current societal conditions and for designing and evaluating effective intervention strategies.

Evaluating the effectiveness of social policies and business strategies through rigorous scientific methods—and improving them based on empirical evidence—is a pressing challenge across the public sector, private enterprises, and local communities. In Japan, the promotion of Evidence-Based Policy Making (EBPM) has recently gained attention; however, its practice remains largely confined to traditional administrative data, while the diverse and rich resources held by corporations and local communities remain underutilized. Building mechanisms that enable collaboration among public, private, and academic sectors is essential for sharing knowledge that contributes to solving societal challenges.

This research employs econometric methodologies to assess the causal impacts of policies and programs implemented by governments and corporations, linking the findings to real-world applications. By integrating administrative records with diverse private-sector data—including consumption, labor, education, and health—this study seeks to generate robust evidence and contribute to the advancement of evidence-based policy and strategy formulation.

Conventional policy evaluations and social surveys have often relied on descriptive statistics and case studies, which have limited capacity to identify generalizable causal relationships. Likewise, corporate initiatives have only rarely been subject to systematic and rigorous impact evaluation. This research employs econometric causal inference methods—such as regression discontinuity design, instrumental variables, and event studies—and applies them to both large-scale administrative data and non-traditional (“alternative”) data. Compared with conventional correlation-based analyses, this approach enables more robust and practically relevant evaluations of causal effects.

• Diverse Data Integration: Integrates administrative, corporate, and community data—regardless of type or continuity—to comprehensively evaluate policy and program impacts.
• Rigorous Causal Inference: Applies advanced econometric methods, such as regression discontinuity design and instrumental variables, to provide evidence that goes beyond simple correlations.
• Cross-Sectoral Applicability: Offers applicability across education, labor, welfare, urban policy, and consumer behavior.
• Social Implementation: Directly links research outcomes to policy improvement and corporate strategy design, fostering implementation through industry–government–academia collaboration.
• Capacity Building: Provides training in statistics and econometrics for municipal officials and corporate analysts, helping them build sustainable, data-driven decision-making cycles.

Public Sector: Provides scientific evidence for addressing educational inequality, designing welfare systems, labor market interventions, and urban environmental policies, thereby supporting the effective allocation of public resources.
Private Sector: Evaluates employee welfare programs, health management, workplace reforms, and reskilling initiatives, offering insights for sustainable and competitive strategic planning.
Society as a Whole: Builds sustainable and efficient social systems by integrating data and knowledge across governments, firms, and academic institutions.

In light of the increase in online transactions since the onset of the pandemic, this study examines how consumers' tendency to agree to terms and conditions without reading them in detail affects
consumers' perception of binding force of fine print contracts. Building on previous research in the United States, we conducted an online experiment targeting Japanese consumers and analysed their responses to different virtual scenarios involving financial products and sports clubs, including fraudulent conditions.

Unlike previous studies, which focused on face-to-face transactions, this study examines online transactions in Japan. It takes a unique approach by investigating the impact of website explanations, the stepwise effect of consumers' purchase behavior, and the effect of different transaction targets, such as financial products and sports clubs.

• The following findings were obtained from the online experiment.
• (1) In online transactions, the presence of terms and conditions makes consumers strongly aware of their binding nature. Even if the terms and conditions contain unfair contents, consumers believe that they have 'agreed' to them and are bound by them simply by virtue of their existence and the act of clicking or tapping on them.
• (2) The effect of explanations on websites in strengthening the binding force of terms and conditions is limited.
• (3) The evaluation of the binding nature of terms and conditions by consumers varies depending on the type of transaction, e.g. financial products versus sports club memberships. In everyday sports club transactions, consumers tend to evaluate unfair conditions more strictly.

The results of this study suggest two practical measures for creating a fair trading environment: policy formulation and legal reform.
(1) Consumer protection laws and cancellation rules must be strengthened to prevent unfair trading practices.
(2) Since the impact of binding rules varies depending on the type of goods and services, it is necessary to formulate sector-specific policies.