Researcher Interviews

Policy Twin: Toward evidence-based policy-making — Maximizing policies’ impact through digital space verification

Policy-making is the very foundation of social design, underpinning the fabric of how society works. The ability to digitally twin policies and open up new pathways to decision-making has the potential to reshape the whole process – and Fujitsu’s Policy Twin is an exciting new technology that can do just that. By digitally converting policies into machine-readable formats and treating them as formalized knowledge, Policy Twin has the potential fundamentally to transform how decisions are made. In this article, using an actual municipal initiative as an example, we speak with two researchers involved in the technology’s development to explore how Policy Twin is transforming the policy-making process.

Published on April 27, 2026

RESEARCHERS

Sayuri Kohmura

Senior Research Manager
Geoeconomic Resilience Core Project
Security Science Laboratory
Fujitsu Research
Fujitsu Limited

Yuta Kurume

Geoeconomic Resilience Core Project
Security Science Laboratory
Fujitsu Research
Fujitsu Limited

New approaches to solve increasingly complex and diverse challenges

To help address complex social issues such as climate change, economic disparities, and a declining and aging population, Fujitsu has been advancing research and development in converging technologies that combine humanities and social sciences with computer science. As part of these activities, Fujitsu is developing Social Digital Twin (*1), which recreates real-world human behavior and social dynamics in a digital space, enabling the effectiveness and impact of policies to be tested and verified in advance of implementation.

Building on this foundation, Fujitsu has advanced this concept further, with the introduction of our new technology: Policy Twin. This is a technology that goes beyond simply representing the state of people and society, and actually expresses policies themselves (*2) —implemented across various domains—in machine-readable flow formats that can be handled within a digital space. This makes it possible to formalize policies as explicit knowledge and apply a wide range of mathematical and analytical processes. One of the key features of Policy Twin is its ability to derive diverse policy options automatically while taking social acceptability into account.

In this article, we present a case study that demonstrates the value that Policy Twin can deliver, in this instance by applying it to municipal initiatives for preventing the progression of diabetic nephropathy. As well as setting out the value it delivers to users, the case study also features behind-the-scenes stories about its development.

(*1) A technology that reproduces not only the states of people and objects but also economic and social activities as a whole in a digital space based on real-world data
(*2) Refers to a series of activities involved in planning and executing which services to provide to which people

Policy-making challenges faced by national and local governments

── Could you tell us about the difficulties involved in policy-making by national and local governments?

Sayuri: Officials responsible for policy-making at national and local governments are required to examine and implement policies that can deliver greater impact, while responding to the diverse challenges faced by residents and communities. However, the factors that influence the effectiveness of policies are intricately intertwined, and properly evaluating their impacts under the constraints of limited budgets and human resources requires a great deal of effort as well as advanced analytical capabilities. As a result, it is not easy to determine in advance how effective a given policy will be. In many cases, current policy-making still has no choice but to rely on past outcomes, the experience of those in charge, and established practices.

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Policy Twin for maximizing policy effectiveness in solving social issues

── How does Policy Twin overcome these challenges? What can it enable?

Sayuri: By using Policy Twin, it becomes possible easily to compare and verify the effectiveness and impact of policies based on objective evidence. Policy Twin recreates policies themselves as flows within a digital space. In addition, by reconfiguring existing policies, it can automatically generate new policy candidates and quantitatively simulate the effects of each policy through digital rehearsal. As a result, before a policy is actually implemented, it becomes possible to visualize how much impact each policy is likely to produce. This enables policymakers to examine and select more effective policy options with greater confidence.

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Overview of Policy Twin

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── Tell us some more about the technical validation in a diabetes nephropathy progression prevention program.

Sayuri: Diabetic nephropathy is a disease that may progress to the point where dialysis becomes necessary, significantly impairing patients’ quality of life. It also contributes to rising medical costs at the national level. Against this backdrop, municipalities across Japan have been promoting initiatives aimed at preventing the progression of diabetic nephropathy by identifying high-risk individuals at an early stage and providing appropriate health guidance. In this project, we collaborated with a municipality to apply our technology to initiatives focused on preventing the progression of diabetic nephropathy.

── What do you need to take into consideration when identifying high-risk individuals?

Sayuri: When identifying high-risk individuals, it is essential to base the approach on national guidelines, while also taking into account the balance between a municipality’s operational capacity and costs. The number of people identified can vary significantly depending on the selection criteria, which in turn affects the amount of manpower and budget required. For example, even something as simple as how high-risk individuals are contacted—whether by phone, mail, or other means—differs from one municipality to another. As a result, initiatives that have proven effective in other municipalities may not necessarily be feasible under the organizational structures or budget constraints of one’s own municipality. Therefore, policies must be designed with careful consideration of both effectiveness and practicality, taking into account the specific circumstances of each municipality.

── How did you utilize Policy Twin?

Sayuri: First, based on the municipality’s policy documents related to measures for preventing the progression of diabetic nephropathy, we created policy flows that represented those initiatives. At the same time, we also developed policy flows for similar initiatives being implemented in other municipalities. We then decomposed the large number of policy flows we had created into their constituent components and reassembled them to generate new policy candidates automatically. For each candidate, we conducted simulations covering factors such as medical cost reductions, health improvement effects, and the costs associated with implementing outreach notifications. As a result, we were able to confirm that it is possible to derive policy candidates that are expected to reduce medical expenses and improve health indicators through health guidance. This could be achieved while still satisfying constraint conditions such as the available human resources required to provide that guidance.

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Example of existing policy documents and digitized policy flows

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Delivering tangible value to Policy Twin users

── What value does Policy Twin offer to its users?

Yuta: One of the major values of Policy Twin lies in digitizing policies in the form of flows, which makes it possible to view, in a mathematical and structural manner, content that previously could only be understood as individual documents. This enables users, for example, to make quantitative comparisons with similar or successful cases from other municipalities more easily, allowing policy discussions to move forward without excessive reliance on experience or established practices. In addition, another important value of Policy Twin is its ability to help users formulate new policy candidates in a way that fosters understanding and facilitates implementation. As policy candidates newly generated by Policy Twin are flexibly reconfigured based on existing policies, they incorporate new perspectives while remaining consistent with past initiatives and on-the-ground knowledge and experience. This makes them easier to accept in practical settings. This capability—deriving new policies while maintaining continuity with existing ones—is one of the key strengths of the Policy Twin algorithm. For more detailed information, please refer to our TechBlog (*3).

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(*3) Policy Twin: Innovative Policy-Making Technology for Social Design —New Technologies to Enhance Social Digital Twin—

── How do you plan to further enhance the capabilities of Policy Twin?

Sayuri: As Policy Twin continues to be used in real-world settings, we are steadily making improvements to its usability and overall user experience. First and foremost, we aim to evolve it into a more intuitive and practical technology for on-site use by carefully incorporating feedback from a wide range of users. At the same time, the scope of what Policy Twin can handle is not limited to a single policy or a specific domain. Looking ahead, we plan to broaden its perspective to capture policies implemented across various municipalities, and to include policies from different fields as well. By doing so, Policy Twin will grow into a foundational technology that enables users to consider optimal policy combinations as a whole, taking into account mutual interactions and trade-offs among different policies.

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Application to other fields

── Can this technology be applied to other fields?

Sayuri: Yes. Let me introduce some of the domains we have in mind and examples of potential applications.

Regional healthcare planning: Designing systems that can deliver high-quality medical and health services using limited regional social resources
Medical–dental collaboration: Coordinating policies for preventing the progression of diabetes with initiatives for preventing periodontal disease, thereby preventing severe conditions through synergistic effects
Transportation–healthcare collaboration: Improving convenience and increasing opportunities for early medical visits by providing transportation options tailored to individuals’ health conditions

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Beyond these examples, Policy Twin can be widely applied to any domain that involves deciding what kind of services should be provided to which people and executing those decisions.

Behind the development of Policy Twin

— How was the Policy Twin development team formed?

Yuta: The Policy Twin research and development team is composed of members with diverse expertise, unified around the concept of converging technologies—integrating different scientific and technological disciplines to address social challenges. What makes the team distinctive is not only the fact that it brings together specialists from a wide range of technical fields such as speech processing, image processing, data mining, and robotics, but also that it includes researchers specializing in the humanities and social sciences, including economics.

I myself have been involved in research and development related to computing, but I was strongly drawn to this cross-disciplinary approach and joined the team. After becoming part of the team, I found that this interdisciplinary nature naturally manifests itself in our day-to-day research activities. For example, during our regular sessions where we introduce and discuss academic papers, even when we are addressing the same theme, the perspectives differ, and new papers and unique viewpoints are brought to the table every time. These diverse perspectives play a vital role in deepening discussions and refining our research themes.

— Could you tell us about your approach to conducting research?

Sayuri: We have advanced our research with a strong focus on listening to voices from the field. At the initial stage of the research, we visited people involved in policy-making and conducted interviews to understand on-the-ground challenges, based on which we developed hypotheses and technical concepts. In order to gain new perspectives, we also spoke with professionals involved in game design. The methodologies used to design virtual worlds share many commonalities with policy-making, which is essentially the design of the real world, and these insights proved very valuable. As the research progressed, we continuously refined the technology by incorporating feedback from those with practical, on-the-ground experience, gradually brushing up the technology step by step.

Yuta: We are engaged in cutting-edge research, and to accelerate our progress, we are also conducting joint research with top-tier universities in Japan and abroad (*4). In working toward solutions for a wide range of social issues, we closely collaborate with university researchers on topics such as the development of new mathematical modeling technologies and their real-world implementation. At times, we visit universities to give research presentations or take part in discussions, and when I returned to my alma mater after some time, I felt a sense of nostalgia. At the same time, it made me reflect on my current position—engaging with universities through research in a very different way from when I was a student.

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(*4) Fujitsu Small Research Lab

Policy Twin development team

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Toward solving diverse business and social challenges

── Could you share your thoughts on future prospects?

Sayuri: First, we plan to promote the application of Policy Twin further to policy-making in the field of preventive healthcare in Japan, where we have been advancing proof-of-concept trials to date. Looking ahead, our goal is to expand into other domains and global markets, and ultimately to evolve Policy Twin into a technology that enables policy-making across regions and fields. Through this, we hope to contribute to solving a wide range of social challenges.

Yuta: I have a strong interest in mathematical approaches. Even for social challenges that may not appear to be directly related to mathematics at first glance, I believe that reexamining their underlying structure from a mathematical perspective can reveal new insights and pathways toward solutions. Going forward, I would like to continue pursuing research that leverages this perspective to contribute to real-world society.

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To learn more about Policy Twin, please refer to the related information below.

If you are interested in participating in a proof-of-concept (PoC) trial, please contact us.
- Fujitsu Research Portal：Policy Twin
To explore other advanced technologies from Fujitsu, please visit the Fujitsu Research Portal.
- Fujitsu Research Portal

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