We propose research and development of social data management, social simulation, and social simulation usage method and environment related to Computational Social Science. Computational social science is an academic field that uses personal behavior data and posted data that have become available with the spread of Internet of Things（IoT）, smart phones, and social networking services （SNS）. It takes social science, which traditionally relied on questionnaires and small-scale experimental data, to a new level. In realizing the Society 5.0, which will create a new society through systems that highly integrate cyberspace and physical space, computational social science has focused on both empirical researches based on big data and theoretical researches based on social simulation. Therefore, computational social science is one of the disciplines in which the humanities, social sciences, and information science can work together to solve social issues. In this proposal, we propose research and development of a tool that uses simulations instead of social experiments to show how society reacts to various measures to contribute to decision-making and consensus building. The proposed research and development includes three issues: management of social data, social simulation, and methods and environment for using social simulation.

This proposal proposes the management of data obtained from the real world, the development of social simulations, and the research and development of social simulation usage environments, which will contribute to the realization of Society 5.0 by using simulations to predict how society will react to various measures.

The management of social data requires to solve information processing issues such as integration of data items, cataloging, making them open, and platform construction for the data collected in the IT systems of the ministries and agencies, as well as social science issues such as the unification of the handling of personal information, which is regulated in slightly different ways in each municipality, the protection as well as the utilization of data, the development of rules for the development of data, and the consideration of data ethics for fair and equitable use. On top of that, building a social model that can be simulated based on the collected data is a challenge that should be solved by social and information sciences in cooperation.

The social simulation assumes an agent-based simulation in which an agent is set and simulated based on a social model that models how individuals optimize their behavior through interactions with the people and environment around them. Although social simulations have traditionally been used for theoretical research, we aim to use them to present the possibility of social responses to policies by improving their accuracy through data assimilation and other means.

The effects of the measures presented by the social simulation are presented as possibilities and should not be applied as-is. As significant social impacts are brought about by social simulation predictions, it is necessary to consider social implementation from the beginning of the development. As shown by the fact that the infection simulation that served as the basis for the call for an 80% reduction in contact opportunities in the response to the new coronavirus outbreaks has caused much debate, without sufficient verification and explanation of the simulation input data, parameters, and causal relationships assumed in the model, confidence in the simulation results may not be gained. Therefore, research and development on what explanations to provide and what social issues to apply it to is also essential. Potential areas of application include preventive medicine, infectious diseases, labor environment, sharing economy, disaster prevention, fintech, economic simulation, and welfare policy. We envision a promotion strategy to expand the scope of utilization through repeated demonstrations in limited areas.

In order to promote research and development in the interdisciplinary field of computational social science, it is also necessary to consider issues such as how to develop human resources who are familiar with both social science and information science and how to conduct research evaluation. In addition, it is also important to consider how to take into account minorities who are less likely to be represented in social data. To specifically address these issues, it is necessary to realize a method to check and evaluate the results of social simulations using social data.