Press Release

Accelerated adoption of maritime autonomous vessels by simulating the interplay of stakeholder decisions and learning

Release:Jun 29, 2023 Update:Jun 29, 2023
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Graduate student Takuya Nakashima Project Associate Professor Bryan Moser Professor Kazuo Hiekata of the Department of Human and Engineered Environmental Studies in the Graduate School of Frontier Sciences led the research project.

Abstract

Despite worldwide efforts to develop technologies for autonomous vessels, large-scale implementation has yet to be achieved. This lack of achievement is not only due to technical and economic constraints but also to decision-making of individual stakeholders who may explore decisions narrowly, for example solely on the maximization of short-term profits. Therefore, it is desirable to consider other combinations of decision-making by stakeholders to break out of this situation. However, it is challenging to grasp how individual decisions affect the entire maritime industry given market, technology, and competition complexity.

In this study, we examine the investment strategies that stakeholders, especially policymakers, should adopt to promote the more rapid adoption of autonomous vessels. A simulator is developed which emulates the decisions and interplay of three types of activities: research and development, manufacturing, and operational activities. Patterns of adoption due to combinations of stakeholders' decisions are predicted to delay or accelerate the introduction of automation in the maritime industry. Specifically, this study estimates that the introduction of fully autonomous vessels can be accelerated by more than ten years through a mix of subsidies for prototyping, R&D (research and development) technology, deregulation for new technology, and encouraging ship operators to make safety-centric decisions.

Article

〈Publication〉Technological Forecasting and Social Change

〈Title〉Accelerated adoption of maritime autonomous vessels by simulating the interplay of stakeholder decisions and learning

〈Authors〉Takuya Nakashima*, Bryan Moser, Kazuo Hiekata

〈DOI〉 10.1016/j.techfore.2023.122710

〈URL〉 https://doi.org/10.1016/j.techfore.2023.122710

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