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      Two-dimensional constrained chaos and industrial revolution cycles

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          Significance

          Between the 1760s and the 1980s, three industrial revolutions occurred with almost equal intervals. We explain this phenomenon, which is left untouched in the existing literature. Although the standard theory of discrete-time chaos provides a perfect tool to explain business cycles in a single macroeconomic variable, it is of little use fully to explain innovation dynamics; innovation dynamics inherently involves two state variables representing monopolistically supplied new products and competitively circulating existing products separately. This study unveils two-dimensional ergodic chaos and develops a model of innovation dynamics with these variables. It demonstrates that if the first mover advantage from new products lasts for about 8 y, an industrial revolution-like phenomenon will occur about every 100 y.

          Abstract

          Since the 1760s, at least three industrial revolutions have occurred. To explain this phenomenon, we introduce two-dimensional (2D) constrained chaos. Using a model of innovation dynamics, we show that an industrial-revolution-like technology burst, driven by investment/saving motives for R&D activities, recurs about every one hundred years if the monopolistic use of a new technology lasts about 8 y.

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          Most cited references40

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          Capital in the Twenty-First Century

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            How to improve R&D productivity: the pharmaceutical industry's grand challenge.

            The pharmaceutical industry is under growing pressure from a range of environmental issues, including major losses of revenue owing to patent expirations, increasingly cost-constrained healthcare systems and more demanding regulatory requirements. In our view, the key to tackling the challenges such issues pose to both the future viability of the pharmaceutical industry and advances in healthcare is to substantially increase the number and quality of innovative, cost-effective new medicines, without incurring unsustainable R&D costs. However, it is widely acknowledged that trends in industry R&D productivity have been moving in the opposite direction for a number of years. Here, we present a detailed analysis based on comprehensive, recent, industry-wide data to identify the relative contributions of each of the steps in the drug discovery and development process to overall R&D productivity. We then propose specific strategies that could have the most substantial impact in improving R&D productivity.
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              Proof of the Ergodic Theorem.

              G Birkhoff (1931)
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                27 January 2023
                31 January 2023
                27 January 2023
                : 120
                : 5
                : e2117497120
                Affiliations
                [1] aKyoto University , 606-8501 Kyoto, Japan
                [2] bResearch Institute of Economy, Trade and Industry , 100-8901 Tokyo, Japan
                [3] cChuo University , 192-0351 Tokyo, Japan
                Author notes
                1To whom correspondence may be addressed. Email: yano@ 123456kier.kyoto-u.ac.jp .

                Edited by Jose Scheinkman, Columbia University, New York, NY; received June 5, 2022; accepted December 6, 2022

                Author information
                https://orcid.org/0000-0002-2702-1894
                https://orcid.org/0000-0002-6339-5535
                Article
                202117497
                10.1073/pnas.2117497120
                9945953
                36706220
                cb5a8ac5-d578-4b19-a396-a322eeb70249
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 05 June 2022
                : 06 December 2022
                Page count
                Pages: 10, Words: 5109
                Funding
                Funded by: Japan Society for the Promotion of Science London (JSPS), FundRef 501100000646;
                Award ID: Global COE
                Award Recipient : Makoto Yano Award Recipient : Yuichi Furukawa
                Funded by: Japan Society for the Promotion of Science London (JSPS), FundRef 501100000646;
                Award ID: #23000001
                Award Recipient : Makoto Yano Award Recipient : Yuichi Furukawa
                Funded by: Japan Society for the Promotion of Science London (JSPS), FundRef 501100000646;
                Award ID: 16H02015
                Award Recipient : Makoto Yano Award Recipient : Yuichi Furukawa
                Funded by: Japan Society for the Promotion of Science London (JSPS), FundRef 501100000646;
                Award ID: 18K01522
                Award Recipient : Makoto Yano Award Recipient : Yuichi Furukawa
                Funded by: Japan Society for the Promotion of Science London (JSPS), FundRef 501100000646;
                Award ID: 23730198
                Award Recipient : Makoto Yano Award Recipient : Yuichi Furukawa
                Funded by: Japan Society for the Promotion of Science London (JSPS), FundRef 501100000646;
                Award ID: 22K01405
                Award Recipient : Makoto Yano Award Recipient : Yuichi Furukawa
                Funded by: Toyota Foundation (公益財団法人トヨタ財団), FundRef 100009584;
                Award ID: D17-R-0088
                Award Recipient : Yuichi Furukawa
                Categories
                dataset, Dataset
                research-article, Research Article
                econ-sci, Economic Sciences
                415
                Social Sciences
                Economic Sciences

                industrial revolutions,two-dimensional (2d) chaos,intellectual properties,market quality,innovation cycles

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