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Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves

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  • Rupert Way
  • Franc{c}ois Lafond
  • Fabrizio Lillo
  • Valentyn Panchenko
  • J. Doyne Farmer

Abstract

We consider how to optimally allocate investments in a portfolio of competing technologies using the standard mean-variance framework of portfolio theory. We assume that technologies follow the empirically observed relationship known as Wright's law, also called a "learning curve" or "experience curve", which postulates that costs drop as cumulative production increases. This introduces a positive feedback between cost and investment that complicates the portfolio problem, leading to multiple local optima, and causing a trade-off between concentrating investments in one project to spur rapid progress vs. diversifying over many projects to hedge against failure. We study the two-technology case and characterize the optimal diversification in terms of progress rates, variability, initial costs, initial experience, risk aversion, discount rate and total demand. The efficient frontier framework is used to visualize technology portfolios and show how feedback results in nonlinear distortions of the feasible set. For the two-period case, in which learning and uncertainty interact with discounting, we compare different scenarios and find that the discount rate plays a critical role.

Suggested Citation

  • Rupert Way & Franc{c}ois Lafond & Fabrizio Lillo & Valentyn Panchenko & J. Doyne Farmer, 2017. "Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves," Papers 1705.03423, arXiv.org, revised Aug 2018.
  • Handle: RePEc:arx:papers:1705.03423
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    Cited by:

    1. Heinrich, Torsten, 2015. "Growth Cycles, Network Effects, and Intersectoral Dependence: An Agent-Based Model and Simulation Analysis," MPRA Paper 79575, University Library of Munich, Germany, revised 08 Jun 2017.
    2. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    3. Korzinov, Vladimir & Savin, Ivan, 2018. "General Purpose Technologies as an emergent property," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 88-104.
    4. Cameron Hepburn & Jacquelyn Pless & David Popp, 2018. "Policy Brief—Encouraging Innovation that Protects Environmental Systems: Five Policy Proposals," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 154-169.

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    More about this item

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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