Recent Publications

•Anuraag Singh, Giorgio Triulzi, Christopher L. Magee. Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description,
Research Policy, Volume 50, Issue 9, 2021, 104294, ISSN 0048-7333, https://doi.org/10.1016/j.respol.2021.104294.
(https://www.sciencedirect.com/science/article/pii/S0048733321000950)

•Magee, C. L., Basnet, S., Funk, J. L., & Benson, C. L. (2016). Quantitative empirical trends in technical performance. Technological Forecasting and Social Change, 104, 237–246. https://doi.org/10.1016/j.techfore.2015.12.011

•Benson, C. L., & Magee, C. L. (2018). Data-Driven Investment Decision-Making: Applying Moore’s Law and S-Curves to Business Strategies. ArXiv:1805.06339 [Econ]. http://arxiv.org/abs/1805.06339

•Triulzi, G., Alstott, J., & Magee, C. L. (2020). Estimating technology performance improvement rates by mining patent data. Technological Forecasting and Social Change, 158, 120100. https://doi.org/10.1016/j.techfore.2020.120100