New Things Under the Sun

Much of the world’s population lives in countries in which little research happens. Is this a problem? According to classical economic models of the “ideas production function,” ideas are universal; ideas developed in one place are applicable everywhere. This is probably true enough for some contexts; but not all. In this post we’ll look at four domains - agriculture, health, the behavioral sciences, and program evaluation research - where new discoveries do not seem to have universal application across all geographies.This podcast is an audio read through of the (initial version of the) article "When research over there isn't helpful here," originally published on New Things Under the Sun.Articles mentionedComin, Diego, and Marti Mestieri. 2014. Technology diffusion: Measurement, causes, and consequences. In Handbook of economic growth, Vol. 2, 565-622. Elsevier. https://doi.org/10.1016/B978-0-444-53540-5.00002-1Verhoogen, Eric. Forthcoming. Firm-level upgrading in developing countries. Journal of Economic Literature. (link)Moscona, Jacob, and Karthik Sastry. 2022. Inappropriate technology: Evidence from global agriculture. SSRN working paper. https://doi.org/10.2139/ssrn.3886019Wilson, Mary Elizabeth. 2017. The geography of infectious diseases. Infectious Diseases: 938–947.e1. https://doi.org/10.1016%2FB978-0-7020-6285-8.00106-4Wang, Ting, et al. 2022. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604(7906): 437-446. https://doi.org/10.1038/s41586-022-04601-8Hotez, Peter J., David H. Molyneux, Alan Fenwick, Jacob Kumaresan, Sonia Ehrlich Sachs, Jeffrey D. Sachs, and Lorenzo Savioli. 2007. Control of neglected tropical diseases. New England Journal of Medicine 357(10): 1018-1027. https://doi.org/10.1056/NEJMra064142Henrich, Joseph, Steven J. Heine, and Ara Norenzayan. 2010. The weirdest people in the world? Behavioral and Brain Sciences 33(2-3): 61-83. https://doi.org/10.1017/S0140525X0999152XApicella, Coren, Ara Norenzayan, and Joseph Henrich. 2020. Beyond WEIRD: A review of the last decade and a look ahead to the global laboratory of the future. Evolution and Human Behavior 41(5): 319-329. https://doi.org/10.1016/j.evolhumbehav.2020.07.015Klein Richard A., et al. 2018. Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science. 2018;1(4):443-490. https://doi.org/10.1177/2515245918810225Schimmelpfennig, Robin, et al. 2023. A Problem in Theory and More: Measuring the Moderating Role of Culture in Many Labs 2. PsyArXiv. https://doi.org/10.31234/osf.io/hmnrx.Vivalt, Eva. 2020. How much can we generalize from impact evaluations? Journal of the European Economic Association18(6): 3045-3089. https://doi.org/10.1093/jeea/jvaa019Vivalt, Eva, Aidan Coville, and K. C. Sampada. 2023. Tacit versus Formal Knowledge in Policy Decisions.

This week, Arnaud Dyèvre (@ArnaudDyevre) and I follow up on a previous podcast, where we documented a puzzle: larger firms conduct R&D at the same rate as smaller firms, despite getting fewer (and more incremental) innovations per R&D dollar. Why wouldn’t firms decelerate their research spending as the return on R&D apparently declines? In this follow-up podcast, we look at one explanation: firms of different sizes face different incentives when it comes to innovation.This podcast is an audio read through of the (initial version of the) article "Big firms have different incentives", originally published on New Things Under the Sun.

How do academic researchers decide what to work on?  Part of it comes down to what you judge to be important and valuable; and that can come from exposure to problems in your local community. This podcast is an audio read through of the (initial version of the) article "Geography and What Gets Researched", originally published on New Things Under the Sun.

Most of the time, we think of innovation policy as a problem of how to accelerate desirable forms of technological progress. But there are other times when we may wish to actively slow technological progress. The AI pause letter is a recent example, but less controversial examples abound. A lot of energy policy acts as a brake on the rate of technological advance in conventional fossil fuel innovation. Geopolitical rivals often seek to impede the advance of rivals’ military technology.Today I want to look at policy levers that actively slow technological advance, sometimes (but not always) as an explicit goal.This podcast is an audio read through of the (initial version of the) article "How to impede technological progress", originally published on New Things Under the Sun.

This is not the usual podcast on New Things Under the Sun. For the third issue of Asterisk Magazine, Tamay Besiroglu and I were asked to write an article on how likely it is that artificial intelligence will lead to not just faster economic growth, but explosive economic growth. (Tamay will introduce himself in a minute here). Since we wrote that article as a literal dialogue, we thought it would be fun to also record ourselves performing the parts we wrote for ourselves and that is what we bring to you on this very special edition of New Things Under the Sun. During this podcast, you’ll hear two voices - mine and Tamay’s - as we perform our debate about the potential for explosive economic growth after we develop sufficiently advanced artificial intelligence.Then, after about an hour, our performance of the article will wrap up, but we keep talking. For another forty minutes, we talk a bit about policy implications of artificial intelligence, the prospects for spooky smart AI, and how our own views have evolved on this topic.If you want to read our article instead of listening, head over to here. If you’ve already read that and just want to hear some of our extra commentary, jump to about one hour into this podcast. Special thanks to Clara Collier, Asterisk’s Editor-in-Chief, for reaching out to us and giving us this opportunity.

We’ve got something new this week! This is post, which is on how the size of firms is related to the kind of innovation they do, is the first ever collaboration published on New Things Under the Sun. My coauthor is Arnaud Dyèvre (@ArnaudDyevre), a PhD student at the London School of Economics working on growth and the economic returns to publicly funded R&D. Going into this post, Arnaud knew this literature better than me and drew up an initial reading plan. We iterated on that for awhile, jointly discovering important papers, and eventually settled on a set of core papers, which we’ll talk about in this post. I think this turned out great and so I wanted to extend an invitation to the rest of you - if you want to coauthor a post with me, go to newthingsunderthesun.com/collaborations to learn more. One last thing; I want to assure listeners that, as in all my posts, I read all the papers that we talk about in detail in the following podcast. There is no division of labor between coauthors on that topic, because I view part of my job as making connections between papers, and I think that works better if all the papers covered on this site are bouncing around in my brain, rather than split across different heads. So what you are about to hear is not half Arnaud and half me, it’s all him and all me, all the time.Articles mentioned

Innovation has, historically, been pretty good for humanity. But technology is just a tool, and tools can be used for good or evil purposes. So far, technology has skewed towards “good” rather than evil but there are some reasons to worry things may differ in the future. What does science and technology policy look like in a world where we can no longer assume that more innovation generally leads to more human flourishing? It’s hard to say too much about such an abstract question, but a number of economic growth models have grappled with this idea.This podcast is an audio read through of the (initial version of the) article "When technology goes bad", originally published on New Things Under the Sun.Articles Mentioned:Jones, Charles. 2016. Life and Growth. Journal of Political Economy, 124 (2): 539 - 578. http://dx.doi.org/10.1086/684750Jones, Charles. 2023. The A.I. Dilemma: Growth versus Existential Risk. Working paper.Singla, Shikhar. 2023. Regulatory Costs and Market Power. LawFin WP 47. http://dx.doi.org/10.2139/ssrn.4368609Aschenbrenner, Leopold. 2020. Existential risk and growth. Global Priorities Institute Working Paper 6-2020. Link.Acemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, and David Hemous. 2012. The Environment and Directed Technical Change. American Economic Review 102 (1): 131-66. http://dx.doi.org/10.1257/aer.102.1.131

Scientific peer review is widely used as a way to distribute scarce resources in academic science, whether those are scarce research dollars or scarce journal pages. At the same time, peer review has several potential short-comings. One alternative is to empower individuals to make decisions about how to allocate scientific resources. Indeed, we do this with journal editors and grant makers, though generally in consultation with peer review. Under what conditions might we expect individuals empowered to exercise independent judgement to outperform peer review?This podcast is an audio read through of the (initial version of the) article "Can taste beat peer review?", originally published on New Things Under the Sun.Articles mentionedWagner, Caroline S., and Jeffrey Alexander. 2013. Evaluating transformative research programmes: A case study of the NSF Small Grants for Exploratory Research programme. Research Evaluation 22 (3): 187–197. https://doi.org/10.1093/reseval/rvt006Goldstein, Anna, and Michael Kearney. 2017. Uncertainty and Individual Discretion in Allocating Research Funds. Available at SSRN. https://ssrn.com/abstract=3012169 or http://dx.doi.org/10.2139/ssrn.3012169Card, David, and Stefano DellaVigna. 2020. What Do Editors Maximize? Evidence from Four Economics Journals. The Review of Economics and Statistics 102 (1): 195–217. https://doi.org/10.1162/rest_a_00839Teplitskiy, Misha, Hao Peng, Andrea Blasco, and Karim R. Lakhani. 2022. Is novel research worth doing? Evidence from peer review at 49 journals. Proceedings of the National Academy of Sciences 119 (47): e2118046119. https://doi.org/10.1073/pnas.2118046119

People rag on peer review a lot (including, occasionally, New Things Under the Sun). Yet it remains one of the most common ways to allocate scientific resources, whether those be R&D dollars or slots in journals. Is this all a mistake? Or does peer review help in its purported goal to identify the science most likely to have an impact and hence, perhaps most deserving of some of those limited scientific resources?A simple way to check is to compare peer review scores to other metrics of subsequent scientific impact; does peer review predict eventual impact?A number of studies find it does. This podcast is an audio read through of the (initial version of the) article What does peer review know?, originally published on New Things Under the Sun.Articles mentionedLi, Danielle, and Leila Agha. 2015. Big names or big ideas: Do peer-review panels select the best science proposals? Science 348(6233): 434-438. https://doi.org/10.1126/science.aaa0185Park, Hyunwoo, Jeongsik (Jay) Lee, and Byung-Cheol Kim. 2015. Project selection in NIH: A natural experiment from ARRA. Research Policy 44(6): 1145-1159. https://doi.org/10.1016/j.respol.2015.03.004.Card, David, and Stefano DellaVigna. 2020. What do Editors Maximize? Evidence from Four Economics Journals. The Review of Economics and Statistics 102(1): 195-217. https://doi.org/10.1162/rest_a_00839Siler, Kyle, Kirby Lee, and Lisa Bero. 2014. Measuring the effectiveness of scientific gatekeeping. PNAS 112(2): 360-365. https://doi.org/10.1073/pnas.1418218112Teplitskiy, Misha, and Von Bakanic. 2016. Do Peer Reviews Predict Impact? Evidence from the American Sociological Review, 1978 to 1982. Socius, 2. https://doi.org/10.1177/2378023116640278

A frequent worry is that our scientific institutions are risk-averse and shy away from funding transformative research projects that are high risk, in favor of relatively safe and incremental science. Why might that be?Let’s start with the assumption that high-risk, high-reward research proposals are polarizing: some people love them, some hate them. If this is true, and if our scientific institutions pay closer attention to bad reviews than good reviews, then that could be a driver of risk aversion. In this podcast, I look at three channels through which negative assessments may have outsized weight in decision-making, and how this might bias science away from transformative research.This podcast is an audio read through of the (initial version of the) article Biases Against Risky Research, originally published on New Things Under the Sun.Articles mentionedGross, Kevin, and Carl T. Bergstrom. 2021. Why ex post peer review encourages high-risk research while ex ante review discourages it. PNAS 118(51) e2111615118. https://doi.org/10.1073/pnas.2111615118Krieger, Joshua, and Ramana Nanda. 2022. Are Transformational Ideas Harder to Fund? Resource Allocation to R&D Projects at a Global Pharmaceutical Firm. Harvard Business School Working Paper 21-014. Jerrim, John, and Robert Vries. 2020. Are peer reviews of grant proposals reliable? An analysis of Economic and Social Research Council (ESRC) funding applications. The Social Science Journal 60(1): 91-109. https://doi.org/10.1080/03623319.2020.1728506Lane, Jacqueline N., Misha Teplitskiy, Gary Gray, Harder Ranu, Michael Menietti, Eva C. Guinan, and Karim R. Lakhani. 2022. Conservatism Gets Funded? A Field Experiment on the Role of Negative Information in Novel Project Evaluation. Management Science 68(6): 3975-4753. https://doi.org/10.1287/mnsc.2021.4107

Talent is spread equally over the planet, but opportunity is not. Today I want to look at some papers that try to quantify the costs to science and innovation from barriers to immigration. Specifically, let’s look at a set of papers on what happens to individuals with the potential to innovate when they immigrate versus when they do not. (See my post Importing Knowledge for some discussion on the impact of immigration on native scientists and inventors)This podcast is an audio read through of the (initial version of the) article Innovators Who Immigrate, originally published on New Things Under the Sun.Articles Mentioned:Agarwal, Ruchir and Patrick Gaule. 2020. Invisible Geniuses: Could the Knowledge Frontier Advance Faster? American Economic Review: Insights 2(4): 409-24. https://doi.org/10.1257/aeri.20190457Agarwal, Ruchir, Ina Ganguli, Patrick Gaule, and Geoff Smith. 2023. Why U.S. immigration matters for the global advancement of science. Research Policy 52(1): 104659. https://doi.org/10.1016/j.respol.2022.104659Gibson, John and David McKenzie. 2014. Scientific mobility and knowledge networks in high emigration countries: Evidence from the Pacific. Research Policy 43(9): 1486-1495. https://doi.org/10.1016/j.respol.2014.04.005Kahn, Shulamit, and Megan J. MacGarvie. 2016. How Important is U.S. Location for Research in Science? The Review of Economics and Statistics 98(2): 397-414. https://doi.org/10.1162/REST_a_00490Shi, Dongbo, Weichen Liu, and Yanbo Wang. 2023. Has China’s Young Thousand Talents Program been successful in recruiting and nurturing top-caliber scientists? Science 379(6627): 62-65. https://doi.org/10.1126/science.abq1218Prato, Marta. 2022. The Global Race for Talent: Brain Drain, Knowledge Transfer, and Growth. Job market paper. https://dx.doi.org/10.2139/ssrn.4287268

Are there some kinds of discoveries that are easier to make when young, and some that are easier to make when older?This podcast is an audio read through of the (initial version of the) article Age and the Nature of Innovation, originally published on New Things Under the Sun.Articles Mentioned:Yu, Huifeng, Gerald Marschke, Matthew B. Ross, Joseph Staudt and Bruce Weinberg. 2022. Publish or Perish: Selective Attrition as a Unifying Explanation for Patterns in Innovation over the Career. Journal of Human Resources 1219-10630R1. https://doi.org/10.3368/jhr.59.2.1219-10630R1Cui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging Scientists and Slowed Advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044Kalyani, Aakash. 2022. The Creativity Decline: Evidence from US Patents. Dissertation paper. https://www.aakashkalyani.comGalenson, David W. 2007. Old Masters and Young Geniuses: The Two Life Cycles of Artistic Creativity. Princeton University Press.Weinberg, Bruce A. and David W. Galenson. 2019. Creative Careers: The Life Cycles of Nobel laureates in Economics. De Economist 167: 221-239. https://doi.org/10.1007/s10645-019-09339-9Jones, Benjamin F., and Bruce A. Weinberg. 2011. Age Dynamics in Scientific Creativity. PNAS 108(47): 18910-18914. https://doi.org/10.1073/pnas.1102895108Jones, Benjamin F., E.J. Reedy, and Bruce A. Weinberg. 2014. Age and Scientific Genius. NBER Working Paper 19866. https://doi.org/10.3386/w19866Kaltenberg, Mary, Adam B. Jaffe, and Margie E. Lachman. 2021. Invention and the Life Course: Age Differences in Patenting. NBER Working Paper 28769. https://doi.org/10.3386/w28769

Scientists are getting older. Is this a problem? What’s the relationship between age and innovation?This podcast is an audio read through of the (initial version of the) article Age and the Impact of Innovations, originally published on New Things Under the Sun.Articles MentionedCui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging Scientists and Slowed Advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044Jones, Benjamin, E.J. Reedy, and Bruce A. Weinberg. 2014. Age and Scientific Genius. NBER Working Paper 19866. https://doi.org/10.3386/w19866Yu, Huifeng, Gerald Marschke, Matthew B. Ross, Joseph Staudt and Bruce Weinberg. 2022. Publish or Perish: Selective Attrition as a Unifying Explanation for Patterns in Innovation over the Career. Journal of Human Resources 1219-10630R1. https://doi.org/10.3368/jhr.59.2.1219-10630R1Wu, L., Wang, D. & Evans, J.A. Large teams develop and small teams disrupt science and technology. Nature 566, 378–382 (2019). https://doi.org/10.1038/s41586-019-0941-9Kaltenberg, Mary, Adam B. Jaffe, and Margie E. Lachman. 2021. Invention and the Life Course: Age Differences in Patenting. NBER Working Paper 28769. https://doi.org/10.3386/w28769Liu, Lu, Yang Wang, Roberta Sinatra, C. Lee Giles, Chaoming Song, and Dan Wang. 2018. Hot streaks in artistic, cultural, and scientific careers. Nature 559: 396-399. https://doi.org/10.1038/s41586-018-0315-8

Suppose in some parallel universe history proceeded down a quite different path from our own, shortly after Homo sapiens evolved. If we fast forward to 2022 of that universe, how different would the technological stratum of that parallel universe be from our own? Would they have invented the wheel? Steam engines? Railroads? Cars? Computers? Internet? Social media? Or would their technologies rely on principles entirely alien to us? In other words, once humans find themselves in a place where technological improvement is the rule (hardly a given!), is the form of the technology they create inevitable? Or is it the stuff of contingency and accident?In academic lingo, this is a question about path dependency. How much path dependency is there in technology?This week's podcast is a bit unusual. I designed New Things Under the Sun to feature two kinds of articles: claims and arguments. Almost everything I write (and podcast) is a claim article. Today’s podcast is the other kind of thing, an argument. The usual goal of a claim article is to synthesize several academic papers in service of assessing a specific narrow claim about innovation. Argument articles live one level up the chain of abstraction: the goal is to synthesize many claim articles (referenced mostly in footnotes) in service of presenting a bigger picture argument. That means in this podcast you won’t hear me talk much about specific papers; instead, I’ll talk about various literatures and how I think they interact with each other.This podcast is an audio read through of the (initial version of the) article Are technologies inevitable?, originally published on New Things Under the Sun.

Remote work seems to be well suited for some kinds of knowledge work, but it’s less clear that it’s well suited for the kind of collaborative creativity that results in breakthrough innovations. A series of new papers suggests breakthrough innovation by distributed teams has traditionally been quite difficult, but also that things have changed, possibly dramatically, as remote collaboration technology has improved.This podcast is an audio read through of the (initial draft of the) post Remote Breakthroughs, originally published on New Things Under the Sun.Articles MentionedVan der Wouden, Frank. 2020. A history of collaboration in US invention: changing patters of co-invention, complexity and geography. Industrial and Corporate Change 29(3): 599-619. https://doi.org/10.1093/icc/dtz058Lin, Yiling, Carl Benedikt Frey, and Lingfei Wu. 2022. Remote collaboration fuses fewer breakthrough ideas. arXiv:2206.01878. https://doi.org/10.48550/arXiv.2206.01878Lin, Yiling, James A. Evans, and Lingfei Wu. 2022. New directions in science emerge from disconnection and discord. Journal of Informetrics 16(1): 101234. https://doi.org/10.1016/j.joi.2021.101234Berkes, Enrico, and Ruben Gaetani. 2021. The Geography of Unconventional Innovation. The Economic Journal131(636): 1466-1514. https://doi.org/10.1093/ej/ueaa111Duede, Eamon, Misha Teplitskiy, Karim Lakhani, and James Evans. 2021. Being Together in Place as a Catalyst for Scientific Advance. arXiv:2107.04165. https://doi.org/10.48550/arXiv.2107.04165Frey, Carl Benedikt, and Giorgio Presidente. 2022. Disrupting Science. Working Paper.Esposito, Christopher. 2021. The Geography of Breakthrough Innovation in the United States over the 20th Century. Papers in Evolutionary Economic Geography 2126. Working paper.

These are weird times. On the one hand, scientific and technological progress seem to be getting harder. Add to that slowing population growth, and it’s possible economic growth over the next century or two might slow to a halt. On the other hand, one area where we seem to be observing rapid technological progress is in artificial intelligence. If that goes far enough, it’s easy to imagine machines being able to do all the things human inventors and scientists do, possibly better than us. That would seem to pull in the opposite direction, leading to accelerating and possibly unbounded growth; a singularity.Are those the only options? Is there a middle way? Under what conditions? This is an area where some economic theory can be illuminating. This article is bit unusual for New Things Under the Sun in that I am going to focus on a small but I think important part of a single 2019 article: “Artificial Intelligence and Economic Growth” by Aghion, Jones, and Jones. There are other papers on what happens to growth if we can automate parts of economic activity,undefined but Aghion, Jones, and Jones (2019) is useful because (among other things) it focuses on what happens in economic growth models if we automate the process of invention itself.This podcast is an audio read through of the (initial draft of the) post What if we could automate invention?, originally published on New Things Under the Sun.Articles MentionedAghion, Philippe, Benjamin F. Jones, and Charles I. Jones. 2019. Artificial Intelligence and Economic Growth. In The Economics of Artificial Intelligence: An Agenda, ed. Ajay Agrawal, Joshua Gans, and Avi Goldfarb. National Bureau of Economic Research. ISBN 978-0-226-61333-8

For decades, the office was the default way to organize workers, but that default is being re-examined. Many workers (including me) prefer working remotely, and seem to be at least as productive working remotely as they are in the office. Remote capable organizations can hire from a bigger pool of workers than is available locally. All in all, remote work seems to have been underrated, relative to just a few years ago.But there are tradeoffs. I’ve written before that physical proximity seems to be important for building new relationships, even though those relationships seem to remain productive as people move away from each other. This podcast narrows the focus down to the office. Does bringing people together in the office actually facilitate meeting new people? (spoiler: yes) But I’ll try and get more specific about how, when, and why this happens too.This podcast is an audio read through of the (initial draft of the) post Innovation at the Office, originally published on New Things Under the Sun.Articles Mentioned:Allen, Thomas and Gunter Henn. 2007. The Organization and Architecture of Innovation. Routledge Publishing. Link.Miranda, Arianna Salazar and Matthew Claudel. 2021. Spatial proximity matters: A study on collaboration. PLoS ONE 16(12): e0259965. https://doi.org/10.1371/journal.pone.0259965Catalini, Christian. 2017. Microgeography and the Direction of Inventive Activity. Management Science 64(9) https://doi.org/10.1287/mnsc.2017.2798Roche, Maria P., Alexander Oettl, and Christian Catalini. 2022. (Co-)Working in Close Proximity: Knowledge Spillovers and Social Interactions. NBER Working Paper 30120. https://doi.org/10.3386/w30120Hasan, Sharique, and Rembrand Koning. 2019. Prior ties and the limits of peer effects on startup team performance. Strategic Management Journal 40(9): 1394-1416. https://doi.org/10.1002/smj.3032Appel-Meulenbroek, Rianne, Bauke de Vries, and Mathieu Weggeman. 2017. Knowledge Sharing Behavior: The Role of Spatial Design in Buildings. Environment and Behavior 49(8): 874-903. https://doi.org/10.1177/0013916516673405Kabo, Felichism W., Natalie Cotton-Nessler, Yongha Hwang, Margaret C. Levenstein, and Jason Owen-Smith. 2014. Proximity effects on the dynamics and outcomes of scientific collaborations. Research Policy 43(9): 1469-1485. https://doi.org/10.1016/j.respol.2014.04.007

A huge quantity of academic research that seeks to understand how science works relies on citation counts to measure the value of knowledge created by scientists. This measure of scientific impact is so deeply embedded in the literature that it's absolutely crucial to know if it’s reliable. So today I want to look at a few recent articles that look into this foundational question: are citation counts a good measure of the value of scientific contributions?This podcast is an audio read through of the (initial draft of the) post Do Academic Citations Measure the Impact of New Ideas?, originally published on New Things Under the Sun.Articles Mentioned:Teplitsky, Misha, Eamon Duede, Michael Menietti, and Karim R. Lakhani. 2022. How Status of Research Papers Affects the Way They are Read and Cited. Research Policy 51(4): 104484. https://doi.org/10.1016/j.respol.2022.104484Gerrish, Sean M., and David M. Blei. 2010. A Language-based Approach to Measuring Scholarly Impact. Proceedings of the 26th International Conference on Machine Learning: 375-382. http://www.cs.columbia.edu/~blei/papers/GerrishBlei2010.pdfGerow, Aaron, Yuenig Hu, Jordan Boyd-Graber, and James Evans. 2018. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academy of Science 115(13): 3308-3313. https://doi.org/10.1073/pnas.1719792115Poege, Felix, Dietmar Harhoff, Fabian Guesser, and Stefano Baruffaldi. 2019. Science Quality and the Value of Inventions. Science Advances 5(12). https://doi.org/10.1126/sciadv.aay7323Yin, Yian, Yuxiao Dong, Kuansan Wang, Dashun Wang, and Benjamin Jones. 2021. Science as a Public Good: Public Use and Funding of Science. NBER Working Paper 28748. https://doi.org/10.3386/w28748Card, David, and Stefano DellaVigna. 2020. What do Editors Maximize? Evidence from Four Economics Journals. The Review of Economics and Statistics 102(1): 195-217. https://doi.org/10.1162/rest_a_00839Tahamtan, Iman, and Lutz Bornmann. 2019. What do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published Between 2006 and 2018. Scientometrics 121: 1635-1684. https://doi.org/10.1007/s11192-019-03243-4Kousha, Kayvan, and Mike Thelwell. 2016. Are Wikipedia Citations Important Evidence of the Impact of Scholarly Articles and Books? Journal of the Association for Information Science and Technology 68(3): 762-779. https://doi.org/10.1002/asi.23694

An old divide in the study of innovation is whether ideas come primarily from individual/group creativity, or whether they are “in the air”, so that anyone with the right set of background knowledge will be able to see them. In this episode, I look at how much redundancy there is in innovation: if the discoverer of some idea had failed to find it, would someone else have figured it out later?This podcast is an audio read through of the (initial draft of the) post How common is independent discovery?, originally published on New Things Under the Sun.Articles Mentioned:Ogburn, William F., and Dorothy Thomas. 1922. Are Inventions Inevitable? A Note on Social Evolution. Political Science Quarterly 37(1): 83-98. https://www.jstor.org/stable/2142320Haagstrom, Warren O. 1974. Competition in Science. American Sociological Review 39(1): 1-18. https://doi.org/10.2307/2094272Hill, Ryan, and Carolyn Stein. 2020. Scooped! Estimating Rewards for Priority in Science. Working Paper.Painter, Deryc T., Frank van der Wouden, Manfred D. Laubichler, and Hyejin Youn. 2020. Quantifying simultaneous innovations in evolutionary medicine. Theory in Biosciences 139: 319-335. https://doi.org/10.1007/s12064-020-00333-3Bikard, Michaël. 2020. Idea Twins: Simultaneous discoveries as a research tool. Strategic Management Journal 41(8): 1528-1543. https://doi.org/10.1002/smj.3162Ganguli, Ina, Jeffrey Lin, and Nicholas Reynolds. 2020. The Paper Trail of Knowledge Spillovers: Evidence from Patent Interferences. American Economic Journal: Applied Economics 12(2): 278-302. https://doi.org/10.1257/app.20180017Lück, Sonja, Benjamin Balmier, Florian Seliger, and Lee Fleming. 2020. Early Disclosure of Invention and Reduced Duplication: An Empirical Test. Management Science 66(6): 2677-2685.  https://doi.org/10.1287/mnsc.2019.3521Iaria, Alessandro, Carlo Schwarz, and Fabian Waldinger. 2018. Frontier Knowledge and Scientific Production: Evidence from the Collapse of International Science. Quarterly Journal of Economics: 927-991. https://doi.org/10.1093/qje/qjx046Borjas, George J., and Kirk B. Doran. 2012. The Collapse of the Soviet Union and the Productivity of American Mathematicians. The Quarterly Journal of Economics 127(3): 1143-1203. https://doi.org/10.1093/qje/qjs015Hill, Ryan, and Carolyn Stein. 2021. Race to the bottom: competition and quality in science. Working paper.Cotropia, Christopher Anthony, and David L. Schwartz. 2018. Patents Used in Patent Office Rejections as Indicators of Value. SSRN Working Paper https://dx.doi.org/10.2139/ssrn.3274995

A basket of indicators all seem to document a similar trend. Even as the number of scientists and publications rises substantially, we do not appear to be seeing a concomitant rise in new discoveries that supplant older ones. Science is getting harder.This podcast is an audio read through of the (initial draft of the) post Science is getting harder, published on New Things Under the Sun.Articles mentioned:Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. Are Ideas Getting Harder to Find? American Economics Review 110(4): 1104-1144. https://doi.org/10.1257/aer.20180338Wang, Dashun and Albert-László Barabási. 2021. The Science of Science. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108610834Li, Jichao, Yian Yin, Santo Fortunato, and Dashun Wang. 2019. A dataset of publication records for Nobel Laureates. Scientific Data 6: 33. https://doi.org/10.1038/s41597-019-0033-6Collison, Patrick and Michael Nielsen. 2018. Science is Getting Less Bang for Its Buck. The Atlantic. Chu, Johan S.G. and James A. Evans. 2021. Slowed canonical progress in large fields of science. PNAS 118(41): e2021636118. https://doi.org/10.1073/pnas.2021636118Milojević, Staša. 2015. Quantifying the cognitive extent of science. Journal of Informetrics 9(4): 962-973. https://doi.org/10.1016/j.joi.2015.10.005Carayol, Nicolas, Agenor Lahatte, and Oscar Llopis. 2019. The Right Job and the Job Right: Novelty, Impact and Journal Stratification in Science. SSRN working paper. http://dx.doi.org/10.2139/ssrn.3347326Larivière, Vincent, Éric Archambault, & Yves Gingras. 2007. Long-term patterns in the aging of the scientific literature, 1900–2004. Proceedings of ISSI 2007, ed. Daniel Torres-Salinas and Henk F. Moed. https://www.issi-society.org/publications/issi-conference-proceedings/proceedings-of-issi-2007/Cui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging scientists and slowed advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044Marx, Matt, and Aaron Fuegi. Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686