See also Intellectual Property.
I am especially interested in modeling how technology changes the rules of the game, as opposed to marginally changes some parameters; not, say residual stochastic shocks (in the “Real Business Cycle” models), or as the slope of a marginal cost of production curve (in textbook microeconomics). That is, technological innovation that leads to a qualitative, rather than incremental, change in the state of play – respecting that a lot of marginal changes might in fact lead to major qualitative changes.
In recognition of that emphasis, I briefly called this entry “disruptive technology” instead of mere “innovation”, but then I felt like a TED speaker and woke up sweating in the night to change it.
This is at the very limit of modelability, surely?. The introduction of a new technology has many components, from social uptake, to supply chains, to the discovery process. The unexpected interactions with the other technologies out there. The internal combustion engine changed more than just transit times. The computer network altered more than just mail delivery times.
The cascade of effects from any one alteration is, it is likely, unknowable in advance, but might have some regularities, or at least some kind of underlying set of distributions as a stochastic process – some kind of branching process perhaps? Fixation processes, by analogy with evolutionary theory?
Where did the industrial revolution come from?
Gregory Clark and Julia Galef in podcast conversation: What caused the industrial revolution?:
the timing in 1770 in Britain makes it very, very difficult to explain the industrial revolution. The reason for that is that Britain at that time was institutionally a very stable society, and essentially had very little institutional change in the previous 80 years. When you’re trying to explain this event, it’s occurring against the kind of unchanged background of a society… with stable institutions. Very small government that mainly exists to fight more abroad. You have very stable wages within the society, they’re really not changing, the cost of capital was not changing. [..] It’s an economic environment which just looks very flat. Suddenly, in the middle of all of this, you’ve got this transforming event occurring.
“Product space” model
Due originally to Hidalgo and Hausmann, and made purportedly more rigorous by Caldarelli et al.
Considers products and nations in a bipartite graph, and does various network statistics upon it.
Attempts to be predictive about the “natural level” of a country’s GDP.
(c.f. Felix Reed-Tsochas’ affinity for such graphs, har har) Note that there is an implicit third part in the graph, to whit “capabilities”, which represent infrastructure to manufacture products.
Frank Schweitzer et al have a similar notion of inter-firm R&D networks which may be related? See references.
random idea: Estimating number of SKUs as a surrogate for divisions of a modern economy a la Beinhocker (lots of research into this because of Long Tail theories, though the primary data is rarely included – might chase this.)
Should mention this, despite nonverifiability etc.
Marginal returns on research
See Kevin Bryan’s paper reviews, especially Models of Innovation I: The Patent Race has a lot of papers discussed which I should file one day > I’ve been going through some old literature on innovation again as part of a current project, so I figured I ought put up a little review of this literature. I’ll cover five strands: the patent race, the partial equilibrium/auction, the quality ladder, sequential innovation a la Scotchmer and Green, and bandit experimentation.
Moore’s law versus Eroom’s law governing trends in marginal research productivity. What does the paucity of new drugs mean? Is this the same as the problem in science? The difficulties here form the basis of François Chollet’s arguments against the likelihood of a hard AI singularity.
Do technological improvements primarily result in lower prices for consumers or in higher profits for producers? If producers are able to capture (or appropriate) most of the social returns to innovation, then profits will rise and prices will fall relatively little.
How much of the profits from a new technology are captured by innovators will vary greatly across industries. For sectors where knowledge is in the public domain, such as weather forecasting, the new knowledge cannot be appropriated and productivity improvements are passed on in lower prices. In other industries with well-defined products and strong patents, such as pharmaceuticals, producers may be successful in capturing a large fraction of social gains in “Schumpeterian profits.”
Other interesting things to look at
So Loreto, Strogatz, and co have modified Polya’s urn model to account for the possibility that discovering a new color in the urn can trigger entirely unexpected consequences. They call this model “Polya’s urn with innovation triggering.” [..they] then calculate how the number of new colors picked from the urn, and their frequency distribution, changes over time. The result is that the model reproduces Heaps’ and Zipf’s Laws as they appear in the real world
Phillip Ball on What innovation really is
Mariana Mazzucato seems to be interesting
Would a journal of infrastructure complexity be any good?
Source of the alarming graphic above.
Is it just me, or does this resemble a maximal statistic, or perhaps a rarefaction curve?
Thiel, P. A. (2014). Zero to one: notes on startups, or how to build the future (First edition.). New York: Crown Business.
“Thiel begins with the contrarian premise that we live in an age of technological stagnation, even if we’re too distracted by shiny mobile devices to notice. Information technology has improved rapidly, but there is no reason why progress should be limited to computers or Silicon Valley. Progress can be achieved in any industry or area of business. It comes from the most important skill that every leader must master: learning to think for yourself.
Doing what someone else already knows how to do takes the world from 1 to n, adding more of something familiar. But when you do something new, you go from 0 to 1. The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. Tomorrow’s champions will not win by competing ruthlessly in today’s marketplace. They will escape competition altogether, because their businesses will be unique.”
I gather this is re-introducing Austrian economics to the silicon valley age. The sting will be in the policy prescriptions.
The vexing Sam Kriss is back, being glum and hyperbolically amusing as always. The Long, Slow, Rotten March of Progress:
Desperation is everywhere; exhibitors make lunging grabs for any passers-by wearing an “INVESTOR” lanyard, proffer stickers and goodies, scream for attention on their convention-standard signs. These do not, to put it kindly, make a lot of sense. “Giving you all the tools you need to activate and manage your influencer marketing relationships,” promises one. “Leverage what is known to find, manage, and understand your data,” entices another. The gleaming technological future looks a lot like a new golden age of hucksterism. It’s networking; the sordid, stupid business of business; pressing palms with arrogant pricks, genuflecting to idiots, entirely unchanged by the fact that this time it’s about apps and code rather than dog food or dishwashers.
None of these start-ups are doing anything new or interesting. Which shouldn’t be surprising: how often does anyone have a really good idea? What you actually get is just code, sloshing around, congealing into apps and firms that exist simply to exist. Uber for dogs, GrubHub for clothes, Patreon for sex, Slack for death, PayPal for God, WhatsApp for the spaceless non-void into which a blind universe expands.
Here is a methodooligcally quirky paper: Brian Castellani and Rajeev Rajaram, How large must a population be to accomplish great things? A question for social complexity scholars.
Aghion, Philippe, Nicholas Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt. 2002. “Competition and Innovation: An Inverted U Relationship.” Working Paper 9269. National Bureau of Economic Research. http://eprints.ucl.ac.uk/2962/1/2962.pdf.
Aghion, Philippe, Christopher Harris, Peter Howitt, and John Vickers. 2001. “Competition, Imitation and Growth with Step-by-Step Innovation.” The Review of Economic Studies 68 (3): 467–92. https://doi.org/10.1111/1467-937X.00177.
Arbesman, Samuel, and Nicholas A Christakis. 2011. “Eurekometrics: Analyzing the Nature of Discovery.” PLoS Comput Biol 7 (6): –1002072. https://doi.org/10.1371/journal.pcbi.1002072.
Arbilly, Michal, and Kevin N. Laland. 2017. “The Magnitude of Innovation and Its Evolution in Social Animals.” Proceedings of the Royal Society B: Biological Sciences 284 (1848). https://doi.org/10.1098/rspb.2016.2385.
Arthur, W. Brian. 1989. “Competing Technologies, Increasing Returns, and Lock-in by Historical Events.” The Economic Journal 99 (394): 116–31. https://doi.org/10.2307/2234208.
Arthur, W Brian. 2007. “The Structure of Invention.” Research Policy 36 (2): 274–87. https://doi.org/10.1016/j.respol.2006.11.005.
Baber, Zaheer. 2010. “Society: The Rise of the ’Technium’.” Nature 468 (7322): 372. https://doi.org/10.1038/468372a.
Behrens, Arno, Stefan Giljum, Jan Kovanda, and Samuel Niza. 2007. “The Material Basis of the Global Economy: Worldwide Patterns of Natural Resource Extraction and Their Implications for Sustainable Resource Use Policies.” Ecological Economics, Special Section - Ecosystem Services and Agriculture Ecosystem Services and Agriculture, 64 (2): 444–53. https://doi.org/10.1016/j.ecolecon.2007.02.034.
Beinhocker, Eric D. 2011. “Evolution as Computation: Integrating Self-Organization with Generalized Darwinism.” Journal of Institutional Economics 7 (Special Issue 03): 393–423. https://doi.org/10.1017/S1744137411000257.
Beinhocker, Eric D. 2007. Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics. Harvard Business Press.
Bhattacharya, Jay, and Mikko Packalen. 2020. “Stagnation and Scientific Incentives.” Working Paper 26752. National Bureau of Economic Research. https://doi.org/10.3386/w26752.
Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2016. “Are Ideas Getting Harder to Find?” Manuscript, Stanford University, Palo Alto. http://www-leland.stanford.edu/~chadj/IdeaPF.pdf.
Castellani, Brian, and Rajeev Rajaram. n.d. “How Large Must a Population Be to Accomplish Great Things?” 15. https://www.art-sciencefactory.com/population&creativity.pdf.
Cristelli, Matthieu, Andrea Tacchella, and Luciano Pietronero. 2015. “The Heterogeneous Dynamics of Economic Complexity.” PLoS ONE 10 (2): e0117174. https://doi.org/10.1371/journal.pone.0117174.
David, Paul A. 1985. “Clio and the Economics of QWERTY.” The American Economic Review 75 (2): 332–37.
Filimonov, Vladimir, David Bicchetti, Nicolas Maystre, and Didier Sornette. 2014. “Quantification of the High Level of Endogeneity and of Structural Regime Shifts in Commodity Markets.” Journal of International Money and Finance, Understanding International Commodity Price Fluctuations, 42 (April): 174–92. https://doi.org/10.1016/j.jimonfin.2013.08.010.
Filimonov, Vladimir, and Didier Sornette. 2012. “Quantifying Reflexivity in Financial Markets: Toward a Prediction of Flash Crashes.” Physical Review E 85 (5): 056108. https://doi.org/10.1103/PhysRevE.85.056108.
Filimonov, V., and D. Sornette. 2013. “A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model.” Physica A: Statistical Mechanics and Its Applications 392 (17): 3698–3707. https://doi.org/10.1016/j.physa.2013.04.012.
Frenken, Koen. 2006. Innovation, Evolution and Complexity Theory. Edward Elgar Publishing.
Funtowicz, Silvio O, and Jerome R Ravetz. 1994. “The Worth of a Songbird: Ecological Economics as a Post-Normal Science.” Ecological Economics 10: 197–207. https://doi.org/10.1016/0921-8009(94)90108-2.
Goldenberg, Jacob, Barak Libai, Yoram Louzoun, David Mazursky, and Sorin Solomon. 2004. “Inevitably Reborn: The Reawakening of Extinct Innovations.” Technological Forecasting and Social Change 71 (9): 881–96. https://doi.org/10.1016/j.techfore.2003.09.005.
Hammond, Allen, Albert Adriaanse, Stefan Bringzeu, Yuichi Moriguchi, Eric Rodenburg, Donald Rogich, and Helmut Schütz. 1997. Resource Flows: The Material Basis of Industrial Economies. World Resources Institute Washington, DC.
Hawken, Paul, Amory Lovins, and L Hunter Lovins. 2000. Natural Capitalism: Creating the Next Industrial Revolution. Back Bay Books.
Iribarren, José Luis, and Esteban Moro. 2011. “Branching Dynamics of Viral Information Spreading.” Physical Review E 84 (4): 046116. https://doi.org/10.1103/PhysRevE.84.046116.
Jackson, Matthew O. 2008. Social and Economic Networks. Princeton University Press.
Kali, Raja, Javier Reyes, Joshua McGee, and Stuart Shirrell. 2013. “Growth Networks.” Journal of Development Economics 101 (March): 216–27. https://doi.org/10.1016/j.jdeveco.2012.11.004.
König, Michael D., S. Battiston, M. Napoletano, and F. Schweitzer. 2011. “Recombinant Knowledge and the Evolution of Innovation Networks.” Journal of Economic Behavior & Organization 79 (3): 145–64. https://doi.org/10.1016/j.jebo.2011.01.007.
König, Michael D., Stefano Battiston, Mauro Napoletano, and Frank Schweitzer. 2012. “The Efficiency and Stability of R&D Networks.” Games and Economic Behavior 75 (2): 694–713. https://doi.org/10.1016/j.geb.2011.12.007.
Lane, David A, and Robert R Maxfield. 2005. “Ontological Uncertainty and Innovation.” Journal of Evolutionary Economics 15: 3–50. https://doi.org/10.1007/s00191-004-0227-7.
Loreto, Vittorio, Vito D. P. Servedio, Steven H. Strogatz, and Francesca Tria. 2016. “Dynamics on Expanding Spaces: Modeling the Emergence of Novelties.” In Creativity and Universality in Language, edited by Mirko Degli Esposti, Eduardo G. Altmann, and François Pachet, 59–83. Lecture Notes in Morphogenesis. Springer International Publishing. https://doi.org/10.1007/978-3-319-24403-7_5.
Moussaïd, Mehdi, Juliane E. Kämmer, Pantelis P. Analytis, and Hansjörg Neth. 2013. “Social Influence and the Collective Dynamics of Opinion Formation.” PLoS ONE 8 (11): e78433. https://doi.org/10.1371/journal.pone.0078433.
Nelson, Richard R, and Sidney G Winter. 2002. “Evolutionary Theorizing in Economics.” The Journal of Economic Perspectives 16: 23–46. https://doi.org/10.2307/2696495.
Nordhaus, William D. 2005. “Schumpeterian Profits and the Alchemist Fallacy.” SSRN Scholarly Paper ID 820309. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=820309.
Nowak, Martin A, and DAvid C Krakauer. 1999. “The Evolution of Language.” Proceedings of the National Academy of Sciences of the United States of America 96 (14): 8028.
Ollhoff, Jim, and Michael Walcheski. 2002. Stepping in Wholes: Introduction to Complex Systems. Sparrow Media Group.
Onnela, J P, and Felix Reed-Tsochas. 2010. “Spontaneous Emergence of Social Influence in Online Systems.” Proceedings of the National Academy of Sciences 107 (43): 18375–80. https://doi.org/10.1073/pnas.0914572107.
Ormerod, Paul, and R Alexander Bentley. 2010. “Modelling Creative Innovation.” Cultural Science 3 (1).
Ormerod, Paul, and Rich Colbaugh. 2006. “Cascades of Failure and Extinction in Evolving Complex Systems.” Journal of Artificial Societies and Social Simulation 9.
Pezzey, John C V, and John M Anderies. 2003. “The Effect of Subsistence on Collapse and Institutional Adaptation in Population-Resource Societies.” Journal of Development Economics 72: 299–320. https://doi.org/10.1016/S0304-3878(03)00078-6.
Repenning, Nelson P. 2002. “A Simulation-Based Approach to Understanding the Dynamics of Innovation Implementation.” Organization Science 13.
Rivkin, Jan W. 2001. “Reproducing Knowledge: Replication Without Imitation at Moderate Complexity.” Organization Science, 274–93.
Rizzo Jr, Mario J. 1996. The Economics of Time and Ignorance: With a New Introduction. Routledge.
Rosewell, Bridget, and Paul Ormerod. 2004. “How Much Can Firms Know?” Computing in Economics and Finance 2004. https://ideas.repec.org/p/sce/scecf4/44.html.
Rosicky, Anton’ın. 2001. “Information and Social Systems Evolution.” In.
Scannell, Jack W., Alex Blanckley, Helen Boldon, and Brian Warrington. 2012. “Diagnosing the Decline in Pharmaceutical R&D Efficiency.” Nature Reviews Drug Discovery 11 (3): 191–200. https://doi.org/10.1038/nrd3681.
Schweitzer, Frank, Giorgio Fagiolo, Didier Sornette, Fernando Vega-Redondo, Alessandro Vespignani, and Douglas R White. 2009. “Economic Networks: The New Challenges.” Science 325 (5939): 422–25. https://doi.org/10.1126/science.1173644.
Serafinelli, Michel, and Guido Tabellini. 2017. “Creativity over Time and Space.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3070203.
———. 2018. “Creativity and Freedom.” VoxEU.org. January 6, 2018. https://voxeu.org/article/creativity-and-freedom.
Solé, Ricard V, Bernat Corominas-Murtra, Sergi Valverde, and Luc Steels. 2010. “Language Networks: Their Structure, Function, and Evolution.” Complexity 15: 20–26. https://doi.org/10.1002/cplx.20305.
Solé, Ricard V., Sergi Valverde, Marti Rosas Casals, Stuart A. Kauffman, Doyne Farmer, and Niles Eldredge. 2013. “The Evolutionary Ecology of Technological Innovations.” Complexity 18 (4): 15–27. https://doi.org/10.1002/cplx.21436.
Sood, Vishal, Myléne Mathieu, Amer Shreim, Peter Grassberger, and Maya Paczuski. 2010. “Interacting Branching Process as a Simple Model of Innovation.” Physical Review Letters 105 (17): 178701. https://doi.org/10.1103/PhysRevLett.105.178701.
Spranzi, Marta. 2004. “Galileo and the Mountains of the Moon: Analogical Reasoning, Models and Metaphors in Scientific Discovery.” Journal of Cognition and Culture 4 (3): 451–83. https://doi.org/10.1163/1568537042484904.
Stadler, Bärbel M R, Peter F Stadler, Günter P Wagner, and Walter Fontana. 2001. “The Topology of the Possible: Formal Spaces Underlying Patterns of Evolutionary Change.” Journal of Theoretical Biology 213 (2): 241–74. https://doi.org/10.1006/jtbi.2001.2423.
Stebbing, A R D. 2006. “Genetic Parsimony: A Factor in the Evolution of Complexity, Order and Emergence.” Biological Journal of the Linnean Society, 88: 295. https://doi.org/10.1111/j.1095-8312.2006.00652.x.
Sterman, John D. 2000. Business Dynamics. McGraw Hill Higher Education.
Straatman, Bas, Roger White, and Wolfgang Banzhaf. 2008. “An Artificial Chemistry-Based Model of Economies.” Artificial Life 11: 592.
Sutton, John. 2001. Technology and Market Structure: Theory and History. The MIT Press.
Tacchella, Andrea, Matthieu Cristelli, Guido Caldarelli, Andrea Gabrielli, and Luciano Pietronero. 2012. “A New Metrics for Countries’ Fitness and Products’ Complexity.” Scientific Reports 2 (October). https://doi.org/10.1038/srep00723.
Tainter, Joseph A. 1995. “Sustainability of Complex Societies.” Futures 27: 397–407. https://doi.org/10.1016/0016-3287(95)00016-P.
Thiel, Peter A. 2014. Zero to One: Notes on Startups, or How to Build the Future. First edition. New York: Crown Business.
Thorngate, Warrem, Jing Liu, and Wahida Chowdhury. 2011. “The Competition for Attention and the Evolution of Science.” Journal of Artificial Societies and Social Simulation 14 (4): 17.
Tomasello, Mario Vincenzo, Mauro Napoletano, Antonios Garas, and Frank Schweitzer. 2013. “The Rise and Fall of R&D Networks,” April. http://arxiv.org/abs/1304.3623.
Tomasello, Mario V., Nicola Perra, Claudio J. Tessone, Márton Karsai, and Frank Schweitzer. 2014. “The Role of Endogenous and Exogenous Mechanisms in the Formation of R&D Networks.” Scientific Reports 4 (July). https://doi.org/10.1038/srep05679.
Topolinski, Sascha, and Rolf Reber. 2010. “Gaining Insight into the ?Aha? Experience.” Current Directions in Psychological Science 19: 402–5. https://doi.org/10.1177/0963721410388803.
Tria, F., V. Loreto, V. D. P. Servedio, and S. H. Strogatz. 2013. “The Dynamics of Correlated Novelties” 4 (October). https://doi.org/10.1038/srep05890.
Valverde, Sergi, Ricard V Solé, Mark A Bedau, and Norman H Packard. 2007. “Topology and Evolution of Technology Innovation Networks.” Phys. Rev. E 76 (5): 056118. https://doi.org/10.1103/PhysRevE.76.056118.
Vitali, Stefania, James B. Glattfelder, and Stefano Battiston. 2011. “The Network of Global Corporate Control.” PLoS ONE 6 (10): e25995. https://doi.org/10.1371/journal.pone.0025995.
Young, H Peyton. 1998. Individual Strategy and Social Structure : An Evolutionary Theory of Institutions. Princeton, N.J.: Princeton University Press. http://www.loc.gov/catdir/toc/prin031/97041419.html.
———. 2002. “The Diffusion of Innovations in Social Networks.”
———. 2005. “The Spread of Innovations Through Social Learning.” http://www.brookings.edu/research/reports/2005/12/agentbehavior.
Zabell, S. L. 1992. “Predicting the Unpredictable.” Synthese 90 (2): 205–32. https://doi.org/10.1007/BF00485351.