The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models
By Luís M. A. Bettencourt, A. Cintrón-Arias, David I. Kaiser, Carlos Castillo-Ch'avez. 2005
461 citations recorded by the source corpus.
The population dynamics underlying the diffusion of ideas hold many qualitative similarities to those involved in the spread of infections. In spite of much suggestive evidence this analogy is hardly ever quantified in useful ways. The standard benefit of modeling epidemics is the ability to estimate quantitatively population average parameters, such as interpersonal contact rates, incubation times, duration of infectious periods, etc. In most cases such quantities generalize naturally to the spread of ideas and provide a simple means of quantifying sociological and behavioral patterns. Here we apply several paradigmatic models of epidemics to empirical data on the advent and spread of Feynman diagrams through the theoretical physics communities of the USA, Japan, and the USSR in the period immediately after World War II. This test case has the advantage of having been studied historically in great detail, which allows validation of our results. We estimate the effectiveness of adoption of the idea in the three communities and find values for parameters reflecting both intentional social organization and long lifetimes for the idea. These features are probably general characteristics of the spread of ideas, but not of common epidemics.
Subfields: Genetics, Modeling and Simulation, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/6ab8c1ee082095448d03fcf6ce8b6a20d34c233b
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Citations in the corpus
- What Stops Social Epidemics? (2011, background): “Many diverse phenomena can be modeled as contact processes, including adoption of new ideas (Rogers 2003; Bettencourt et al. 2005), spread of infectious disease (Anderson and May 1991; Hethcote 2000) and behaviors (Christakis and Fowler 2007; Christakis and Fowler 2008), computer virus epidemics on…”
- The science of science: from the perspective of complex systems (2017, methodology): “[440] add E (idea incubators) and Z (skeptics) to the population model to quantify the spread of Feynman diagrams throughout the theoretical physics community.”
- The "Majority Illusion" in Social Networks (2015, background): “…Majority Illusion in Social Networks Kristina Lerman, Xiaoran Yan, and Xin-Zeng Wu USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA 90292 (Dated: June 10, 2015) Social behaviors are often contagious, spreading through a population as individuals imitate the decisions…”
- Business Intelligence and Analytics: From Big Data to Big Impact (2012, background): “We also provide some suggestions for the IS discipline in addressing these challenges (Chiang et al. 2012).”
- Standard errors and confidence intervals in inverse problems: sensitivity and associated pitfalls (2007, methodology): “There is a growing interest in measures of uncertainty in many areas of mathematical modeling as more sophisticated models are employed in biology [4, 3, 8], chemistry, sociology [7], etc.”