What’s wrong with Science? Modeling the collective discovery processes with the Nobel game

Scientometrics

pp 1–23

• David Chavalarias

Article

First Online:

26 August 2016

DOI: 10.1007/s11192-016-2109-9

Cite this article as:

Chavalarias, D. Scientometrics (2016). doi:10.1007/s11192-016-2109-9

• 
  

Abstract

There is an increasing pressure on scholars to publish to further or sustain a career in academia. Governments and funding agencies are greedy of indicators based on scientific production to measure science output. But what exactly do we know about the relation between publication levels and advances in science? How do social dynamics and norms interfere with the quality of the scientific production? Are there different regimes of scientific dynamics? The present study proposes some concepts to think about scientific dynamics, through the modeling of the relation between science policies and scholars’ exploration–exploitation dilemmas. Passing, we analyze in detail the effects of the “publish or perish” policy, that turns out to have no significant effects in the developments of emerging scientific fields, while having detrimental impacts on the quality of the production of mature fields.

Keywords

Collective discoveryDistributed knowledgeSocial dynamicsScience dynamicsPublish or perishReproducibilityScience policy

Electronic supplementary material

The online version of this article (doi:10.1007/s11192-016-2109-9) contains supplementary material, which is available to authorized users.

Mathematics Subject Classification

MSC 00A71MSC 91A06MSC 91A5091A10

JEL Classification

JEL D79JEL C73

Supplementary material

11192_2016_2109_MOESM1_ESM.pdf (2.3 mb)

Supplementary material 1 (pdf 2361 KB)

References

1. Allanach, B. (2014). Ambulance-chasing Large Hadron Collider collisions. The Guardian. https://www.theguardian.com/science/life-and-physics/2014/sep/17/ambulance-chasing-large-hadron-collider-collisions.
2. Aumann, R.J. (1997). Rationality and bounded rationality. In Sergiu Hart and Andreu Mas-Colell, editors, Cooperation: Game-Theoretic Approaches, number 155 in NATO ASI Series, (pp. 219–231). Springer, Berlin, Heidelberg. ISBN 978-3-642-64413-9 978-3-642-60454-6. doi:10.1007/978-3-642-60454-6_15. 00278.
3. Backović, M. (2016). A Theory of Ambulance Chasing. arXiv:1603.01204 [hep-ph, physics:physics].
4. Borner, K., Maru, J. T., & Goldstone, R. L. (2004). The simultaneous evolution of author and paper networks. Proceedings of the National Academy of Sciences, 101(Supplement 1), 5266–5273. doi:10.1073/pnas.0307625100. ISSN 0027-8424, 1091-6490.CrossRef
5. Calude, C.S., & Longo, G. (2016). The Deluge of Spurious Correlations in Big Data. Foundations of Science, pages 1–18, March 2016. ISSN 1233-1821, 1572-8471. doi:10.1007/s10699-016-9489-4.
6. Chavalarias, D. (2006). Metamimetic games: Modeling metadynamics in social cognition. Journal of Artificial Societies and Social Simulation, 9(2), 5. ISSN 1460-7425.
7. Chavalarias, D., Charron, S., Gardelle, V., & Bourgine, P. (2006). NOBEL, Le jeu de la découverte scientifique, Une approche analytique, experimentale et computationnelle. In conférence Modélisation, Optimisation et Simulation des Systèmes, Défis et Opportunités (MOSIM’06), Rabbat, 2006.
8. Chavalarias, D., & Gardelle, V. (2008). Social simulation of collective a discovery process: the Nobel Game. In 5th conference of the European Social Simulation Association, Brescia, Italy.
9. Chavalarias, D. (1998). La thèse de Popper est-elle réfutable. Master thesis, Ecole Polytechnique, Paris.
10. Chavalarias, D., Leenart, J.-B., & Panahi, M. (2014). Publish or Perish—Is that all? What is the social game behind science?. In Conference on Complex Systems 2014 (CSS'14), Italy: Lucca.
11. Cohen, J.D., McClure, S.M., & Yu, A.J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 362(1481), 933–942. doi:10.1098/rstb.2007.2098.00426. ISSN 0962-8436, 1471-2970.CrossRef
12. Conte, R., & Paolucci, M. (2001). Intelligent Social Learning. http://jasss.soc.surrey.ac.uk/4/1/3.html.
13. Edmond, B., Gilbert, N., Ahrweiler, P., & Scharnhorst, A. (2011). Simulating the Social Processes of Science. JASSS.
14. Edmonds, B. (2008). Artificial science: A simulation to study the Social Processes of Science. In Bruce Edmonds, Cesareo Hernandez, and Klaus G. Troitzsch, editors, Social Simulation - Technologies, advances, and new discoveries, pages 61–67. Hershey - New York.
15. Freedman, L.P., Cockburn, I.M., & Simcoe, T.S. (2015). The economics of reproducibility in preclinical research. Plos Biology, 13(6), e1002165. doi:10.1371/journal.pbio.1002165.00053. ISSN 1545-7885.CrossRef
16. Gilbert, G.N. (1997). A simulation of the structure of academic science. Sociological Research Online.
17. Harzing, A.W. (2007). Publish or Perish.
18. He, X., & Zhang, J. (2009). On the growth of scientific knowledge: Yeast biology as a case study. PLoS Computational Biology, 5(3), e1000320. doi:10.1371/journal.pcbi.1000320. ISSN 1553-7358.CrossRef
19. Ioannidis, J.P.A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. doi:10.1371/journal.pmed.0020124. ISSN 1549-1676.MathSciNetCrossRef
20. Larousserie, D., & Morin, H. (2015). Olivier Voinnet, star de la biologie végétale, sanctionné par le CNRS. Le Monde.fr, July 2015. ISSN 1950-6244. 00000.
21. Newman, M.E.J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409. doi:10.1073/pnas.98.2.404. ISSN 0027-8424, 1091-6490.MathSciNetCrossRefMATH
22. Open Science Collaboration. Estimating the reproducibility of psychological science. Science, 349 (6251): aac4716–aac4716, August 2015. ISSN 0036-8075, 1095-9203. doi:10.1126/science.aac4716.
23. Payette, Nicolas. (2012). Agent-Based Models of Science. In Andrea Scharnhorst, Katy Börner, & Peter van den Besselaar (Eds.), Models of science dynamics (pp. 127–157). Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN 978-3-642-23067-7 978-3-642-23068-4.CrossRef
24. Pentland, A. (2012). Big Data’s Biggest Obstacles. https://hbr.org/2012/10/big-datas-biggest-obstacles.
25. Popper, K.R. (1962). Conjectures and refutations: The growth of scientific knowledge. New York City: Basic Books.
26. Popper, K.R. (2002). The logic of scientific discovery. New York, London: Routledge. ISBN 978-0-203-99462-7 978-0-415-27843-0 978-0-415-27844-7.
27. Rabin, M. (2002). A perspective on psychology and economics. European Economic Review, 46(4–5), 657–685. doi:10.1016/S0014-2921(01)00207-0.00586. ISSN 00142921.CrossRef
28. Raymond, E.S. (2001). The Cathedral & the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. O’Reilly Media, Beijing ; Cambridge, Mass, 1 edition edition, January 2001. ISBN 978-0-596-00108-7.
29. Scharnhorst, A., Börner, K., & van den Besselaar, P. (eds). (2012). Models of Science Dynamics. Understanding Complex Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. ISBN 978-3-642-23067-7 978-3-642-23068-4.
30. Schelling, T.C. (1978). Micromotives and macrobehavior. Fels lectures on public policy analysis. Norton, New York, 1st ed edition. ISBN 978-0-393-05701-0 978-0-393-09009-3.
31. Sun, R., Naveh, I. (2009). Cognitive simulation of academic science. pages 3011–3017. IEEE, June 2009. ISBN 978-1-4244-3548-7. doi:10.1109/IJCNN.2009.5178638.
32. Watts, D.J., & Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. doi:10.1038/30918.28244. ISSN 0028-0836.CrossRef
33. Weisberg, M., & Muldoon, R. (2009). Epistemic landscapes and the division of cognitive labor. Philosophy of Science, 76(2), 225–252. doi:10.1086/644786. ISSN 0031-8248, 1539-767X.CrossRef
34. Zollman, K.J.S. (2007). The Communication Structure of Epistemic Communities. Philosophy of Science, 74(5), 574–587. doi:10.1086/525605. ISSN 0031-8248, 1539-767X.CrossRef

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016