Behavior Mining in h-index Ranking Game

Tagiew, Rustam and Ignatov, Dmitry I. (2017): Behavior Mining in h-index Ranking Game. Published in: CEUR Workshop Proceeding , Vol. 1968, No. Experimental Economics and Machine Learning (28 October 2017): pp. 52-61.

Abstract

Academic rewards and honors are proven to correlate with h-index, although it was not the decision criterion for them till recent years. Once h-index becomes the rule-setting scientometric ranking measure in the zero-sum game for academic positions and research resources as suggested by its advocates, the rational behavior of competing academics is expected to converge towards its game-theoretic solution. This paper derives the game-theoretic solution, its evidence in scientometric data and discusses its consequences on the development of science. DBLP database of 07/2017 was used for mining. Additionally, the openly available scientometric datasets are introduced as a good alternative to commercial datasets of comparable size for public research in behavioral sciences.

• Item Type: MPRA Paper
• Language: English
• Keywords: h-index, scientometrics, behavior mining, behavioral game theory, experimental economics, data science, social networks, research funding, R&D budget, innovation management
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; C - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C72 - Noncooperative Games; D - Microeconomics > D7 - Analysis of Collective Decision-Making > D78 - Positive Analysis of Policy Formulation and Implementation

References

Battelle Memorial Institute: 2014 global R&D funding forecast (2013)

Bornmann, L., Daniel, H.D.: What do we know about the h index? Journal of the American Society for Information Science and Technology 58 (9) (2007) 1381–1385

Hirsch, E.J.: An index to quantify an individual’s scientific research output. Proc. Nat. Acad. Sci. 46 (2005)

Lawrence, P.A.: The mismeasurement of science. Current Biology 17 (15) (2007) 583–585

Sengör, A.M.C.: How scientometry is killing science. GSA Today (2014) 44–45

Hirsch, J., Buela-Casal, G.: The meaning of the h-index. International Journal of Clinical and Health Psychology 14 (2) (2014) 161–164

Batista, P.D., Campiteli, M.G., Kinouchi, O.: Is it possible to compare researchers with different scientific interests? Scientometrics 68 (1) (2006) 179–189

Egghe, L.: The hirsch index and related impact measures. Annual review of information science and technology 44(1) (2010) 65–114

Hirsch, J.E.: An index to quantify an individual's scientific research output that takes into account the effect of multiple coauthorship. Scientometrics 85 (3) (2010) 741–754

Yu, M.C., Wu, Y.C.J., Alhalabi, W., Kao, H.Y., Wu, W.H.: Researchgate: An effective altmetric indicator for active researchers? Computers in human behavior 55 (2016) 1001–1006

Tagiew, R.: Research project: Text engineering tool for ontological scientometry. CoRR abs/1601.01887 (2016)

Ley, M.: DBLP - some lessons learned. PVLDB 2 (2) (2009) 1493–1500

Bailey, M.: What does an economist at facebook do? quora.com/What-does-an-economist-at-Facebook-do (2014)

Microsoft Inc.: Microsoft research new york city. research.microsoft.com/en-us/labs/newyork/ (2014)

Varian, H.R.: Big data: New tricks for econometrics. people.ischool.berkeley.edu/ hal/Papers/2013/ml.pdf (2013)

Tagiew, R., Ignatov, D.I., Neznanov, A.A., Poelmans, J., eds.: First International Workshop on Experimental Economics and Machine Learning, KU-Leuven (2012) ceur-ws.org/Vol-870/, at ICFCA.

Sunstein, C., Zittrain, J.: Social media and behavioral economics. today.law.harvard.edu/social-media-and-behavioral-economics-conference/ (2013)

Tagiew, R., Ignatov, D.I., Amroush, F., eds.: Second International Workshop on Experimental Economics and Machine Learning, IEEE Computer Society (2013) dx.doi.org/10.1109/ICDMW.2013.178, at ICDM.

Tagiew, R., Ignatov, D.I., Hilbert, A., Delhibabu, R., eds.: Proceedings of the Third Workshop on Experimental Economics and Machine Learning co-located with the 13th International Conference on Concept Lattices and Their Applications (CLA 2016), Moscow, Russia, July 18, 2016. Volume 1627 of CEUR Workshop Proceedings., CEUR-WS.org (2016)

Chen, Z.: From data mining to behavior mining. International Journal of Information Technology and Decision Making 5 (4) (2006) 703–712

United States. Congress. Senate: Hearings. Number Bd. 6. U.S. Government Printing Office (1961) 64

Roth, A., Sotomayor, M.: Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis. Econometric Society Monographs. Cambridge University Press (1992)

Persson, O., Glänzel, W., Danell, R.: Inflationary bibliometric values: The role of scientific collaboration and the need for relative indicators in evaluative studies. Scientometrics 60 (3) (2004) 421–432

Abbasi, A., Altmann, J., Hossain, L.: Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics 5 (4) (2011) 594–607

Bornmann, L., Daniel, H.D.: Multiple publication on a single research study: does it pay? the influence of number of research articles on total citation counts in biomedicine. Journal of the Association for Information Science and Technology 58 (8) (2007) 1100–1107

Parolo, P.D.B., Kumar, R., Ghosh, R., Huberman, B.A., Kaski, K.: Attention decay in science. (2015)

Evans, J.A.: Electronic publication and the narrowing of science and scholarship. Science (New York, N.Y.) 321 (5887) (2008) 395–399

Lozano, G.A., Larivilre, V., Gingras, Y.: The weakening relationship between the impact factor and papers’ citations in the digital age. Journal of the Association for Information Science & Technology 63 (11) (2012) 2140–2145

Bonzi, S., Snyder, H.W.: Motivations for citation: A comparison of self citation and citation to others. Scientometrics 21(2) (Jun 1991) 245–254

Sterzi, V.: Patent quality and ownership: An analysis of uk faculty patenting. Research Policy 42 (2) (2013) 564–576

Group, D.C.S.: Joint Declaration of Data Citation Principles. force11.org/datacitation (2014)

Tagiew, R., Ignatov, D.I., Delhibabu, R.: Economics of internet-based hospitality exchange. In: Proceedings of the 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) - Volume 01. WI-IAT ’15, Washington, DC, USA, IEEE Computer Society (2015) 493–498

Wu, J., Williams, K., hsuan Chen, H., Khabsa, M., Caragea, C., Ororbia, A., Jordan, D., Giles, C.L.: CiteSeerX : AI in a Digital Library Search Engine. (2014) 2930–2937