A new model that generates Lotka's law

In this paper, we develop a new model for a process that generates Lotka's Law. We show that four relatively mild assumptions create a process that fits five different informetric distributions: rate of production, career duration, randomness, and Poisson distribution over time, as well as Lotka's Law. By simulation, we obtain good fits to three empirical samples that exhibit the extreme range of the observed parameters. The overall error is 7% or less. An advantage of this model is that the parameters can be linked to observable human factors. That is, the model is not merely descriptive, but also provides insight into the causes of differences between samples. Furthermore, the differences can be tested with powerful statistical tools.

[1]  John C. Huber Portmanteau test for randomness in poisson data , 2000 .

[2]  P. Allison Inequality and Scientific Productivity , 1980 .

[3]  Roland Wagner-Döbler,et al.  Scientific production: A statistical analysis of authors in physics, 1800-1900 , 2001, Scientometrics.

[4]  Miranda Lee Pao An empirical examination of Lotka's law , 1986, J. Am. Soc. Inf. Sci..

[5]  John C. Huber Cumulative advantage and success-breeds-success: the value of time pattern analysis , 1998, KDD 1998.

[6]  Abraham Bookstein Informetric distributions, part II: Resilience to ambiguity , 1990 .

[7]  Michael R. Fenton,et al.  Yes, the GIGP Really Does Work--And Is Workable!. , 1993 .

[8]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

[9]  John C. Huber Invention and Inventivity Is a Random, Poisson Process: A Potential Guide to Analysis of General Creativity , 1998 .

[10]  Kendall Birr,et al.  Pioneering in industrial research : the story of the general electric research laboratory , 1959 .

[11]  J. S. Long,et al.  Cumulative Advantage and Inequality in Science , 1982 .

[12]  P. Allison,et al.  Productivity Differences Among Scientists: Evidence for Accumulative Advantage , 1974 .

[13]  Ronald Rousseau Concentration and Diversity of Availability and Use in Information Systems: A Positive Reinforcement Model , 1992, J. Am. Soc. Inf. Sci..

[14]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[15]  M. F. Fox Publication Productivity among Scientists: A Critical Review , 1983 .

[16]  William Shockley,et al.  On the Statistics of Individual Variations of Productivity in Research Laboratories , 1957, Proceedings of the IRE.

[17]  Thomas C. Cochrane,et al.  The Americans: The Democratic Experience , 1974 .

[18]  John C. Huber A Statistical Analysis of Special Cases of Creativity. , 2000 .

[19]  Roland Wagner-Döbler,et al.  The Dependence of Lotka's Law on the Selection of Time periods in the Development of Scientific areas and authors , 1995, J. Documentation.

[20]  J. Pickands Statistical Inference Using Extreme Order Statistics , 1975 .

[21]  Abraham Bookstein,et al.  Informetric distributions, part II: Resilience to ambiguity , 1990, J. Am. Soc. Inf. Sci..

[22]  John C. Huber Invention and Inventivity as a Special Kind of Creativity, with Implications for General Creativity. , 1998 .

[23]  H. S. Sichel,et al.  A bibliometric distribution which really works , 1985, J. Am. Soc. Inf. Sci..

[24]  Derek de Solla Price,et al.  A general theory of bibliometric and other cumulative advantage processes , 1976, J. Am. Soc. Inf. Sci..

[25]  A. P. M. FLEMING Willis Rodney Whitney, Pioneer of Industrial Research , 1946, Nature.

[26]  Leo Egghe,et al.  Generalized Success-Breeds-Success Principle Leading to Time-Dependent Informetric Distributions , 1995, J. Am. Soc. Inf. Sci..

[27]  J. Tague The Success-Breeds-Success Phenomenon and Bibliometric Processes. , 1981 .

[28]  Jean-Marie Dufour,et al.  Generalized Portmanteau Statistics and Tests of Randomness , 1985 .

[29]  E. Gbur On the poisson index of dispersion , 1981 .

[30]  A. W. Kemp,et al.  Univariate Discrete Distributions , 1993 .

[31]  John C. Huber,et al.  A new method for analyzing scientific productivity , 2001, J. Assoc. Inf. Sci. Technol..

[32]  Wolfgang Glänzel,et al.  The cumulative advantage function. A mathematical formulation based on conditional expectations and its application to scientometric distributions , 1990 .

[33]  R. Eisenberger Learned industriousness. , 1992, Psychological review.

[34]  Alfred J. Lotka,et al.  The frequency distribution of scientific productivity , 1926 .

[35]  Abraham Bookstein Informetric Distributions. III. Ambiguity and Randomness , 1997, J. Am. Soc. Inf. Sci..

[36]  John C. Huber,et al.  The Underlying Process Generating Lotka's Law and the Statistics of Exceedances , 1998, Inf. Process. Manag..

[37]  Roland Wagner-Döbler,et al.  Scientific production: A statistical analysis of authors in mathematical logic , 2001, Scientometrics.

[38]  I. K. Ravichandra Rao,et al.  The distribution of scientific productivity and social change , 1980, J. Am. Soc. Inf. Sci..

[39]  Jane Fedorowicz,et al.  The Theoretical Foundation of Zipf's Law and Its Application to the Bibliographic Database Environment , 2007, J. Am. Soc. Inf. Sci..