Mathematical Approach to the Prediction of Scientific Discovery

Goffman, William; Harmon, Glynn

doi:10.1038/229103a0

Cited by 46 publications

(22 citation statements)

References 2 publications

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“…Each state was a function of its preceding state, and all states were representative of the others (ergodic), so the limited memory of the Markov model was sufficient to explain transitions. In summary, these discoveries resulted from a predictable pattern of oscillations through three states to acquire the necessary information elements and order them appropriately to arrive in state IV, the discovery state (Goffman & Harmon, 1971).…”

Section: Scientific Discovery and Growth Of Knowledgementioning

confidence: 99%

“…It required a dramatically larger number of journals to produce the number of generally used articles equal to the number in the minimal core set. They proposed that journals should thus be acquired according to their relative information usage value, budgetary constraints, and other local factors (Goffman & Morris, 1970).…”

Section: Bradfordian Distributions and Core Literature Identificationmentioning

confidence: 99%

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Remembering William Goffman: Mathematical information science pioneer

Harmon

2008

Information Processing & Management

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Section: Scientific Discovery and Growth Of Knowledgementioning

confidence: 99%

Section: Bradfordian Distributions and Core Literature Identificationmentioning

confidence: 99%

Remembering William Goffman: Mathematical information science pioneer

Harmon

2008

Information Processing & Management

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“…There are certainly a variety of emerging methodologies that unify the study of science. These include population dynamical models [1,2,3,4,5,6,7], networks of cocitation [8,9] and collaboration [10,11,12,13,14,15,16,17], disciplinary maps of science [18,19,20,21] and phylogenetic term analyses [22,23], among others. But a more profound question is whether studies using these methodologies and others capture common dynamics across fields.…”

Section: Introductionmentioning

confidence: 99%

General Critical Properties of the Dynamics of Scientific Discovery

Bettencourt

Kaiser

2011

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Scientific fields are difficult to define and compare, yet there is a general sense that they undergo similar stages of development. From this point of view it becomes important to determine if these superficial similarities can be translated into a general framework that would quantify the general advent and subsequent dynamics of scientific ideas. Such a framework would have important practical applications of allowing us to compare fields that superficially may appear different, in terms of their subject matter, research techniques, typical collaboration size, etc. Particularh' important in a field's history is the moment at which conceptual and technical unification allows widespread exchange of ideas and collaboration, at which point networks of collaboration show the analog of a percolation phenomenon, developing a giant connected component containing most authors. Here we investigate the generality of this topological transition in the collaboration structure of scientific fields as they grow and become denser. We develop a general theoretical framework in which each scientific field is an instantiation of the same large-scale topological critical phenomenon. We consider whether the evidence from a variety of specific fields is consistent with this picture, and estimate critical exponents associated with the transition. We then discuss the generality of the phenomenon and to what extent we may expect other scientific fields -including very large ones -to follow the same dynamics.

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“…Some have focused on characterizing the broad properties of relevant time series, such as numbers of publications and authors in a given field (see [1,2,3], and [4] and references therein). Others have focused on the structure and evolution of networks of coauthorship and citation, see e. g. [5,6,7].…”

Section: Introductionmentioning

confidence: 99%