Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution


A version of this paper appears in Organization Science,Vol. 10, No. 3, May-June 1999

Jason Wittenberg
Department of Political Science
Massachusetts Institute of Technology
Cambridge, MA 02142

     
John D. Sterman
Sloan School of Management
Massachusetts Institute of Technology
Cambridge, MA 02142

Abstract

What is the relative importance of structural versus contextual forces in the birth and death of scientific theories? We describe a dynamic model of the birth, evolution, and death of scientific paradigms based on Kuhn's Structure of Scientific Revolutions. The model creates a simulated ecology of interacting paradigms in which the creation of new theories is stochastic and endogenous. The model captures the sociological dynamics of paradigms as they compete against one another for members. Puzzle solving and anomaly recognition are also endogenous. We specify various regression models to examine the role of intrinsic versus contextual factors in determining paradigm success. We find that situational factors attending the birth of a paradigm largely determine its probability of rising to dominance, while the intrinsic explanatory power of a paradigm is only weakly related to the likelihood of success. For those paradigms that do survive the emergence phase, greater explanatory power is significantly related to longevity. However, the relationship between a paradigm's 'strength' and the duration of normal science is also contingent on the competitive environment during the emergence phase. Analysis of the model shows the dynamics of competition and succession among paradigms to be conditioned by many positive feedback loops. These self-reinforcing processes amplify intrinsically unobservable micro-level perturbations in the environment - the local conditions of science, society, and self faced by the creators of a new theory - until they reach macroscopic significance. Such dynamics are the hallmark of self-organizing evolutionary systems.


Thank you for your interest in our model. The Dynamo source code and accompanying documentation are downloadable below. Please note that we originally simulated the model in S4. Since we rely on random number generation you will almost certainly not be able to exactly reproduce our results.

Downloadable Files


SCIREV.ZIP (ZIP file)
This ZIP archive contains all three of the files listed below.

SCIREV.DYN (Text, Dynamo code)

This file contains the Dynamo code for the model we describe in our paper. Please note that simulating this code will not directly yield the results presented in the paper. Rather, it produces raw data on paradigm lifetimes from which the figures in the paper are created.

SCIREV.DOC (Text)
This file provides line-by-line documentation for SCIREV.DYN.

SCIREV.DEF (Text)
This file provides an alphabetical list of variable names, with accompanying definitions.


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