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

This is a very interesting model, both because it tackles ‘soft’ dynamics of paradigm formation in ‘hard’ science, and because it is an aggregate approach to an agent problem. Unfortunately, until now, the model was only available in DYNAMO, which limited access severely. It turns out to be fairly easy to translate to Vensim using the dyn2ven utility, once you know how to map the DYNAMO array FOR loops to Vensim subscripts.

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

J. Wittenberg and J. D. Sterman, 1999


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.

We consider the implications of these results for the rise and fall of new ideas in contexts outside the natural sciences such as management fads.

Cite as: J. Wittenberg and J. D. Sterman (1999) Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution. Organization Science, 10.

I believe that this version is faithful to the original, but it’s difficult to be sure because the model is stochastic, so the results differ due to differences in the random number streams. For the moment, this model should be regarded as a beta release.

Continue reading “Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution”

Dumb and Dumber

Not to be outdone by Utah, South Dakota has passed its own climate resolution.

They raise the ante – where Utah cherry-picked twelve years of data, South Dakotans are happy with only 8. Even better, their pattern matching heuristic violates bathtub dynamics:

WHEREAS, the earth has been cooling for the last eight years despite small increases in anthropogenic carbon dioxide

They have taken the skeptic claim, that there’s little warming in the tropical troposphere, and bumped it up a notch:

WHEREAS, there is no evidence of atmospheric warming in the troposphere where the majority of warming would be taking place

Nope, no trend here:

Satellite tropospheric temperature, RSS

Satellite tropospheric temperature (RSS, TLT)

Continue reading “Dumb and Dumber”

Legislating Science

The Utah House has declared that CO2 is harmless. The essence of the argument in HJR 12: temperature’s going down, climategate shows that scientists are nefarious twits, whose only interest is in riding the federal funding gravy train, and emissions controls hurt the poor. While it’s reassuring that global poverty is a big concern of Utah Republicans, the scientific observations are egregiously bad:

29 WHEREAS, global temperatures have been level and declining in some areas over the
30 past 12 years;
31 WHEREAS, the “hockey stick” global warming assertion has been discredited and
32 climate alarmists’ carbon dioxide-related global warming hypothesis is unable to account for
33 the current downturn in global temperatures;
34 WHEREAS, there is a statistically more direct correlation between twentieth century
35 temperature rise and Chlorofluorocarbons (CFCs) in the atmosphere than CO2;
36 WHEREAS, outlawed and largely phased out by 1978, in the year 2000 CFC’s began to
37 decline at approximately the same time as global temperatures began to decline;

49 WHEREAS, Earth’s climate is constantly changing with recent warming potentially an
50 indication of a return to more normal temperatures following a prolonged cooling period from
51 1250 to 1860 called the “Little Ice Age”;

The list cherry-picks skeptic arguments that rely on a few papers (if that), nearly all thoroughly discredited. There are so many things wrong here that it’s not worth the electrons to refute them one by one. The quality of their argument calls to mind to the 1897 attempt in Indiana to legislate that pi = 3.2. It’s sad that this resolution’s supporters are too scientifically illiterate to notice, or too dishonest to care. There are real uncertainties about climate; it would be nice to see a legislative body really grapple with the hard questions, rather than chasing red herrings.

The Dynamics of Science

First, check out SEED’s recent article, which asks, When it comes to scientific publishing and fame, the rich get richer and the poor get poorer. How can we break this feedback loop?

For to all those who have, more will be given, and they will have an abundance; but from those who have nothing, even what they have will be taken away.
—Matthew 25:29

Author John Wilbanks proposes to use richer metrics to evaluate scientists, going beyond publications to consider data, code, etc. That’s a good idea per se, but it’s a static solution to a dynamic problem. It seems to me that it spreads around the effects of the positive feedback from publications->resources->publications a little more broadly, but doesn’t necessarily change the gain of the loop. A better solution, if meritocracy is the goal, might be greater use of blind evaluation and changes to allocation mechanisms themselves.


The reason we care about this is that we’d like science to progress as quickly as possible. That involves crafting a reward system with some positive feedback, but not so much that it easily locks in to suboptimal paths. That’s partly a matter of the individual researcher, but there’s a larger question: how to ensure that good theories out-compete bad ones?


Now check out the work of John Sterman and Jason Wittenberg on Kuhnian scientific revolutions.

Update: also check out filter bubbles.

Friendly Climate Science & Policy Models

Beth Sawin just presented our C-ROADS work in Copenhagen. The model will soon be available online and in other forms, for decision support and educational purposes. It helps people to understand the basic dynamics of the carbon cycle and climate, and to add up diverse regional proposals for emissions reductions, to see what they imply for the globe. It’s a small model, yet there are those who love it. No model can do everything, so I thought I’d point out a few other tools that are available online, fairly easy to use, and serve similar purposes.


From MNP, Netherlands. Like C-ROADS, runs interactively. The downloadable demo version is quite sophisticated, but emphasizes discovery of emissions trajectories that meet goals and constraints, rather than characterization of proposals on the table. The full research version, with sector/fuel detail and marginal abatement costs, is available on a case-by-case basis. Backed up by some excellent publications.


Ben Matthews’ Java Climate Model. Another interactive tool. Generates visually stunning output in realtime, which is remarkable given the scale and sophistication of the underlying model. Very rich; it helps to know what you’re after when you start to get into the deeper levels.


The tool used in AR4 to summarize the behavior of 19 GCMs, facilitating more rapid scenario experimentation and sensitivity analysis. Its companion SCENGEN does nice regional maps, which I haven’t really explored. MAGICC takes a few seconds to run, and while it has a GUI, detailed input and output is buried in text files, so I’m stretching the term “friendly” here.

I think these are the premier accessible tools out there, but I’m sure I’ve forgotten a few, so I’ll violate my normal editing rules and update this post as needed.