Archive for the 'Social' Category

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

Published on May 11, 2011 in Arrays, Published, Social and Vensim. 3 Comments Tags: , , , , .

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

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.

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.

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Polya urn with increasing returns

Published on April 13, 2011 in Economics, Published, Social, Stochastic and Vensim. 0 Comments Tags: .

This set of models performs a variant of a Polya urn experiment, along the lines of that described in Bryan Arthur’s Increasing Returns and Path Dependence in the Economy, Chapter 10. There’s a small difference, which is that samples are drawn with replacement (Bernoulli distribution) rather than without (hypergeometric distribution).

The interesting dynamics arise from competing positive feedback loops through the stocks of red and white balls. There’s useful related reading at http://tuvalu.santafe.edu/~wbarthur/Papers/Papers.html

I did the physical version of this experiment with Legos with my kids:

I tried the Polya urns experiment over lunch. We put 5 red and 5 white legos in a bowl, then took turns drawing a sample of 5. We returned the sample to the bowl, plus one lego of whichever color dominated the sample. Iterate. At the start, and after 2 or 3 rounds, I solicited guesses about what would happen. Gratifyingly, the consensus was that the bowl would remain roughly evenly divided between red and white. After a few more rounds, the reality began to diverge, and we stopped when white had a solid 2:1 advantage. I wondered aloud whether using a larger or smaller sample would lead to faster convergence. With no consensus about the answer, we tried it – drawing samples of just 1 lego. I think the experimental outcome was somewhat inconclusive – we quickly reached dominance of red, but the sampling process was much faster, so it may have actually taken more rounds to achieve that. There’s a lot of variation possible in the outcome, which means that superstitious learning is a possible trap.

This model automates the experiment, which makes it easier and more reliable to explore questions like the sensitivity of the rate of divergence to the sample size.

PolyaUrn.vpm

This version works with Vensim PLE (though it’s not supposed to, because it uses the RANDOM BERNOULLI function). It performs a single experiment per run, but includes sensitivity control files for performing hundreds of runs at a time (requires PLE Plus). That makes for a nice map of outcomes:

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A System Zoo

Published on April 8, 2011 in Biology, Business, Chaos & oscillations, Climate, Delays, Discrete, Dynamics, Economics, Energy, Engineering, Environment, External, Health, Population, Published, Resources, Social and Vensim. 0 Comments Tags: .

I just picked up a copy of Hartmut Bossel’s excellent System Zoo 1, which I’d seen years ago in German, but only recently discovered in English. This is the first of a series of books on modeling – it covers simple systems (integration, exponential growth and decay), logistic growth and variants, oscillations and chaos, and some interesting engineering systems (heat flow, gliders searching for thermals). These are high quality models, with units that balance, well-documented by the book. Every one I’ve tried runs in Vensim PLE so they’re great for teaching.

I haven’t had a chance to work my way through the System Zoo 2 (natural systems – climate, ecosystems, resources) and System Zoo 3 (economy, society, development), but I’m pretty confident that they’re equally interesting.

You can get the models for all three books, in English, from the Uni Kassel Center for Environmental Systems Research, http://www.usf.uni-kassel.de/cesr/. Follow the Download link and choose the Software category to obtain a .zip archive of the zoo models for the whole series, in Vensim .mdl format.

To tantalize you, here are some images of model output from Zoo 1. First, a phase map of a bistable oscillator, which was so interesting that I built one with my kids, using legos and neodymium magnets:

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Urban Dynamics

Published on June 14, 2010 in Economics, Published, Social and Vensim. 1 Comment Tags: , .

This is an updated version of Urban Dynamics, the classic by Forrester et al.

John Richardson upgraded the diagrams and cleaned up a few variable names that had typos.

I added some units equivalents and fixed a few variables in order to resolve existing errors. The model is now free of units errors, except for 7 warnings about use of dimensioned inputs to lookups (not uncommon practice, but it would be good to normalize these to suppress the warnings and make the model parameterization more flexible). There are also some runtime warnings about lookup bounds that I have not investigated (take a look – there could be a good paper lurking here).

Behavior is identical to that of the original from the standard Vensim distribution.

Urban Dynamics 2010-06-14.vpm

Urban Dynamics 2010-06-14.mdl

Urban Dynamics 2010-06-14.vmf

Terrorism Dynamics

Published on March 9, 2010 in External, iThink, Social, Thesis and Vensim. 0 Comments Tags: .

Contributed by Bruce Skarin

Introduction

This model is the product of my Major Qualifying Project (MQP) for my Bachelors degree in the field of system dynamics at Worcester Polytechnic Institute. There were two goals to this project:

1) To develop a model that reasonably simulates the historic attacks by the al-Qaida terrorist network against the United States.

2) To evaluate the usefulness of the model for developing public understanding of the terrorism problem.

The full model and report are available on my website.

Reference Mode

The reference mode for this model was the escalation of attacks linked to al-Qaida against the U.S., as shown below. The data for this chart is available through this Google Document.
Image:Terrorism_Reference_Mode.jpg

Causal View of the Model

Below is the causal diagram of the primary feedback loops in the model.

Image:Terrorism_Causal_Loop.png

Online Story Model

There is an online story version that explains the primary model structure as well as complete iThink and Vensim models on my MQP page.