Complex dynamics in learning complicated games (replicating Galla & Farmer 2012)

I have written a NetLogo version of the random game model of Galla & Farmer (2012) (free download).  It has been uploaded to the NetLogo community library and should appear in a day or so.  Read on if you are interested in Game Theory, esp. learning models and computational methods.

Chaotic dynamics in a complicated game. The payoffs are negatively correlated (-0.7) and memory for learning is long (alpha ≈ 0). Notice the squiggly lines in the time series plots (lower right). Each line is probability for a given move.
If the game were in equilibrium, these lines would be flat. (click for larger image)

Download the .nlogo file 
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B-Sides LV slides

Here are my slides for today's B-Sides Las Vegas talk (5pm Wednesday).  I'll be demoing the B-Sides SF spreadsheet (see previous post).  A video of the talk will be available on Archive.org in a day or so.

Ideal book for self-study: "Doing Bayesian Data Analysis"

In this post, I'd like to heartily recommend a book for anyone doing self-study who doesn't have much statistics or math in their background:

• Kruschke, J. (2010). Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS. Academic Press.

This book is head-and-shoulders better than the others I've seen.  I'm using it myself right now.  Here's what's good about it:

• It builds from very simple foundations.
• Math is minimized.  No proofs.
• From start to finish, everything is demonstrated through R programs. Anyone learning statistics today should be learning a statistics programming language at the same time.  R is the most popular choice and by some measures the best choice.
• It helps you learn Empirical Bayesian methods from every angle.  It does great both with the fundamental concepts and the practical applications.
• It takes you as far as you want to go, at least into advanced territory if you want.  But you don't have to read the whole textbook to benefit.

For what it's worth, this book was voted most popular introductory book on Stack Exchange.

Where are the NLP or Text Mining Tools for Automated Frame Analysis?

I'd like to do Frame Analysis (Goffman 1976, Johnston 1995) on a medium-sized corpus of text (articles, speeches, blog posts and comments) and I'm looking for NLP or text mining tools to help with it.  Strangely, I can't find anything. All the examples of frame analysis in published research (e.g. Nisbet 2009) use purely manual methods or computer-augmented manual analysis.

Frame Analysis requires sophisticated semantic analysis, filtering, situational understanding, and inference on missing text. Near as I can tell, this level of sophistication is beyond the grasp of the common NLP and text mining tools.  Is this true?  If not, do any of you know of fully automated tools for Frame Analysis?

I should add that I have two use cases. First, the most demanding, is automatic identification of frames followed by text classification.  Second, more feasible, is automatic classification of texts given frame definitions and sample texts.  The latter fits the classic machine learning model of supervised learning, so I assume that as long as my training set is large enough and representative enough, I can probably find an adequate ML classification algorithm.

[Edit 7/23/2013:  This is the best summary I could find of available tools: Frame Analysis: Software]

__________

Goffman, E. (1976). Frame Analysis: An Essay on the Organization of Experience. Harvard University Press.

Johnston, Hank (1995). A methodology for frame analysis: from discourse to cognitive schemata. In Social Movements and Culture (pp. 217–246). University of Minnesota Press.

Nisbet, M. C. (2009). Communicating Climate Change: Why Frames Matter for Public Engagement, Environment. Science and Policy for Sustainable Development, 51(2): 12–23.

Path Dependence in Tools, or Why I Use Mathematica When Everyone Else Uses Python & R

Nearly every job posting I see requires or desires experience in Python or R or both.  They have clearly won the programming language race for data science applications.  Sadly, I'm just getting started with them, because three years ago I made an impulsive decision that leaves me competent and skilled in Mathematica and not in the others.  The easy path is to continue using it on each new project.

Using LaTex equations in Blogger

Here are simple instructions for using MathJax within Blogger.  For example, this LaTex code:

 x\leq y+ \sum_{k=1}^n y_k 

surrounded by "$". This renders as:

$x\leq y+ \sum_{k=1}^n y_k$