From their website:

PredictIt is an exciting new, real money site that tests your knowledge of political and financial events by letting you make and trade predictions on the future.

Taking part in PredictIt is simple and easy. Pick an event you know something about and see what other traders believe is the likelihood it will happen. Do you think they have it right? Or do you think you have the knowledge to beat the wisdom of the crowd?

The key to success at PredictIt is timing. Make your predictions when most people disagree with you and the price is low. When it turns out that your view may be right, the value of your predictions will rise. You’ll need to choose the best time to sell!

Keep in mind that, although the stakes are limited, PredictIt involves real money so the consequences of being wrong can be painful. Of course, winning can also be extra sweet.

For detailed instructions on participating in PredictIt, How It Works.

PredictIt is an educational purpose project of Victoria University, Wellington of New Zealand, a not-for-profit university, with support provided by Aristotle International, Inc., a U.S. provider of processing and verification services. Prediction markets, like this one, are attracting a lot of academic and practical interest (see our Research section). So, you get to challenge yourself and also help the experts better understand the wisdom of the crowd.

Coursera is now offering a sequence of online courses on data science. They include:

1. The Data Scientist's Toolbox

Upon completion of this course you will be able to identify and classify data science problems. You will also have created your Github account, created your first repository, and pushed your first markdown file to your account.

Upon completion of this course you will be able to obtain data from a variety of sources. You will know the principles of tidy data and data sharing. Finally, you will understand and be able to apply the basic tools for data cleaning and manipulation.

After successfully completing this course you will be able to make visual representations of data using the base, lattice, and ggplot2 plotting systems in R, apply basic principles of data graphics to create rich analytic graphics from different types of datasets, construct exploratory summaries of data in support of a specific question, and create visualizations of multidimensional data using exploratory multivariate statistical techniques.

In this course you will learn to write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize a data analysis so that it is reproducible and accessible to others.

In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use  the skills developed as a roadmap for more complex inferential challenges.

In this course students will learn how to fit regression models, how to interpret coefficients, how to investigate residuals and variability.  Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed.

Upon completion of this course you will understand the components of a machine learning algorithm. You will also know how to apply multiple basic machine learning tools. You will also learn to apply these tools to build and evaluate predictors on real data.

Students will learn how communicate using statistics and statistical products. Emphasis will be paid to communicating uncertainty in statistical results. Students will learn how to create simple Shiny web applications and R packages for their data products.

Discussion article for the meetup : Tempe, AZ: How to Measure Anything II

Discussion article for the meetup : Tempe, AZ: How to Measure Anything II

Discussion article for the meetup : Tempe, AZ: How to Measure Anything I

Discussion article for the meetup : Tempe, AZ: How to Measure Anything I

Discussion article for the meetup : Tempe Meetup

Discussion article for the meetup : Tempe Meetup

In a post today at EconLog, Bryan defends the "a bet is a tax on bullshit" maxim contra "portfolios reveal beliefs, bets reveal personality traits and public posturing" (preferred by Noah Smith and Tyler Cowen).

1. If portfolios really "reveal beliefs," Tyler and Noah should be able to look at a random person's portfolio and tell us everything he believes.  Yet neither Tyler, Noah, nor anyone else can do this.  They can't even deduce someone's financial beliefs from his portfolio, much less his beliefs about economic policy or the Fermi paradox.  Portfolios say something about beliefs, but every portfolio is consistent with a very wide range of views.

2. Most people's portfolios exhibit extreme inertia.  Even prominent Nobel prize-winning economists admit they follow simple rules of thumb when they invest.  So unless people's beliefs are carved in stone, how could portfolios possibly reveal much about their beliefs?  Tyler is a case in point: He changes his mind a hundred times a day, but he follows a simple financial strategy that hasn't varied in years.

The full post can be found here.

Bryan Caplan of Econlog asks his readers how to improve his will (given a few constraints) in light of the principles of optimal philanthropy. His current draft reads:

I give and bequeath to whatever charity is currently ranked #1 by GiveWell, the sum of $100,000 adjusted for inflation since 2013 using the U.S. Consumer Price Index, or 10% of the total value of my estate excluding our primary residence, whichever is smaller.  If GiveWell no longer exists, I give and bequeath the same sum to another charity, selected by my wife and children, dedicated to helping the deserving poor in the Third World in a maximally cost-effective manner.  I request that my wife and children consult my friends Robin Hanson, Alexander Tabarrok, Fabio Rojas, James Schneider, Michael Huemer, William Dickens, and Jason Brennan to help them select the most cost-effective charity with this mission.  If possible, funding for this bequest should come from my tax-deferred 403(b) retirement accounts.

The full blog post can be found here.

Robin Hanson responds:

I fear "the Third World" might not be a robust reference, and that GiveWell will no longer exist. You might pick some "ex ante % chance that I'd have died by now", such as 25%, and give the money away when you are at an age where you've suffered that % chance. This could ensure at 75% chance that you'll give the money away yourself.

If it's worth saying, but not worth its own post, even in Discussion, it goes here.

Here is a new post at EconLog in which Bryan Caplan discusses how signalling contributes to the status quo bias.

The lesson: In the real world, signaling naturally tends to ossify behavior - to lock in whatever the status quo happens to be.  If you're an optimist, you can protest, "It's only a tendency."  But even an optimist should admit that this tendency leads to atypically slow and unreliable progress.