The Augur team is happy to have Dr. Robin Hanson as one of our advisors and I wanted to share this short chat I had with him about his history and Augur. In addition, I wanted to go over a couple of his most important papers and how they relate to Augur.
Case Studies
As I have frequently stated on this blog, the amount of information I have processed and learned since I started has been amazing. Some of the most interesting and convincing material about this topic were the case studies, both formal and informal, that were documented across industries and subjects. I wanted to share some great resources and case studies as well as go into a little more detail in some instances.I’m writing this from the perspective of someone who is familiar with the general idea and principles of Prediction Markets but maybe hasn’t dug deep into the research that has been conducted in the past. Our blog is a place where we have posts that really cater to every level of potential Augur user, my pieces in particular are geared toward unfamiliar or newer audiences to the subject. Case studies are an excellent way to gain a deeper understanding of what we are doing.
A great place to start would be the very first recommendations made to me by Augur lead developer Joey Krug. These would be some articles written for peer reviewed publications and other pieces by Dr. Robin Hanson. Hanson has been an Augur Advisor early on and his work is the foundation of Prediction Markets. Dr. Hanson is currently a Professor of Economics at George Mason University, writer at OvercomingBias.com, and Chief Scientist at Consensus Point. His history includes involvement of the creation of Foresight Exchange and DARPA’s FutureMAP project. I wanted to summarize a few of Hanson’s papers as well as provide some other links to great examples for anyone to read.
Futarchy
The word most associated with Dr. Hanson is “Futarchy” and it’s best explained in a piece on Hanson’s website titled Futarchy: Vote Values, But Bet Beliefs. The simplified definition that can be found online for Futarchy is “a form of government proposed by economist Robin Hanson, in which elected officials define measures of national welfare, and prediction markets are used to determine which policies will have the most positive effect.”
The word most associated with Dr. Hanson is “Futarchy” and it’s best explained in a piece on Hanson’s website titled Futarchy: Vote Values, But Bet Beliefs. The simplified definition that can be found online for Futarchy is “a form of government proposed by economist Robin Hanson, in which elected officials define measures of national welfare, and prediction markets are used to determine which policies will have the most positive effect.”
The idea is that “democracy would continue to say what we want, but betting markets would now say how to get it.” This idea relies heavily on the accuracy and potential of prediction markets. There are three assumption that the reader must assume when reading the full Futarchy article. These assumptions are: Democracies fail largely by not aggregating available information, it is not that hard to tell rich happy nations from poor miserable ones, and finally that betting markets are our best known institution for aggregating information.
The previous assumption leaves an issue of how to properly define and measure both “rich happy nations” and “poor miserable nations”. Once a measurement on this is agreed upon, it can be used to settle bets made in a prediction market for policies.
He does give some excellent other examples of how prediction market results compare to other institutions used for predictions. The examples he provides include:
Racetrack market odds improve on the predictions of racetrack experts
Florida orange juice commodity futures improve on government weather forecasts
Prediction markets beat opinion polls at predicting U.S. election results (My previous blog “Prediction Markets & The UK Election is a very recent real world example of this occurring in a different country)
Prediction markets consistently beat Hewlett Packard official forecasts at predicting Hewlett Packard printer sales.
Florida orange juice commodity futures improve on government weather forecasts
Prediction markets beat opinion polls at predicting U.S. election results (My previous blog “Prediction Markets & The UK Election is a very recent real world example of this occurring in a different country)
Prediction markets consistently beat Hewlett Packard official forecasts at predicting Hewlett Packard printer sales.
The Idea Futures piece by Dr. Hanson expands on the idea of futures markets for ideas, the idea evolved into the modern term of “prediction markets” since the publication of his 1995 paper. On Dr. Hanson’s website hosted by George Mason University, he also published this excellent page that summarizes many of the ideas in his longer published piece. I will use some examples from the summary in this as it provides additional examples that illustrate the potential power of prediction markets.
These idea futures markets would create a consensus of relevant experts on a variety of subjects, with incentives for honesty when contributing. The idea that these markets would be visible to the public in a transparent way could lead to great things.
Some of the examples Hanson gives that I wanted to share are:
Real money markets, such as Iowa Electronic Markets and Wall Street, predict election outcomes better than opinion polls.
All of our familiar financial instruments: stocks, insurance, commodity futures, options were once forbidden by anti-gambling laws. Laws could change to favor Idea Futures too.
Some credit derivatives pay out if agencies downgrade the credit rating of a company's debt. This shows that subjective judgements by established judges can be used to settle bets.
Governments tend to use prizes less than private patrons. When science was patronized more by private sources, prizes were used much more often. Prizes are not infeasible now.
I highly recommend reading his page on idea futures which goes into much further detail on web games, legal limits, and criticisms of his publications.
