The work performed in the project was organized in 4 parts:
A. The first part is theoretical. We developed a new form of markets, on which people bet on what others think. Their bets reveal what they themselves think. The first paper introducing Bayesian markets was published in a prestigious general-audience journal and has been presented around the world and across disciplines. We developed several variants of the betting mechanisms, which have been used in the other parts of the project and by other researchers in surveys.
B. We implemented the new markets in the lab and online. In this part, we focused on simplifying the methods, to make them easy and intuitive. We introduced new, simple bets, that we called "top-flop bets". These bets can be used to reveal what people like. It can be used for instance in marketing. Imagine you watch a movie before everyone else (or you can test a product before it is officially launched). The movie producer would typically ask you what you thought of the movie, whether you liked it. But would you always tell the truth? You may want to please the movie producer and say it was nice. What we propose is to make you bet on the performance of the movie, e.g. whether it will get better review than another random movie. We showed how to organize this betting to reveal the most important information: whether you really liked the movie.
C. The methods we are working on also help us identify experts. In some cases, their track records is not available, so we do not know how trustworthy they are (and therefore whether they are real experts). So we do not know whose opinions we should follow. Consider a question such as “Is Proposition X true?” On a Bayesian market (developed in part A), people bet on what others believe the answer to that question is. We demonstrated how studying the earnings of the various agents on the market can tell us whether the proposition is actually true or not. We call it a “follow the money” algorithm. We showed theoretically and empirically that following the money on Bayesian markets outperforms following the majority opinion.
D. In part D, we focused on understanding when people tell the truth, whether we can predict if they don't, and whether we can use methods from the other parts to do so. For instance, we first tried to predict whether statements from a public personality (the 45th US president) were factually correct or not, using linguistic deception detection methods. We could with roughly 74% accuracy. We developed an algorithm to do so, which we used in another study as a benchmark, to see whether we could identify experts in deception detection among a group of volunteers and whether they would perform as well as our algorithm. Short answer: no, humans performs worse in such tasks than a relatively simple algorithm.
