In college I read about the advantages of conjoint analysis over the more intuitive method of using a Likert scale–the familiar rate-this-thing-from-one-to-five or whatever scale. It turns out that people get bored with Likert scales, and end up either reporting everything as extremes, or the median. It has been shown that you can get a better reading on people by asking them about their preference regarding two items. In this post I’d like to share the beginning of a framework for modelling these sorts of situations. Specifically, I’d like to model agents with specific behaviors, and see if those behaviors are apparent through conjoint analysis, i.e, I’d like to test conjoint methods under different controlled circumstances.
I was reading Nate Silver’s The Signal and the Noise today and I ran across his idea of Bayesland, a place where people walk around with sandwich boards listing things on which they would place a bet. If they meet a person whose odds on an event differs substantially from their own odds on that event, then the two will make a bet.
I thought that it would be neat to simulate this idea and see what would happen. In my example, we have three agents that bet on the probability of a coin coming up heads. The coin is simulated through a Bernoulli process, and the true probability or heads is 60%.