2 citations · 3 across the 2 of their papers we have counts for
5 papers
Measuring the Completeness of Theories
Drew Fudenberg, Jon Kleinberg, Annie Liang +1
We use machine learning to provide a tractable measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness." We apply this me…
Games of Incomplete Information Played By Statisticians
Annie Liang
Players are statistical learners who learn about payoffs from data. They may interpret the same data differently, but have common knowledge of a class of learning procedures. I pro…
Dynamically Aggregating Diverse Information
Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
An agent has access to multiple information sources, each of which provides information about a different attribute of an unknown state. Information is acquired continuously -- whe…
Overabundant Information and Learning Traps
Annie Liang, Xiaosheng Mu
We develop a model of social learning from overabundant information: Short-lived agents sequentially choose from a large set of (flexibly correlated) information sources for predic…
The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness
Jon Kleinberg, Annie Liang, Sendhil Mullainathan
When we test a theory using data, it is common to focus on correctness: do the predictions of the theory match what we see in the data? But we also care about completeness: how muc…