Machine Learning in Experiments
Saturday, Jan. 4, 2020 8:00 AM - 10:00 AM (PDT)
- Chair: Jeffrey Naecker, Wesleyan University
Using Machine Learning to Understand Bargaining Experiments
AbstractWe study dynamic unstructured bargaining with deadlines and one-sided private information about the amount available to share (the “pie size”). "Unstructured" means that players can make or withdraw any offers and demands they want at any time. Such paradigms, while lifelike have been displaced in experimental studies by highly structured bargaining because they are hard to analyze. Machine learning comes to the rescue because the players' wide range of choices in unstructured bargaining can be taken as "features" used to predict behavior. Machine learning approaches can accommodate a large number of features and guard against overfitting using test samples and methods such as penalized LASSO regression. In previous research we found that LASSO could add power to theoretical variables in predicting whether bargaining ended in disagreement. We replicate this work with higher stakes, subject experience, and special attention to gender differences. In addition, we add biomarkers, skin conductance and facial emotion reading, to further predict private information in order to lay the groundwork for biologically-informed mechanism design that can, in principle, eliminate all bargaining inefficiencies.
Supervised Machine Learning for Eliciting Individual Demand
AbstractDirect elicitation, guided by theory, is the standard method for eliciting individual-level la- tent variables. We present an alternative approach, supervised machine learning (SML), and apply it to measuring individual valuations for goods. We find that the approach is superior for predicting out-of-sample individual purchases relative to a canonical direct-elicitation approach, the Becker-DeGroot-Marschak (BDM) method. The BDM is imprecise and systematically biased by understating valuations. We characterize the performance of SML using a variety of estimation methods and data. The simulation results suggest that prices set by SML would increase revenue by 22% over the BDM, using the same data.
The Model You Know: Benchmarks for Models of Risk Preferences
AbstractCumulative prospect theory is viewed as a successful model of risk preferences because of its ability to better explain choice patterns than expected utility. However, utility-based models must also be tested by out-of-sample predictive power. We show that while CPT does indeed have better in-sample fit than EU, both models fail to make accurate out- of-sample predictions as compared to a model that makes predictions from non-choice responses. We show that the utility-based models fall behind because of their failure to predict across situations with differing amounts of certainty.
- C9 - Design of Experiments
- C1 - Econometric and Statistical Methods and Methodology: General