Menu Content/Inhalt
Seminars Print
previous year previous month next month next year
See by year See by month See by week See Today Search Jump to month
Miguel Ballester, University of Oxford Print
Tuesday, 27 March 2018, 14:00 - 15:15

Miguel Ballester, University of Oxford

 Separating Predicted Randomness from Noise

Abstract: Given observed stochastic choice data and a model of stochastic choice, we offer a methodology that enables separation of the data representing the model’s inherent randomness from residual noise, and thus quantify the maximal fraction of the data that are consistent with the model. We show how to apply our approach to any model of stochastic choice. We then study the case of four well-known models, each capturing a different notion of randomness. We conclude by illustrating our results with an experimental dataset. 


Location: R42.2.113
Contact: Nancy De Munck - This e-mail address is being protected from spam bots, you need JavaScript enabled to view it