Abstract:

          An evaluator estimates the innate talent of a career-oriented expert by observing the expert's performance in a prediction task, and may interfere with the expert's private signal by reducing or enhancing its precision. The expert on the other hand observes this interference and can misrepresent private beliefs through strategic predictions to enhance her reputation. We show that when priors are significantly uninformative so that the task is a priori hard, the evaluator reduces the precision of the expert's signal, while when priors are significantly informative, he enhances it. We also find that the evaluator's objectives of maximising information about talent and maximising the probability of `truthful expert advice' are aligned in and only in a priori hard tasks.

Biography

     Saptarshi P. Ghosh has completed his PhD in Economics from the University of Birmingham, UK. He has an MA in Economics from the Pennsylvania State University, USA and a Masters in Quantitative Economics from the Indian Statistical Institute, Kolkata. He is currently working as an Assistant Professor in the department of Economics at the Shiv Nadar University, in Delhi-NCR. Saptarshi’s research interests broadly lie in the areas of Game Theory, Applied Microeconomics, Development Economics, Political Economy and Public Policy.

In particular, he is presently working on issues relating to: Information Theory, Institutional Design, Voting, and Consumer Herding. His teaching interests lie in the areas of Microeconomics, Game Theory, Political Economy, Public Economics, Development Economics.