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Found 10 papers, 0 papers with code
Optimal Scoring for Dynamic Information Acquisition
A principal seeks to learn about a binary state and can do so by enlisting an agent to acquire information over time using a Poisson information arrival technology.
With a Grain of Salt: Uncertain Veracity of External News and Firm Disclosures
We examine how uncertain veracity of external news influences investor beliefs, market prices and corporate disclosures.
Familiarity Facilitates Adoption: Evidence from Electric Vehicles
This paper shows that a non-price intervention which increased the prevalence of a new technology facilitated its further adoption.
Learning Underspecified Models
This paper examines whether one can learn to play an optimal action while only knowing part of true specification of the environment.
Coasian Dynamics under Informational Robustness
Under a simple transformation of the buyer's value distribution, the solution (in the gap case) is payoff-equivalent to a classic environment where the buyer knows her valuation at the beginning.
Learning versus Unlearning: An Experiment on Retractions
Our experimental design identifies unlearning -- i. e., updating from retractions -- and enables its comparison with learning from equivalent new information.
Identifying Wisdom (of the Crowd): A Regression Approach
Experts in a population hold (a) beliefs over a state (call these state beliefs), as well as (b) beliefs over the distribution of beliefs in the population (call these hypothetical beliefs).
Iterative Weak Learnability and Multi-Class AdaBoost
As SAMME, our algorithm is reduced to the Adaptive Boosting algorithm (Schapire and Freund (2012)) if the number of labels is two, and can be motivated as a functional version of the steepest descending method to find an optimal solution.
Machine Learning for Strategic Inference
We study interactions between strategic players and markets whose behavior is guided by an algorithm.
Evolutionarily Stable (Mis)specifications: Theory and Applications
Toward explaining the persistence of biased inferences, we propose a framework to evaluate competing (mis)specifications in strategic settings.
