25 citations · 30 across the 12 of their papers we have counts for
16 papers
Robustness of Persuasion to Receiver Preferences
Ronen Gradwohl, Fengming Hu, Rann Smorodinsky
We study the robustness of Bayesian persuasion to (Knightian) uncertainty about the receiver's preferences. We consider two properties: continuity, in which only the modeler lacks…
Fairness under Competition
Ronen Gradwohl, Eilam Shapira, Moshe Tennenholtz
Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal…
Prediction-sharing During Training and Inference
Yotam Gafni, Ronen Gradwohl, Moshe Tennenholtz
Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to der…
Selling Data to a Competitor (Extended Abstract)
Ronen Gradwohl, Moshe Tennenholtz
We study the costs and benefits of selling data to a competitor. Although selling all consumers' data may decrease total firm profits, there exist other selling mechanisms -- in wh…
Selling Data to a Competitor
Ronen Gradwohl, Moshe Tennenholtz
We study the costs and benefits of selling data to a competitor. Although selling all consumers' data may decrease total firm profits, there exist other selling mechanisms -- in wh…
Informationally Robust Cheap-Talk
Itai Arieli, Ronen Gradwohl, Rann Smorodinsky
We study the robustness of cheap-talk equilibria to infinitesimal private information of the receiver in a model with a binary state-space and state-independent sender-preferences.…