Noisy Agents / Francisco Espinosa, Debraj Ray.

By:

Espinosa, Francisco

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w24627.Publication details: Cambridge, Mass. National Bureau of Economic Research 2018.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: Agents signal their type in a principal-agent model; the principal seeks to retain good agents. Types are signaled with some ambient noise. Agents can choose to add or remove additional noise at a cost. It is shown that monotone retention strategies, in which the principal keeps the agent if the signal crosses some threshold, are generically never equilibria. The main result identifies an equilibrium with a bounded retention zone, in which the principal is wary of both excessively good and excessively bad signals: she retains the agent if the signal is "moderate" and replaces him otherwise. The equilibria we uncover are robust to various extensions: non-normal signal structures, non-binary types, interacting agents, costly mean-shifting, or dynamics with term limits. We discuss applications to risky portfolio management, fake news and noisy government statistics.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w24627 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

May 2018.

Agents signal their type in a principal-agent model; the principal seeks to retain good agents. Types are signaled with some ambient noise. Agents can choose to add or remove additional noise at a cost. It is shown that monotone retention strategies, in which the principal keeps the agent if the signal crosses some threshold, are generically never equilibria. The main result identifies an equilibrium with a bounded retention zone, in which the principal is wary of both excessively good and excessively bad signals: she retains the agent if the signal is "moderate" and replaces him otherwise. The equilibria we uncover are robust to various extensions: non-normal signal structures, non-binary types, interacting agents, costly mean-shifting, or dynamics with term limits. We discuss applications to risky portfolio management, fake news and noisy government statistics.