Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Learning the Type of the Opponent in Imperfectly Discriminating Contests with Asymmetric Information

View through CrossRef
In a competitive environment players often face uncertainty about the relative strength of their opponents. This paper considers a winner-take-all rent-seeking contest between two players with different costs of effort. Costs of effort are private knowledge, however, players have an opportunity to learn the opponent's type by engaging in either private (the opponent does not know about the information acquisition) or public (the opponent knows about the information acquisition) learning. We show that a situation, when one player learns the type of the opponent privately while the opponent abstains from learning cannot be an equilibrium. Yet, there exists an equilibrium, when one player engages in public learning and the other refrains from learning.
Elsevier BV
Title: Learning the Type of the Opponent in Imperfectly Discriminating Contests with Asymmetric Information
Description:
In a competitive environment players often face uncertainty about the relative strength of their opponents.
This paper considers a winner-take-all rent-seeking contest between two players with different costs of effort.
Costs of effort are private knowledge, however, players have an opportunity to learn the opponent's type by engaging in either private (the opponent does not know about the information acquisition) or public (the opponent knows about the information acquisition) learning.
We show that a situation, when one player learns the type of the opponent privately while the opponent abstains from learning cannot be an equilibrium.
Yet, there exists an equilibrium, when one player engages in public learning and the other refrains from learning.

Related Results

CREATING LEARNING MEDIA IN TEACHING ENGLISH AT SMP MUHAMMADIYAH 2 PAGELARAN ACADEMIC YEAR 2020/2021
CREATING LEARNING MEDIA IN TEACHING ENGLISH AT SMP MUHAMMADIYAH 2 PAGELARAN ACADEMIC YEAR 2020/2021
The pandemic Covid-19 currently demands teachers to be able to use technology in teaching and learning process. But in reality there are still many teachers who have not been able ...
The Interpersonal Effects of Anger and Happiness on Negotiation Behavior and Outcomes
The Interpersonal Effects of Anger and Happiness on Negotiation Behavior and Outcomes
How do emotions affect the opponent's behavior in a negotiation? Two experiments explored the interpersonal effects of anger and happiness. In Study 1 participants received informa...
Crowd-Sourcing for Data Science and Quantifiable Challenges: Optimal Contest Design
Crowd-Sourcing for Data Science and Quantifiable Challenges: Optimal Contest Design
We study the design of crowd-sourcing contests in settings where the output (from the contestants) is quantifiable -- for example, a data science challenge. This setting is in cont...
Optimal cardinal contests
Optimal cardinal contests
We study the design of crowdsourcing contests in settings where the outputs of the contestants are quantifiable, for example, a data science challenge. This setting is in contrast ...
Indefinitely Repeated Contests: An Experimental Study
Indefinitely Repeated Contests: An Experimental Study
We experimentally explore indefinitely repeated contests. Theory predicts more cooperation, in the form of lower expenditures, in indefinitely repeated contests with a longer expe...
Realizing the Asymmetric Index of a Graph
Realizing the Asymmetric Index of a Graph
A graph G is asymmetric if its automorphism group is trivial. Asymmetric graphs were introduced by Erd\H{o}s and R\'{e}nyi Erdos [1]. They suggested the problem of starting with ...
The Idea of Non-Discriminating War and Japan
The Idea of Non-Discriminating War and Japan
Introduction: It is a well-known formulation in most Japanese international law textbooks that two major changes in the theoretical status of war took place. The first change was ...
An Open-Ended Learning Framework for Opponent Modeling
An Open-Ended Learning Framework for Opponent Modeling
Opponent Modeling (OM) aims to enhance decision-making by modeling other agents in multi-agent environments. Existing works typically learn opponent models against a pre-designated...

Back to Top