Biased Games

Authors: Ioannis Caragiannis, David Kurokawa, Ariel Procaccia

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we are interested in a fundamentally different way in which a player s mixed strategy can directly affect his utility. Specifically, the player may be biased towards (or away from) a specific base strategy, so his utility may also depend on the distance between his mixed strategy and the base strategy.In 3 we prove our main result: the existence of equilibria in biased games. Specifically, we show that an equilibrium always exists if each fi is a non-decreasing continuous convex function, and the norms are all Lp norms.In 4 we make some progress on the computation of equilibria in biased games, by (significantly) generalizing a basic algorithm for Nash equilibrium computation.
Researcher Affiliation Academia Ioannis Caragiannis University of Patras caragian@ceid.upatras.gr David Kurokawa Carnegie Mellon University dkurokaw@cs.cmu.edu Ariel D. Procaccia Carnegie Mellon University arielpro@cs.cmu.edu
Pseudocode No The paper discusses algorithmic approaches but does not provide pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology.
Open Datasets No This paper is theoretical and does not use or reference any publicly available or open datasets for training.
Dataset Splits No This paper is theoretical and does not involve the use of datasets with training, validation, or test splits.
Hardware Specification No This paper is theoretical and does not describe computational experiments that would require specific hardware details.
Software Dependencies No This paper is theoretical and does not describe computational experiments that would require specific software dependencies with version numbers.
Experiment Setup No This paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations.