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Found 4 papers, 0 papers with code
Satisficing in multi-armed bandit problems
Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty.
Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis
We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated UCL algorithm.
Parameter estimation in softmax decision-making models with linear objective functions
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data.
Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits
We develop the upper credible limit (UCL) algorithm for the standard multi-armed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret, which is optimal performance for uninformative priors.
