OBJECTIVE The study examined the role of anticipated regret in human papillomavirus (HPV) vaccination decisions as well as potential antecedents of anticipated regret. METHOD In 2007, we interviewed 889 caregivers for girls aged 10-18 in areas of North Carolina with high rates of cervical cancer. The survey assessed vaccination regret (anticipated regret if daughter became more sexually activ...

BACKGROUND We sought to estimate the extent of decision regret among primary care patients and identify risk factors associated with regret. METHODS Secondary analysis of an observational descriptive study conducted in two Canadian provinces. Unique patient-physician dyads were recruited from 17 primary care clinics and data on patient, physician and consultation characteristics were collecte...

To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently. In this paper, we illustrate an intrinsic connection between these two concepts by showing that the dynamic regret can be expressed in terms of the adaptive regret and the functional variation. This observation implies that strongly adaptiv...

The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-sum game played between an optimizing player and an adversary, where the optimizing player selects a solution and the adversary selects costs with the intention of maximizing the regret of the player. Existing minmax regret models consider only deterministic solutions/strategies, and minmax regret...

n engl j med 377;16 nejm.org October 19, 2017 treatment and overestimate its likelihood for success, paving the way for later regret if the outcome is poor. Patients who choose elective procedures while in a hot state and end up with a bad outcome may be at particular risk for regret due to commission bias. Some researchers believe that the relationship of regret to either action or inertia mus...

Different types of dynamics have been studied in repeated game play, and one of them which has received much attention recently consists of those based on “no-regret” algorithms from the area of machine learning. It is known that dynamics based on generic no-regret algorithms may not converge to Nash equilibria in general, but to a larger set of outcomes, namely coarse correlated equilibria. Mo...

We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that there exist (ǫ, δ) differentially private variants of Upper Confidence Bound alg...

We study an online decision making problem where on each round a learner chooses a list of items based on some side information, receives a scalar feedback value for each individual item, and a reward that is linearly related to this feedback. These problems, known as contextual semibandits, arise in crowdsourcing, recommendation, and many other domains. This paper reduces contextual semibandit...

When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary processes because of their ability to switch between different forecasters or “experts”...