We examine risk attitudes under regret theory and derive analytical expressions for two components—the resolution and regret premiums—of the risk premium under regret theory. We posit that regret-averse decision makers are risk seeking (resp., risk averse) for low (resp., high) probabilities of gains and that feedback concerning the forgone option reinforces risk attitudes. We test these hypoth...

Decision makers can become trapped by myopic regret avoidance in which rejecting feedback to avoid short-term outcome regret (regret associated with counterfactual outcome comparisons) leads to reduced learning and greater long-term regret over continuing poor decisions. In a series of laboratory experiments involving repeated choices among uncertain monetary prospects, participants primed with...

Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm’s ability to learn is the game-theoretic notion of regret. We argue that the standard definition of regret becomes inadequate if the adversary is allowed to adapt to the online algorithm’s actions. We define the alternative notion of p...

W characterize the effect of anticipated regret on consumer decisions and on firm profits and policies in an advance selling context where buyers have uncertain valuations. Advance purchases trigger action regret if valuations turn out to be lower than the price paid, whereas delaying purchase may cause inaction regret from missing a discount or facing a stockout. Consumers whom we describe as ...

We present an algorithm that achieves almost optimal pseudo-regret bounds against adversarial and stochastic bandits. Against adversarial bandits the pseudo-regret is O ( K √ n log n ) and against stochastic bandits the pseudo-regret is O ( ∑ i(log n)/∆i). We also show that no algorithm with O (log n) pseudo-regret against stochastic bandits can achieve Õ ( √ n) expected regret against adaptive...

Regret minimization is important in both the Multi-Armed Bandit problem and Monte-Carlo Tree Search (MCTS). Recently, simple regret, i.e., the regret of not recommending the best action, has been proposed as an alternative to cumulative regret in MCTS, i.e., regret accumulated over time. Each type of regret is appropriate in different contexts. Although the majority of MCTS research applies the...

BACKGROUND Parental hesitancy for recommended childhood vaccines is a growing public health concern influenced by various factors. This study aimed to explore regret regarding parental decisions to vaccinate their children via possible correlations between anticipated regret, altruism, coping strategies, and parents' attitudes toward the vaccination of their children. METHODS The study was co...