Suppose a decision maker has to purchase a commodity over time with varying prices and demands. In particular, the price per unit might depend on the amount purchased and this price function might vary from step to step. The decision maker has a buffer of bounded size for storing units of the commodity that can be used to satisfy demands at later points in time. We seek for an algorithm decidin...

Consider a sequence of bits where we are trying to predict the next bit from the previous bits. Assume we are allowed to say ‘predict 0’ or ‘predict 1’, and our payoff is +1 if the prediction is correct and −1 otherwise. We will say that at each point in time the loss of an algorithm is the number of wrong predictions minus the number of right predictions so far. In this paper we are interested...

of the Orbitofrontal Cortex in the Experience of Regret’’ Regret is an emotion that accompanies negative outcomes to decisions for which we have been responsible (1). Given a choice between two gambles, subjects will experience regret if the unchosen gamble yields a higher reward than the chosen gamble (2). Camille et al. (3) hypothesized that the anticipation of regret is useful for steering d...

We consider regret minimization in adversarial deterministic Markov Decision Processes (ADMDPs) with bandit feedback. We devise a new algorithm that pushes the state-of-theart forward in two ways: First, it attains a regret of O(T ) with respect to the best fixed policy in hindsight, whereas the previous best regret bound was O(T ). Second, the algorithm and its analysis are compatible with any...

We model how decisions about the allocation of available funds to loans and Treasuries are dependent on perceptions about risk and regret in the banking industry. Our discussion is based on utility theory where a regret attribute is considered alongside a risk component as an integral part of the objective function. In addition, we provide a comparison between a riskand a regret-averse banks. I...

Online learning algorithms that minimize regret provide strong guarantees in situations that involve repeatedly making decisions in an uncertain environment, e.g. a driver deciding what route to drive to work every day. While regret minimization has been extensively studied in repeated games, we study regret minimization for a richer class of games called bounded memory games. In each round of ...