We introduce a natural extension of the notion of swap regret, conditional swap regret, that allows for action modifications conditioned on the player’s action history. We prove a series of new results for conditional swap regret minimization. We present algorithms for minimizing conditional swap regret with bounded conditioning history. We further extend these results to the case where conditi...

This study focuses on the antecedents and consequences of Internet buyer regret in the overall purchasing process. We examine the roles that search effort, service-attribute evaluations, product-attribute evaluations and post-purchase price perceptions play in determining buyer regret and satisfaction in e-commerce. Furthermore, the study examines the consequences of regret and satisfaction in ...

Anticipated affective reactions to missing physical activity (PA), often labeled anticipated regret, has reliable evidence as a predictor of PA intention and behavior independent of other standard social cognitive constructs. Despite this evidence, the sources of regret are understudied and may come from many different reasons. The purpose of this study was to theme the reasons for why people r...

We conduct an investigation of supply chain contracts where a supplier incurs the holding cost of inventory and uses it to directly satisfy a retailer's demand. These contracts, known as " pull " contracts, have increased in popularity in practice, but have yet to be studied empirically. We investigate three pull contracts in a controlled laboratory environment: a wholesale price contract and t...

We consider adaptive sequential prediction of arbitrary binary sequences when the performance is evaluated using a general loss function. The goal is to predict on each individual sequence nearly as well as the best prediction strategy in a given comparison class of (possibly adaptive) prediction strategies, called experts. By using a general loss function, we generalize previous work on univer...

We consider online learning problems in multiagent systems comprised of distinct subsets of agents operating without a common time-scale. Each individual in the network is charged with minimizing the global regret, which is a sum of the instantaneous sub-optimality of each agent’s actions with respect to a fixed global clairvoyant actor with access to all costs across the network for all time u...

The starting assumption is that consciousness (subjective experience), rather than being an epiphenomenon, has a causal role in the optimization of certain human behaviors. After briefl y outlining some of the critical properties of consciousness, this chapter reviews empirical studies that demonstrate how much can be achieved in the way of action and decision making in the absence of relevant ...

We study a general class of learning algorithms, which we call regret-matching algorithms, along with a general framework for analyzing their performance in online (sequential) decision problems (ODPs). In each round of an ODP, an agent chooses a probabilistic action and receives a reward. The particular reward function that applies at any given round is not revealed until after the agent acts....

The concept of regret is designed for the long-term interaction of multiple agents. However, most concepts of regret do not consider even the short-term consequences of an agent’s actions: e.g., how other agents may be “nice” to you tomorrow if you are “nice” to them today. For instance, an agent that always defects while playing the Prisoner’s Dilemma will never have any swap or external regre...