In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex-post regret, we are able to adapt techniques of statistical machine learning to the design of payment rules. This computational approach to mechanism design is applicable ...

In the television show Affari Tuoi an individual faces a sequence of binary choices between a risky lottery with equiprobable prizes of up to half a million euros and a monetary amount for certain. The decisions of 114 show participants are used to test the predictions of ten decision theories: risk neutrality, expected utility theory, fanning-out hypothesis (weighted utility theory, transitive...

We investigate how auctioneers set reserve prices in auctions. A well-established theoretical result, assuming risk neutrality of the seller, is that the optimal reserve price should not depend on the number of participating bidders. In a set of controlled laboratory experiments, we find that seller behavior often deviates from the theoretical benchmarks. We extend the existing theory to explor...

In this paper, we propose an exact solution method to generate fair policies in Multiobjective Markov Decision Processes (MMDPs). MMDPs consider n immediate reward functions, representing either individual payoffs in a multiagent problem or rewards with respect to different objectives. In this context, we focus on the determination of a policy that fairly shares regrets among agents or objectiv...

This paper explores a fundamental connection between computational learning theory and game theory through a property we call no-Φ-regret. Given a set of transformations Φ (i.e., mappings from actions to actions), a learning algorithm is said to exhibit no Φ-regret if an agent experiences no regret for playing the actions the algorithm prescribes, rather than playing the transformed actions pre...

The k-regret queries aim to return a size-k subset of the entire database such that, for any query user that selects a data object in this size-k subset rather than in the entire database, her “regret ratio” is minimized. Here, the regret ratio is modeled by the level of difference in the optimality between the optimal object in the size-k subset returned and the optimal object in the entire da...

This research looks into the possibility of bringing together two distinct and empirically successful areas of behavioural economics; regret aversion and quasi-hyperbolic discounting. Standard regret aversion theory (Loomes and Sugden 1982, Bell 1982) operates in a world of uncertainty, where regret aversion arises due to the possibility of, having made an initial choice, a more preferable opti...

In this note, we study Nesterov’s accelerated gradient descent method in an online setting and establish both variational static and dynamic regret bounds using the functional variation, which “match” previous regret bounds in terms of gradient variation. To the best of our knowledge, this is the first work to study Nesterov’s accelerated gradient method in an online setting and our regret boun...

This work deals with four classical prediction games, namely full information, bandit and label efficient (full information or bandit) games as well as three different notions of regret: pseudo-regret, expected regret and tracking the best expert regret. We introduce a new forecaster, INF (Implicitly Normalized Forecaster) based on an arbitrary function ψ for which we propose a unified analysis...

The current study sought to answer three key questions about explaining the emotion of regret in the domain of casual sex: Are sex differences in sexual regret robust or attenuated in a highly egalitarian culture? What proximate psychological variables might explain sex differences in sexual regret? And what accounts for within-sex variation in experiences of sexual regret about casual sex. We ...