In this paper, we investigate the online non-convex optimization problem which generalizes the classic online convex optimization problem by relaxing the convexity assumption on the cost function. For this type of problem, the classic exponential weighting online algorithm has recently been shown to attain a sub-linear regret of O( √ T log T ). In this paper, we introduce a novel recursive stru...

Using data obtained in a controlled ad-auction experiment that we ran, we evaluate the regret-based approach to econometrics that was recently suggested by Nekipelov, Syrgkanis, and Tardos (EC 2015). We found that despite the weak regret-based assumptions, the results were (at least) as accurate as those obtained using classical equilibrium-based assumptions. En route we studied to what extent ...

Often, preferences are driven by comparisons with choices not made. Preferences of a decision-maker over a set of options may arise from regret, i.e., from comparisons with alternatives forgone by the decision maker. This is natural when the decision maker has to choose between two options with random outcomes. Once the uncertainty is resolved he will know what outcome he received, but also wha...

Purpose – The primary purpose of this research was to investigate the role of anticipated regret in time-based work-family conflict decisions. Design/methodology/approach – A total of 90 working parents responded to a decision making problem describing a time-based conflict between a work event and a family event. Participants’ preference for which event to attend constituted the dependent vari...

We propose that decision maker's regulatory mode affects risk-taking through anticipated regret. In the Study 1 either a locomotion or an assessment orientation were experimentally induced, and in the Studies 2 and 3 these different orientations were assessed as chronic individual differences. To assess risk-taking we used two behavioral measures of risk: BART and hot-CCT. The results show that...

A regret minimizing set Q is a small size representation of a much larger database P so that user queries executed on Q return answers whose scores are not much worse than those on the full dataset. In particular, a k-regret minimizing set has the property that the regret ratio between the score of the top-1 item in Q and the score of the top-k item in P is minimized, where the score of an item...

We consider a variation on the problem of prediction with expert advice, where new forecasters that were unknown until then may appear at each round. As often in prediction with expert advice, designing an algorithm that achieves near-optimal regret guarantees is straightforward, using aggregation of experts. However, when the comparison class is sufficiently rich, for instance when the best ex...

This paper investigates the complexity of the min-max and min-max regret versions of the s− t min cut and min cut problems. Even if the underlying problems are closely related and both polynomial, we show that the complexity of their min-max and min-max regret versions, for a constant number of scenarios, are quite contrasted since they are respectively strongly NP-hard and polynomial. Thus, we...

We study the limit behavior and performance of no-regret dynamics in general game theoretic settings. We design protocols that achieve both good regret and equilibration guarantees in general games. In terms of arbitrary no-regret dynamics we establish a strong equivalence between them and coarse correlated equilibria. We examine structured game settings where stronger properties can be establi...

Decisions often imply trade-offs that force people to accept missing an opportunity in the past or in the future. However, it is not fully clear whether a past miss or a future miss elicits more regret. In a direct comparison, previous research had found support for the greater impact of future misses. In an experimental study with 216 participants, we replicated and extended previous research ...