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Hidden Regret and Advantageous Selection in Insurance Markets
Insurance markets Pooling Separating equilibria Coverage Optimal choice
2016/3/18
We examine insurance markets in which there are two types of customers: those who regret suboptimal decisions and those who don't. In this setting, we characterize the equilibria under hidden informat...
Regret, Pride,and the Disposition Effect
Portfolio choice Anticipated regret and pride Individual preference Disposition effect
2016/3/18
We develop a dynamic portfolio choice model which incorporates anticipated regret and pride in individual's preferences and show that those preferences can cause investors to sell winning stocks and h...
Why do we observe overbidding in first
price private value auctions? This paper aims
to answer this question, which has been extensively
studied in the literature, from a nonstandard
point of view...
Losing the auction at an affordable price generates loser regret. In third price auctions if bidders anticipate
loser regret, then in line with the experimental findings, in a symmetric equilibrium t...
This paper demonstrates theoretically and experimentally that in first price auctions overbidding with respect to the risk neutral Nash equilibrium might be driven
from anticipated loser regret...
Regret Bounds for Reinforcement Learning with Policy Advice
Regret Bounds Reinforcement LearningPolicy Advice
2013/6/13
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with p...
Further Optimal Regret Bounds for Thompson Sampling
Further Optimal Regret Bounds Thompson Sampling
2012/11/23
Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several s...
We consider the restless Markov bandit problem, in which the state of each arm evolves according to a Markov process independently of the learner's actions. We suggest an algorithm that after $T$ step...
How Do I Regret Thee? Let Me Count My Alternatives: Regret and Decision Making in Intimate Relationships
Regret Intimate Relationships Decision Making Social Exchange Attractive Alternatives Relationship Satisfaction
2013/2/20
It is unsurprising when dissatisfied couples separate, but happy couples also dissolve their relationship. A hypothesized precursor to such outcomes is the availability of a better alternative partner...
Adaptive Learning of Uncontrolled Restless Bandits with Logarithmic Regret
Uncontrolled Restless Bandits Logarithmic Regret Optimization and Control
2011/9/15
Abstract: In this paper we consider the problem of learning the optimal policy for the uncontrolled restless bandit problem. In this problem only the state of the selected arm can be observed, the sta...
Robust approachability and regret minimization in games with partial monitoring
Robust approachability regret games partial monitoring
2011/6/20
Approachability has become a standard tool in analyzing learning algorithms in the adversarial
online learning setup. We develop a variant of approachability for games where there is ambiguity
in th...
The Non-Bayesian Restless Multi-Armed Bandit: a Case of Near-Logarithmic Regret
The Non-Bayesian Restless Multi-Armed Bandit:Near-Logarithmic Regret
2010/11/24
In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are $N$ arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A player seeks to activa...
No-Regret Reductions for Imitation Learning and Structured Prediction
No-Regret Reductions for Imitation Learning Structured Prediction
2010/11/9
Sequential prediction problems such as imitation learning, where future observations depend on
previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning.
A New Model of Random Regret Minimization
Discrete Choice Analysis Random Regret Minimization semi-compensatory choice choice set-specific preferences
2010/8/16
A new choice model is derived, rooted in the framework of Random Regret Minimization (RRM). The proposed model postulates that when choosing, people anticipate and aim to minimize regret. Whereas prev...
Some Bayesian Credibility Premiums Obtained by Using Posterior Regret Gamma-Minimax Methodology
Classes of distributions Credibility Minimax Premium Robustness
2009/9/24
In this paper,following the robust Bayesian paradigm, a procedure based on
the posterior regret-minimax principle is applied to derive,in a straightforwar
way, new credibility formula,making use of ...