Construct · 4 fields · 9 citations
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Regret theory is a model of choice under uncertainty in which a person's satisfaction from an outcome depends not only on what they receive but on what they would have received had they chosen differently. Loomes and Sugden's 1982 Regret Theory: An Alternative Theory of Rational Choice Under Uncertainty added a regret and rejoice term to the utility of each outcome, set by the difference between it and the outcome of the action not taken. Bell reached a similar formulation the same year in Regret in Decision Making under Uncertainty. Here regret is not a feeling that is measured but a term inside a utility function, a mathematical comparison between a chosen outcome and its forgone alternative that can explain choices standard expected-utility theory cannot. The object is a component of modelled preference, defined by the structure of the choice, not an emotion reported by a person or a bound proved for an algorithm.[1][2]
In online learning, regret is the gap between the total payoff an algorithm actually earned and the payoff it would have earned by always playing the single best fixed action in hindsight. Auer, Cesa-Bianchi, and Fischer's 2002 Finite-time Analysis of the Multiarmed Bandit Problem bounded this quantity for the UCB rule, showing that the cumulative regret of a good bandit algorithm grows only logarithmically in the number of rounds. Hazan's Introduction to Online Convex Optimization gives the general framework, in which an algorithm whose average regret goes to zero is called no-regret. Here regret is a performance measure and a theorem, a number defined by an outcome sequence and a benchmark and proved to grow slowly. It is not a feeling and not a term in a preference model: nothing is experienced, and the comparison is to the best fixed action, not to a specific alternative a person passed over.[3][4]
In psychology regret is a negative, backward-looking emotion a person feels when they realise or imagine that a different choice would have led to a better outcome. It rests on counterfactual thinking, the mental comparison of what happened with what might have been, which Roese reviewed in his 1997 Counterfactual thinking and which Kahneman and Miller's 1986 Norm Theory: Comparing Reality to Its Alternatives traced to the ease with which alternatives come to mind. Zeelenberg and Pieters's 2007 A Theory of Regret Regulation 1.0 set out how the felt emotion shapes behaviour, pushing people to reverse, undo, or avoid the decisions that caused it. Here regret is a state a person experiences and reports, measured by self-report and studied for its causes and effects. It is neither a term in a utility function nor a bound on an algorithm, but an emotion in a mind.[7][5][6]
In consumer research regret is the specific dissatisfaction a buyer feels after a purchase when a rejected option turns out to look better than the one they chose. Tsiros and Mittal's 2000 Regret: A Model of Its Antecedents and Consequences in Consumer Decision Making modelled how this post-decision regret arises from comparison with forgone alternatives and feeds dissatisfaction, complaint, and switching. Inman and Zeelenberg's 2002 Regret in Repeat Purchase versus Switching Decisions showed that whether a decision was easy to justify changes how much regret a poor outcome produces. This meaning is close to the psychological emotion but is anchored to a market act: it is regret about a product choice, studied through its effect on repurchase and loyalty. It borrows the counterfactual emotion, applies it to buying, and is again a different object from the utility term of economics or the performance bound of machine learning.[8][9]
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