In this research, we explore how experience with an "attraction set" of options, designed to elicit an asymmetric-dominance (attraction) effect, affects choice making in a second "compromise set" designed to elicit a compromise effect. In Experiment 1, when a compromise set was presented, subjects who had chosen an asymmetrically dominating option from an attraction set were less likely to surrender to the compromise heuristic than their counterparts who had chosen the equivalent option from a binary set. Lower susceptibility to the compromise heuristic suggests that asymmetric dominance might have facilitated the learning of attribute preferences. In Experiment 2, subjects were asked to make six choices in the personal computer category. Subjects who had chosen any number of asymmetrically dominating options from the attraction condition were less susceptible to the compromise heuristic in a subsequent choice task than their counterparts who had chosen the same number of equivalent options from the binary condition. It was unlikely that the effect was caused by better memory of asymmetrically dominating options subjects had previously chosen. Results from the two experiments corroborated the reasoning that asymmetric dominance affects the learning of attribute weights and this effect persists in a subsequent choice task.

This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.

This paper replies to Christian Schubert's critical review of my work on opportunity as a normative criterion. Schubert argues that the criterion I have proposed would not command general assent because it does not recognize the legitimacy of individuals’ preferences for achieving self-development by constraining their future opportunities. I argue that my account of the ‘responsible agent’ is compatible with self-development, and that preferences for self-constraint are less common than Schubert suggests. For the purposes of normative economics, my opportunity criterion is much more generally applicable than Schubert's criterion of ‘opportunity to learn’.