This paper describes a space of possible models of knowledge-lean human problem solving characterised by the use of recognition knowledge to control search. Recognition-based Problem Solvers (RPS) are contrasted to Soar and ACT - R which tend to use large goal stacks to control search and to situated theories of cognition that tend not to be able to do search at all (e.g. Pengi). It is shown that with appropriate knowledge increments R P S can apply algorithms such as depth-first search with a bounded demand on Working Memory . The discussion then focuses on h o w some weak methods, such as depth- first search, are more difficult to encode in R P S than others. It is claimed that the difficulty of encoding depth-first reflects human performance.

Regret Theory (Loomes & Sugden, 1982) is a theory of decision making based on the idea that people consider not only outcome utility, but also future regret or rejoicing, which depends on both the chosen option and foregone options. Regret theory was originally proposed as a theory of choice under uncertainty. Here, we demonstrate that Regret Theory also predicts the widely studied attraction, compromise, and similarity context effects. First, we show that it predicts attraction effects in choice among gamble triples. Second, we apply Regret Theory to non-gamble multi-attribute choice settings and show that both predicts these context effects and predicts a within-subject dissociation between the compromise and similarity effects previously observed in empirical studies. Regret Theory provides a foundation for a unified account of risky and multi-attribute choice, and we believe the form we present here provides the simplest account to date that explains phenomena in both domains.