by Linus Hof 
Choosing between options is often preceded by information search, where the decision maker learns about the option's payoff distribution from experiential sampling. Such experience-based decisions require not only a procedure for comparing options but also procedures for guiding and stopping information search. To understand how search processes may contribute to patterns in choice, we developed a computational framework for modeling sampling strategies as combinations of three building blocks---a search rule, a comparison rule, and a stopping rule. We analyzed the choice behavior emerging under different settings of each rule. The results show that the search rule (determining the rate of switching between options during sampling) impacts expected value maximization, but with opposite effects depending on the comparison rule: frequent switching increases maximization for strategies with a summary comparison rule but decreases maximization for strategies with a roundwise comparison rule. Further, a roundwise comparison rule leads to an apparent underweighting of rarely experienced events, whereas a summary comparison rule leads to overweighting. The analyses also reveal how the sampling strategies give rise to risk aversion and risk seeking as a function of the existence and attractiveness of rare events, and how the sampling strategies can implement bounded rationality. An empirical analysis (based on 5,627 trials) suggests that people's switching behavior and comparison rules are related in a way that, as our simulations show, leads to more maximizing choices. The results underscore the potential of our proposed framework for understanding the interplay between search and choice processes.