Assessing the robustness and optimality of alternative decision rules with varying assumptions

Several alternative decision rules have been proposed for how individuals assess and choose options, such as mates and territories. Three of these rules are the threshold rule, where individuals choose the first option that exceeds a preset level of quality, the best-of-n rule, where individuals assess a fixed number of options and then choose the best of those options, and the comparative Bayes rule, where individuals use estimates of options to selectively assess and choose options. It has been previously concluded that the threshold rule produces higher average fitness than the best-of-n rule when assessment costs are not trivial. However, previous comparisons assumed that time and options are infinite, individuals can estimate the distribution of option quality without uncertainty or mistakes, and individuals receive perfect information about the quality of assessed options. I found that the best-of-n rule produces higher average fitness than the threshold rule despite significant assessment costs, when time for choosing an option is limited, when individuals are choosing from a small pool of options, when estimates of the distribution of option quality are error-prone, and when there is uncertainty about the distribution of option quality. I also found that the comparative Bayes rule produces higher average fitness than the threshold and best-of-n rules when time or options are limited and when individuals receive imperfect information about the quality of assessed options. Therefore, the optimality of alternative decision rules depends on more than the size of assessment costs and the previous conclusions of empirical studies that have assumed such need to be re-examined.