In choices between uncertain options, information search can increase the chances of distinguishing good from bad options. However, many choices are made in the presence of other choosers who may seize the better option while one is still engaged in search. How long do (and should) people search before choosing between uncertain options in the presence of such competition? To address this question, we introduce a new experimental paradigm called the competitive sampling game. We use both simulation and empirical data to compare search and choice between competitive and solitary environments. Simulation results show that minimal search is adaptive when one expects competitors to choose quickly or is uncertain about how long competitors will search. Descriptively, we observe that competition drastically reduces information search prior to choice.

Proponents of unconscious-thought theory assert that letting the unconscious "mull it over" can enhance decisions. In a series of recent studies, researchers demonstrated that participants whose attention was focused on solving a complex problem (i.e., those using conscious thought) made poorer choices, decisions, and judgments than participants whose attention was distracted from the problem (i.e., those purportedly using unconscious thought). We argue that this finding, rather than establishing the existence of a deliberation-without-attention effect, is explained more compellingly in terms of the well-established distinction between on-line and memory-based judgments. In Experiment 1, we reversed the recent finding by simply changing participants' on-line processing goal from impression formation to memorization. Experiment 2 provided a replication and further established that some cognitive effort appears necessary to produce both the original pattern of results and its reversal, suggesting that such judgments are ultimately a product of conscious, rather than unconscious, thinking.

Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use.

Evaluation judgments were affected by information order and not by subsequent unconscious versus conscious deliberation. In three experiments, we examined the influence of early positive information on final evaluations of four objects. Based on a task analysis, we predicted primacy effects in judgments in a sequential data acquisition task. Thinking periods following presentation were used to manipulate conscious or unconscious processing. In all three studies, we found no effects of thinking manipulations but instead found reliable order effects. We developed and tested an online judgment model on the basis of the belief updating model of Hogarth and Einhorn. The model accounted for large proportion of the individual level variability, and model comparison tests supported the presence of a primacy effect. 1 We note that in Strick et al. (2010), participants saw all the information not in random order across options, but the information was organized such that all aspects of one option were viewed before moving to another option. This is a very different information acquisition paradigm from the used by Note: Standard deviations are in parentheses. Rating scale is À10 (very negative) to +10 (very positive).Online Judgment Model 211 C. González Vallejo et al.

Without competition, organisms would not evolve any meaningful physical or cognitive abilities. Competition can thus be understood as the driving force behind Darwinian evolution. But does this imply that more competitive environments necessarily evolve organisms with more sophisticated cognitive abilities than do less competitive environments? Or is there a tipping point at which competition does more harm than good? We examine the evolution of decision strategies among virtual agents performing a repetitive sampling task in three distinct environments. The environments differ in the degree to which the actions of a competitor can affect the fitness of the sampling agent, and in the variance of the sample. Under weak competition, agents evolve decision strategies that sample often and make accurate decisions, which not only improve their own fitness, but are good for the entire population. Under extreme competition, however, the dark side of the Janus face of Darwinian competition emerges: Agents are forced to sacrifice accuracy for speed and are prevented from sampling as often as higher variance in the environment would require. Modest competition is therefore a good driver for the evolution of cognitive abilities and of the population as a whole, whereas too much competition is devastating.