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.

We investigated the extent to which the human capacity for recognition helps to forecast political elections: We compared naïve recognition-based election forecasts computed from convenience samples of citizens’ recognition of party names to (i) standard polling forecasts computed from representative samples of citizens’ voting intentions, and to (ii) simple—and typically very accurate—wisdom-of-crowds-forecasts computed from the same convenience samples of citizens’ aggregated hunches about election results. Results from four major German elections show that mere recognition of party names forecast the parties’ electoral success fairly well. Recognition-based forecasts were most competitive with the other models when forecasting the smaller parties’ success and for small sample sizes. However, wisdom-of-crowds-forecasts outperformed recognition-based forecasts in most cases. It seems that wisdom-of-crowds-forecasts are able to draw on the benefits of recognition while at the same time avoiding its downsides, such as lack of discrimination among very famous parties or recognition caused by factors unrelated to electoral success. Yet it seems that a simple extension of the recognition-based forecasts—asking people what proportion of the population would recognize a party instead of whether they themselves recognize it—is also able to eliminate these downsides.

Individuals have been shown to adaptively select decision strategies depending on the environment structure. Two experiments extended this research to the group level. Subjects (N = 240) worked either individually or in two-person groups, or dyads, on a multi-attribute paired-comparison task. They were randomly assigned to two different environments that favored one of two prototypical decision strategies—weighted additive or take-the-best (between-subjects design in Experiment 1 and within-subject design in Experiment 2). Performance measures revealed that both individuals and dyads learned to adapt over time. A higher starting and overall performance rate in the environment in which weighted additive performed best led to the conclusion that weighted additive served as a default strategy. When this default strategy had to be replaced, because the environment structure favored take-the-best, the superior adaptive capacity of dyads became observable in the form of a steeper learning rate. Analyses of nominal dyads indicate that real dyads performed at the level of the best individuals. Fine-grained analyses of information-search data are presented. Results thus point to the strong moderating role of the environment structure when comparing individual with group performance and are discussed within the framework of adaptive strategy selection.

Contrary to the common belief that more information is always better, Gigerenzer et al. (1999) showed that simple decision strategies which rely on little information can be quite successful. The success of simple strategies depends both on bets about the structure of the environment and on the core capacities of the human mind, such as recognition memory (Gigerenzer, 2004). However, the interplay between the environment and the mind’s core capacities has rarely been precisely modeled. We illustrate how these environment-mind interactions could be formally modeled within the cognitive architecture ACT-R (J. R. Anderson et al., 2004). ACT-R is an integrated theory of mind that is tuned to the statistical structure of the environment, and it can account for a variety of phenomena such as learning, problem solving, and decision making. Here, we focus on studying decision strategies and show how the success of theses strategies in particular environments depends on characteristics of core cognitive capacities, such as recognition and short term memory.

Decision quality is often evaluated based on whether decision makers can adequately explain the decision process. Accountability often improves judgment quality because decision makers weigh and integrate information more thoroughly, but it could also hurt judgment processes by disrupting retrieval of previously encountered cases. We investigated to what degree process accountability motivates decision makers to shift from retrieval of past exemplars to rule-based integration processes. This shift may hinder accurate judgments in retrieval-based configural judgment tasks (Experiment 1) but may improve accuracy in elemental judgment tasks requiring weighing and integrating information (Experiment 2). In randomly selected trials, participants had to justify their judgments. Process accountability neither changed how accurately people made a judgment, nor the judgment strategies. Justifying the judgment process only decreased confidence in trials involving a justification. Overall, these results imply that process accountability may affect judgment quality less than expected.

The general principle behind the effects of nested sets on the use of base rates, we believe, is that the mind is prepared to take in “ecologically structured information.” Without any need to assume two cognitive systems, this principle explains how the proper use of base rates can be facilitated and also accounts for occasions when base rates are overused.