Academic journal article Memory & Cognition

Does Causal Knowledge Help Us Be Faster and More Frugal in Our Decisions?

Academic journal article Memory & Cognition

Does Causal Knowledge Help Us Be Faster and More Frugal in Our Decisions?

Article excerpt

One challenge that has to be addressed by the fast and frugal heuristics program is how people manage to select, from the abundance of cues that exist in the environment, those to rely on when making decisions. We hypothesize that causal knowledge helps people target particular cues and estimate their validities. This hypothesis was tested in three experiments. Results show that when causal information about some cues was available (Experiment 1), participants preferred to search for these cues first and to base their decisions on them. When allowed to learn cue validities in addition to causal information (Experiment 2), participants also became more frugal (i.e., they searched fewer of the available cues), made more accurate decisions, and were more precise in estimating cue validities than was a control group that did not receive causal information. These results can be attributed to the causal relation between the cues and the criterion, rather than to greater saliency of the causal cues (Experiment 3). Overall, our results support the hypothesis that causal knowledge aids in the learning of cue validities and is treated as a meta-cue for identifying highly valid cues.

Most of the decisions we make in everyday life have to be fast and are based on minimal information. How else are we to buy groceries or find our soul mate in a crowded bar? How do we make such quick decisions, and how effective are they? The ABC Research Group at the Max Planck Institute for Human Development has suggested that we use fast and frugal heuristics in these situations-that is, simple but, nevertheless, accurate rules for making decisions with a minimum of information (Gigerenzer, Todd, & the ABC Research Group, 1999; Todd & Gigerenzer, 2000). These rules are fast because they do not involve much computation, and they are frugal because they search for only some of the available information in the environment.

Some of the fast and frugal heuristics, however, have been criticized for depending on complex computations to select and structure the information they need to be effective. The charge is that these heuristics owe much of thensimplicity and success to the computations necessary for setting up the cue search order before they can be used. These computations are very demanding in terms of both memory requirements and computational complexity (Juslin & Persson, 2002; Wallin & Gärdenfors, 2000). We suggest that causal knowledge-that is, knowledge about causal relationships between events hi the environment-can fill this gap. We explore how this knowledge, in general, might reduce the considerable setup costs of fast and frugal heuristics, and we offer more precise predictions of how it can influence decision-making processes. These predictions were tested in three experiments.

The Fast and Frugal Heuristics Approach: A Critical Review

One of the fast and frugal heuristics proposed by the ABC Research Group is take the best (TTB for short), a lexicographic heuristic for two-alternative forced choice tasks (Gigerenzer & Goldstein, 1996, 1999).' Specifically, TTB is used to infer which of two alternatives, described on several dichotomous cues, has a higher value on a quantitative criterion, such as which of two university professors earns more money, on the basis of such cues as gender or whether the professor teaches at a public or a private university. As a precise step-by-step algorithm, TTB is constructed from building blocks of information gathering and processing to generate a decision. More specifically, this heuristic has a search rule, which prescribes the order in which to search for information (lib looks up cues in the order of their validity-i.e., the probability that a cue will lead to the correct decision given that it discriminates between the alternatives; Gigerenzer & Goldstein, 1996); a stopping rule, describing when the search is to be stopped (TTB stops after the first discriminating cue); and a decision rule for how to use the available cues to make a decision (TTB chooses the alternative favored by the first discriminating cue). …

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