Anja Dieckmann

The influence of information redundancy on probabilistic inferences

Information redundancy affects the accuracy of inference strategies. A simulation study illustrates that under high-information redundancy simple heuristics that rely on only the most important information are as accurate as strategies that integrate all available information, whereas under low redundancy integrating information becomes advantageous. Assuming that people exercise adaptive strategy selection, it is predicted that their inferences will more often be captured by simple heuristics that focus on part of the available information in situations of high-information redundancy, especially when information search is costly. This prediction is confirmed in two experiments. The participants’ task was to repeatedly infer which of two alternatives, described by several cues, had a higher criterion value. In the first experiment, simple heuristics predicted the inference process better under high-information redundancy than under low-information redundancy. In the second experiment, this result could be generalized to an inference situation in which participants had no prior opportunity to learn about the strategies’ accuracies through outcome feedback. The results demonstrate that people are able to respond adaptively to different decision environments under various learning opportunities.

When one cue is not enough : Combining fast and frugal heuristics with compound cue processing

One-reason decision-making heuristics as proposed by Gigerenzer, Todd, and the ABC Research Group (1999) have been shown to perform accurately. However, such strategies cannot deal with compound cues. We propose the Take The Best Configural Cue (TTB-Configural) as a fast and frugal heuristic that processes compound cues. In a series of three experiments, we analysed whether participants used this heuristic when making cue-based inferences on which of two alternatives had a higher criterion value. In two of the experiments, two cues were amalgamated into a valid compound cue by applying the AND or the OR logical rule, respectively. In the third experiment, there was no valid compound cue. Within each experiment, we also manipulated causal mental models through instructions. In the configural causal model, cues were said to act through the same causal mechanism. In the elemental causal model, cues were said to act through different causal mechanisms. In the neutral causal model, the causal mechanism was not specified. When a highly valid compound existed, and participants had a configural causal model, for the majority of them the strategy that could best account for their choices was TTB-Configural. Otherwise, the strategy that best predicted their choices was the Take The Best (TTB) heuristic.

Does causal knowledge help us be faster and more frugal in our decisions

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.