The influence of information redundancy on probabilistic inferences

Abstract

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 insituations ofhigh-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.