Do people treat missing information adaptively when making inferences?

Abstract

When making inferences, people are often confronted with situations with incomplete information. Previous research has led to a mixed picture about how people react to missing information. Options include ignoring missing information, treating it as either positive or negative, using the average of past observations for replacement, or using the most frequent observation of the available information as a placeholder. The accuracy of these inference mechanisms depends on characteristics of the environment. When missing information is uniformly distributed, it is most accurate to treat it as the average, whereas when it is negatively correlated with the criterion to be judged, treating missing information as if it were negative is most accurate. Whether people treat missing information adaptively according to the environment was tested in two studies. The results show that participants were sensitive to how missing information was distributed in an environment and most frequently selected the mechanism that was most adaptive. From these results the authors conclude that reacting to missing information in different ways is an adaptive response to environmental characteristics.

Keywords:

• Adaptive decision making
• Incomplete information
• Missing information
• Probabilistic inferences
• Strategy selection

Acknowledgments

We thank Anita Todd for editorial assistance.

Notes

¹ When using binary cue values, a positive value can be coded as a value of 1.0 and a negative cue value (i.e., a nonpositive cue) can be coded as a value of 0, so that when positive and negative cue values occur equally often the average cue value is .50. When TTB compares an alternative that has a negative (positive) cue value with an alternative carrying a missing cue value that is replaced by the average, the alternative with the missing (positive) cue value will be selected. Thus, for TTB the exact worth of the average cue value is not crucial—using the average is identical with using any value between positive (1.0) and negative (0.0). In contrast, for WADD the exact average cue value was used to replace missing cue values, and the score that WADD determines is based on this average cue value.

² The payoff of a strategy is defined by the strategy's number of correct inferences multiplied by 50 eurocents, minus the strategy's number of incorrect inferences multiplied by 50 eurocents, and minus the strategy's number of cue values searched for multiplied by 3 eurocents.

³ To determine the search costs of WADD, we assumed limited information search. That is, we assumed that WADD would search for cues in the order of the cues' validities and always consider the cue values for both alternatives. With this information, WADD computes the difference of the cue values multiplied by the cues' validities. Information search was stopped whenever the preliminary decision based on the weighted difference could not be changed by the additional cues that had not been searched. For instance, when the three most valid cues spoke in favour of the first alternative, the remaining three cues could not change the preliminary decision, and the search would stop. Because it implements limited search instead of assuming exhaustive search, WADD provides a fair test against TTB, in that participants' limited information search becomes consistent across the two strategies.

⁴ Degrees of freedom for the analyses containing repeated measures factors were corrected by using the Greenhouse–Geisser technique (Greenhouse & Geisser, 1959). Note that this technique can only be applied when factors have more than two levels. We conducted Tukey's HSD (honest significant difference) test in post hoc analyses.