Experiments on Decisions Under Uncertainty: A Theoretical Framework

The analysis of lab data entails a joint test of the underlying theory and of subjects' conjectures regarding the experimental design itself, how subjects frame the experiment. We provide a theoretical framework for analyzing the impacts of such conjectures or frames. We use experiments of decision making under uncertainty as a case study. Absent restrictions on subjects' framing of the experiment, we show that any behavior is consistent with standard updating ('anything goes'), including that suggestive of anomalies such as over-confidence, excess belief stickiness, etc. When the experimental protocol restricts subjects' conjectures (plausibly, by generating information during the experiment), standard updating has non-trivial testable implications. Such ``transparent'' protocols restrict action reversals that Bayesian subjects exhibit when they are provided with additional information. In the extreme case in which the amount of information revealed is conjectured to be independent of the underlying realized uncertainty, Bayesian updating is tantamount to dynamic consistency.