When attempting to predict future events, people commonly rely on historical data. Events in a time series can be experienced sequentially (dynamic mode), as in learning about decisions from experience (e.g., Kusev et al., in press, JEP:HPP), or, as with learning about decisions from descriptions, they can also be retrospectively viewed holistically (static mode) – not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on three sorts of judgments: (i) predictions of the next event (forecast), (ii) estimation of the average value of all the events in the presented series (average) and (iii) judged satisfaction of workers given that the series represented their monthly income (satisfaction). Relative to the static mode participants‘ responses in dynamic mode were anchored on more recent events for all three types of judgments but with different consequences – hence dynamic presentation improved prediction accuracy, but not estimation
