Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions. (6th April 2017)

• Record Type: Journal Article
• Authors: Kusev, Petko; van Schaik, Paul; Tsaneva‐Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick
• Is Part Of: Cognitive science. Volume 42:Number 1(2018)
• Page Start: 77
• Page End: 102
• Publication Date: 2017-04-06
• Subjects: Judgment -- Prediction -- Experience -- Description -- Trend -- Behavioral forecasting; Cognition -- Periodicals; Psycholinguistics -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1111/cogs.12476
• Languages: English
• ISSNs: 0364-0213

Abstract

Abstract: When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so‐called trend‐damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent‐based model—the adaptive anchoring model (ADAM)—to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear‐regression time series model. Moreover, ADAM outperformed autoregressive‐integrated‐moving‐average (ARIMA) and exponential‐smoothing models, while neither of these models accounts for people's responses on the average estimation task.