Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions

Hdl Handle:
http://hdl.handle.net/10149/621126
Title:
Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions
Authors:
Kusev, P. (Petko); van Schaik, P. (Paul) ( 0000-0001-5322-6554 ) ; Tsaneva-Atanasova, K. (Krasimira); Juliusson, A. (Asgeir); Chater, N. (Nick)
Affiliation:
Teesside University. Social Futures Institutue
Citation:
Kusev, P., van Schaik, P., Tsaneva-Atanasova, K., Juliusson, A., Chater, N. (2017) 'Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions' Cognitive Science; Online First 06 Apr 2017 : DOI: 10.1111/cogs.12476
Publisher:
Wiley Blackwell
Journal:
Cognitive Science
Issue Date:
6-Apr-2017
URI:
http://hdl.handle.net/10149/621126
DOI:
10.1111/cogs.12476
Additional Links:
http://doi.wiley.com/10.1111/cogs.12476
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.
Type:
Article
Language:
en
ISSN:
03640213
Rights:
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. For full details see http://onlinelibrary.wiley.com/doi/10.1111/cogs.12476/full [Accessed: 19/05/2017]

Full metadata record

DC FieldValue Language
dc.contributor.authorKusev, P. (Petko)en
dc.contributor.authorvan Schaik, P. (Paul)en
dc.contributor.authorTsaneva-Atanasova, K. (Krasimira)en
dc.contributor.authorJuliusson, A. (Asgeir)en
dc.contributor.authorChater, N. (Nick)en
dc.date.accessioned2017-05-19T12:28:56Z-
dc.date.available2017-05-19T12:28:56Z-
dc.date.issued2017-04-06-
dc.identifier.citationCognitive Science; Online First 06 Apr 2017en
dc.identifier.issn03640213-
dc.identifier.doi10.1111/cogs.12476-
dc.identifier.urihttp://hdl.handle.net/10149/621126-
dc.description.abstractWhen 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.en
dc.language.isoenen
dc.publisherWiley Blackwellen
dc.relation.urlhttp://doi.wiley.com/10.1111/cogs.12476en
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. For full details see http://onlinelibrary.wiley.com/doi/10.1111/cogs.12476/full [Accessed: 19/05/2017]en
dc.titleAdaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictionsen
dc.typeArticleen
dc.contributor.departmentTeesside University. Social Futures Institutueen
dc.identifier.journalCognitive Scienceen
or.citation.harvardKusev, P., van Schaik, P., Tsaneva-Atanasova, K., Juliusson, A., Chater, N. (2017) 'Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions' Cognitive Science; Online First 06 Apr 2017 : DOI: 10.1111/cogs.12476-
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Psychology; Kingston University London-
dc.contributor.institutionSchool of Social Sciences, Business & Law; Teesside University-
dc.contributor.institutionDepartment of Mathematics; University of Exeter-
dc.contributor.institutionDepartment of Psychology; City University London-
dc.contributor.institutionBehavioural Science Group; Warwick Business School-
dc.embargoNoneen
dc.date.accepted2016-11-10-
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