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Related Experiment Videos

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

Petko Kusev1, Paul van Schaik2, Krasimira Tsaneva-Atanasova3

  • 1Department of Psychology, Kingston University London.

Cognitive Science
|April 7, 2017
PubMed
Summary

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People often underestimate future trends when forecasting time series. Dynamic presentation improved prediction accuracy by anchoring judgments on recent data, unlike static presentation.

Area of Science:

  • Cognitive Psychology
  • Behavioral Economics
  • Data Analysis

Background:

  • Forecasting time series commonly relies on historical data.
  • Human judgment in forecasting exhibits trend-damping, underestimating upward trends and overestimating downward trends.
  • Existing models do not fully explain how presentation mode influences judgmental forecasting.

Purpose of the Study:

  • To investigate the influence of presentation mode (dynamic vs. static) on forecasting and average estimation tasks.
  • To determine if dynamic presentation improves prediction accuracy or estimation accuracy.
  • To develop a novel model explaining the effects of presentation mode on human judgment.

Main Methods:

  • An experiment comparing dynamic and static presentation modes for time series data.
Keywords:
Behavioral forecastingDescriptionExperienceJudgmentPredictionTrend

Related Experiment Videos

  • Participants performed forecasting (predicting the next event) and average estimation tasks.
  • Development of an agent-based model, the Adaptive Anchoring Model (ADAM), to simulate human responses.

    • Main Results:

    • Dynamic presentation led to anchoring on more recent events compared to static presentation.
    • Dynamic mode significantly improved prediction accuracy but not average estimation accuracy.
    • The Adaptive Anchoring Model (ADAM) better predicted human responses than linear-regression, ARIMA, and exponential-smoothing models.

    Conclusions:

    • Presentation mode critically influences judgmental forecasting accuracy.
    • Dynamic presentation enhances forecasting by focusing on recent data, but does not improve average estimation.
    • The Adaptive Anchoring Model (ADAM) provides a robust framework for understanding human judgment in time series analysis.