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

机译：自适应锚定模型：时间序列的静态和动态表示如何影响判断和预测

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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 modelthe 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.
机译：尝试预测未来事件时，人们通常会依赖历史数据。研究确定的时间序列判断预测的一个心理特征是，当人们从序列进行预测时，他们倾向于低估未来趋势的上升趋势而高估未来趋势的趋势，即所谓的趋势衰减（通过锚定，以及最近时间序列值的平均值调整不足）。时间序列中的事件可以顺序（动态模式）进行体验，也可以同时回顾性查看（静态模式），而不是实时地单独进行。在一个实验中，我们研究了呈现方式（动态和静态）对两种判断的影响：（a）下一个事件的预测（预测）和（b）估算所呈现系列中所有事件的平均值（平均估算）。对于所有类型的判断，参与者在动态模式下的响应基于静态模式下的最新事件，而不是静态模式下，但后果不同。因此，动态表示可以提高预测精度，但不能提高估计精度。现有的理论解释并不能预期这些结果。我们开发并提出了一种基于主体的模型，即自适应锚定模型（ADAM），以解决动态和静态呈现的刺激（可视呈现的数据）的处理序列之间的差异。 ADAM捕获呈现方式的变化如何在预测和判断任务中产生响应的变化（以及这些响应的准确性）。与线性回归时间序列模型相比，ADAM对预测和判断任务的模型预测更适合响应数据。此外，ADAM的表现优于自回归积分移动平均（ARIMA）和指数平滑模型，而这些模型都不能说明人们对平均估计任务的反应。

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