关键词: 判断预测, 趋势阻尼, 任务框架, 序列趋势

Abstract: Generally, individuals make forecasting in two ways: statistic forecasting and judgmental forecasting. Judgmental forecasting is characterized as being associated with systematic biases because of its subjectivity. Trend damping, one of these biases, refers to that individuals tend to underestimate future values for upward trend, and overestimate them for downward ones when forecasting from time-series with noise. In other words, people underestimate the steepness of the series’ trend. Previous research found that damping effect in upward trend was larger than that in downward trend. However, most experiments on trend damping required participants to forecast quantities for which values are better than lower ones, such as the sales of a good. It is possible that downward trends represented a situation of perceived losses, and that upward ones represented perceived gains. And in literature about decision making, many studies have found that the way a problem is expressed, or framed, can dramatically influence judgment. Some research further showed that trend damping in the frame of loss was larger than that in the frame of gain. This means that previous results may mix the effect of frame. Based on the shortcoming in previous studies, present research aimed to investigate the effect of frame and trend on trend damping through two experiments. Previous research suggested that the effect of frame and series trend on trend damping may result from two reasons. First, participants’ forecasts may have been subject to an optimistic bias. Second, individuals may have expected actions to be taken to reverse downward trends but not to reverse upward ones. Optimistic bias may still existed under uncontrollable event whereas reverse expectation not. However, research suggested that optimistic bias and reverse expectation may be weaken under uncontrollable event. Since then, experiment 1 examined the effect of frame and trend on trend damping under controllable event, and experiment 2 explored the effect of frame and trend on trend damping under uncontrollable event. Among these two experiments, the materials were time series which were constructed using power-law functions, of the general form: y=100+300×(x/48)k +error. The dependent variable was the D-value of predictive value and truth-value. Results showed that:(1)The truth-values were significant bigger than predictive values for upward trends, were significant smaller than predictive values for downward trends, in other words, significant damping effect was occurred. (2)The damping effect was greater when the slope of time series was bigger. (3)The interaction effect of gain-loss frame and series trend was significant under controllable and uncontrollable situation. The simple effect analysis both suggested that damping effect was greater in downward trend than in upward one in the frame of gain, but was no difference in the frame of loss. In the frame of gain, the downward trend are always negative, and the upward one are always positive. According to the optimistic bias, individuals may expect that the negative thing was less likely to occur, which may result in greater damping effect in downward trend. The reverse expectation suggests that individuals may hypothesize some actions to be taken to reverse the negative thing, which may also result in greater damping effect in downward trend. Adaptation account of trend damping proposed that people more frequently experience data series that are increasing than data series that are decreasing. As a result individuals develop expectations about how series typically change. These expectations may influence their forecasts; as a consequence the damping effect in the frame of loss was no difference.

Key words: judgmental forecasting, trend damping, gain-loss frame, series trend