Abstract:

Probability is an important indicator reflecting risk and uncertainty. Existing research focuses on how an individual evaluates the characteristics, antecedents, and underlying mechanisms of static probability. However, in reality, probability estimation is dynamic and therefore has a trend effect, which in turn influences decision-making. This paper describes two manifestations of the trend effect of probability estimation: (1) the tendency of the revised probability estimation—namely, the increased or decreased trend effect of probability estimation changing from one time point to another; and (2) the single-bound probability—namely, the estimated expression being higher or lower than the upper or lower bound of a certain probability interval. In addition, we indicate the trend effect’s impact on an individual’s judgments, decision-making behaviors, and irrational decision deviations. Based on the theoretical perspective of psychological momentum, we propose an integrated model to explore the internal mechanism of probability estimation’s trend effect. The model reveals probability estimation’s trend effect induces psychological momentum through initial triggering of psychological momentum-related stimuli, release of the catalysis via psychological simulating of the future trend, and formation of the psychological momentum perception experience. Also it illustrates the two major components of the perception experience of psychological momentum (perceived quality and perceived velocity). Further, the model includes analyses of the three conditions affecting the generated degree of psychological momentum (strength, frequency, and continuity) and the dynamic between psychological momentum and decision-making behavior. Future research can focus on three areas. (1) Probability estimation’s trend effect when there are multiple information sources. Most existing research emphasizes unilateral information sources when the direction of probability estimation’s trend effect is relatively clear. But there are often multiple sources of information in the real world; for example, if two experts estimate the probability of a future event at the same time, more complicated situations, including the recency effect and framing effect, need further exploration. (2) The interaction between dynamic trend effect and static probability estimation. Static probability research has shown individuals often overestimate the occurrence of low probability events and underestimate the occurrence of medium and high probability events. Thus, are the trend effects of dynamic probability estimation different for the different probability risks? Further, for the interval value of extremely low probability, whether adopting the unilateral probability statement can effectively suppress the deviation of the overestimation of the small probability? (3) The trend effect of revised probability estimation in risk communication. Probability estimation’s trend effect plays an essential role in risk communication. The trend effect produced by the revised probability estimation or unilateral probability expression from the communicating party affects the other party’s understanding and judgment. Compared with those of the narrow interval, what are the characteristics of the wide probability interval communicated? Will the difference between the roles of the two communicating parties affect the probability estimation? All these issues need further exploration.

Key words: probability estimation, the trend effect, psychological momentum, revised probability, single- bound probability, decision making