彩票
任务(项目管理)
情境伦理学
零(语言学)
心理学
感觉
差异(会计)
认知心理学
社会心理学
前景理论
精算学
统计
经济
数学
微观经济学
语言学
会计
哲学
管理
作者
Elisabeth Schneider,Bernhard Streicher,Eva Lermer,Rainer von Sachs,Dieter Frey
出处
期刊:Zeitschrift Fur Psychologie-journal of Psychology
[Johann Ambrosius Barth Verlag]
日期:2017-07-01
卷期号:225 (1): 31-44
被引量:11
标识
DOI:10.1027/2151-2604/a000284
摘要
Abstract. Uncertainty is a dynamic state that is perceived as discomforting and individuals are highly motivated to reduce these feelings. With regard to risky decision making, people tend to overweigh the value of certainty and opt for zero-risk solutions, even if this results in a less favorable outcome. This phenomenon is referred to as the zero-risk bias and it has been demonstrated in varying contexts and with different methods. However, there is a high variance in the emergence of the bias reported by the existing literature, leaving it unclear to what extent the bias was evoked by the method or whether other psychological factors influenced people’s decision making. Four studies were conducted in order to investigate methodological and situational factors on the bias, comparing its emergence within different task formats (questionnaires vs. behavioral tasks), decision types (forced choice vs. free resource allocation), and different decision domains. Results indicate that the zero-risk bias is persistent over different methods but highly sensitive to contextual factors: abstractness of the task, decision domain, and appropriateness of the zero-risk option. First, its emergence varied between the task formats, in that it was shown more often in abstract than in concrete tasks. Second, participants’ choice of zero-risk did not correlate between different tasks, indicating effects of decision domain. Third, a zero-risk strategy seemed to be appropriate for dividing risks on objects (lottery urns in a gambling task) but not on persons (in a health scenario). In the latter situation, aspects like fairness influenced choice. Future research is needed to explore the relation between these factors and identify their underlying mechanisms.
科研通智能强力驱动
Strongly Powered by AbleSci AI