ABSTRACT
In the time of the COVID-19 pandemic, people are often faced with uncertain risky choice. Risky choice will be affected by different descriptions of the event's gain or loss framework, this phenomenon is known as the framing effect. With the continuous expansion and in-depth study of frame effects in the field of risky choice, researchers have found that the are quite different in different situations. People have different interpretations of the same event at different psychological distances, and will also be affected by their own emotions. Therefore, the current study examines the common influence of task frame, spatial distance, and trait emotion on risky choice through two studies. Study 1 used a 2 (framework: gain vs. loss) × 2 (trait sentiment: high vs. low) inter-subject design, and the dependent variable is the choice of the rescue plan for the classic "Asian disease" problem. The results revealed that trait anger did not predict individuals' risky choice preferences, and high trait anxiety led individuals to be more risk-averse. The framing effect exists in risky choice, and individuals prefer risk seeking in the loss frame. Study 2 used a 2 (spatial distance: distant vs. proximal) × 2 (framework: gain vs. loss) × 2 (trait sentiment: high vs. low) three-factor inter-subject design in which the dependent variable is the choice of rescue plan. The results indicate that the framing effect also exists in risky choice, and individuals prefer risk seeking in a loss frame. High trait anxiety lead individuals to be more risk-averse, while trait anger has no significant predictive effect on risk preference. Distant spatial distance lead individuals to increase their preference for risk-seeking under the gain frame, which leads to the disappearance of the framing effect. In conclusion, trait anxiety and spatial distance have a certain degree of influence on risky choice under the framework of gain and loss.
ABSTRACT
Outcomes of clinical trials need to be communicated effectively to make decisions that save lives. We investigated whether framing can bias these decisions and if risk preferences shift depending on the number of patients. Hypothetical information about two medicines used in clinical trials having a sure or a risky outcome was presented in either a gain frame (people would be saved) or a loss frame (people would die). The number of patients who signed up for the clinical trials was manipulated in both frames in all the experiments. Using an unnamed disease, lay participants (experiment 1) and would-be medical professionals (experiment 2) were asked to choose which medicine they would have administered. For COVID-19, lay participants were asked which medicine should medical professionals (experiment 3), artificially intelligent software (experiment 4), and they themselves (experiment 5) favour to be administered. Broadly consistent with prospect theory, people were more risk-seeking in the loss frames than the gain frames. However, risk-aversion in gain frames was sensitive to the number of lives with risk-neutrality at low magnitudes and risk-aversion at high magnitudes. In the loss frame, participants were mostly risk-seeking. This pattern was consistent across laypersons and medical professionals, further extended to preferences for choices that medical professionals and artificial intelligence programmes should make in the context of COVID-19. These results underscore how medical decisions can be impacted by the number of lives at stake while revealing inconsistent risk preferences for clinical trials during a real pandemic.
Subject(s)
COVID-19 , Risk-Taking , Artificial Intelligence , Clinical Trials as Topic , Decision Making , HumansABSTRACT
Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between-subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk-seeking behavior among participants. We developed cognitive and heuristics models using instance-based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience-based cognitive models could help build artificial intelligence models with fewer preference reversals.