Ignorance illusion in decisions under risk: The impact of perceived expertise on probability weighting

Abstract

Current decision‐making models assume that an individual's attitude towards risk is unique. Hence, a decision maker's processing of probabilities and the resulting degree of probability weighting should not vary within the domain of risk. This paper provides evidence that challenges this assumption. We conduct two experiments involving different gambles, that is, risky games where objective probabilities are known, no further information‐based advantages exist, and outcomes are independent of knowledge. Even though all probabilities are explicitly provided, we find that individuals exhibit more pronounced inverse‐S‐shaped probability weighting if they perceive their level of expertise regarding a gamble to be lower. This result suggests that individuals are subject to ignorance illusion in decisions under risk, constituting expertise‐dependent risk attitudes. We document that ignorance illusion stems from the wrongly assigned importance of perceived expertise in the decision‐making process and that it occurs in both the gain and the loss domain.

Explaining cryptocurrency returns: A prospect theory perspective

“…It is worth noting that previous research shows that loss aversion and nonlinear probability weighting, two key components of PT, are more pronounced among inexperienced individuals (Mrkva et al, 2020;Baars and Goedde-Menke, 2022). Since retail investors typically fall into this category, it is not surprising that PT has been particularly successful in describing their behaviour in traditional markets such as the stock market (Barberis et al, 2016).…”

“…PT is potentially an ideal candidate for explaining cryptocurrency returns because this market, unlike conventional asset markets, is dominated by (financially naïve) individual investors (Graffeo, 2021), who have little trading experience compared to institutional investors. Previous research shows that less (real and perceived) experience is accompanied by a higher degree of loss aversion (Mrkva et al, 2020) and "more pronounced inverse-S-shaped probability weighting" (Baars and Goedde-Menke, 2022), which are key components of PT.…”