The Role of Surprise in Hindsight Bias – A Metacognitive Model of Reduced and Reversed Hindsight Bias

Hindsight bias is the well researched phenomenon that people falsely believe that they would have correctly predicted the outcome of an event once it is known. In recent years, several authors have doubted the ubiquity of the effect and have reported a reversal under certain conditions. This article presents an integrative model on the role of surprise as one factor explaining the malleability of the hindsight bias. Three ways in which surprise influences the reconstruction of pre-outcome predictions are assumed: (1) Surprise is used as direct metacognitive heuristic to estimate the distance between outcome and prediction. (2) Surprise triggers a deliberate sense-making process, and (3) also biases this process by enhancing the retrieval of surprise-congruent information and expectancy-based hypothesis testing.

A Dichotomic Analysis of the Surprise Examination Paradox

This paper presents a dichotomic analysis of the surprise examination paradox. In section 1, I analyse the surprise notion in detail. I introduce then in section 2, the distinction between a monist and dichotomic analysis of the paradox. I also present there a dichotomy leading to distinguish two basically and structurally different versions of the paradox, respectively based on a conjoint and a disjoint definition of the surprise. In section 3, I describe the solution to SEP corresponding to the conjoint definition. Lastly, I expose in section 4, the solution to SEP based on the disjoint definition

Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations

Surprise Volume and Heteroskedasticity in Equity Market Returns

Heteroskedasticity in returns may be explainable by trading volume. We use different volume variables, including surprise volume---i.e. unexpected above-average trading activity---which is derived from uncorrelated volume innovations. Assuming weakly exogenous volume, we extend the Lamoureux and Lastrapes (1990) model by an asymmetric GARCH in-mean specification following Golsten et al. (1993). Model estimation for the U.S. as well as six large equity markets shows that surprise volume provides superior model fit and helps to explain volatility persistence as well as excess kurtosis. Surprise volume reveals a significant positive market risk premium, asymmetry, and a surprise volume effect in conditional variance. The findings suggest that, e.g., a surprise volume shock (breakdown)---i.e. large (small) contemporaneous and small (large) lagged surprise volume---relates to increased (decreased) conditional market variance and return.ARCH, trading volume, return volume dependence, asymmetric volatility, market risk premium, leverage effect