Experience, Expectations and Hindsight:

Hindsight bias: how knowledge and heuristics affect our reconstruction of the past.

Once people know the outcome of an event, they tend to overestimate what could have been anticipated in foresight. Although typically considered to be a robust phenomenon, this hindsight bias is subject to moderating circumstances. In their meta-analysis, Christensen-Szalanski and Willham (1991) observed that the more experience people have with the task under consideration, the smaller is the ...

Hindsight Bias, Risk Perception, and Investment Performance

Once they have observed information, hindsight biased agents fail to remember how ignorant they were initially, “they knew it all along.” We formulate a theoretical model of this bias, providing a foundation for empirical measures, and implying that hindsight biased agents conduct incorrect learning about volatility. In an experiment involving 67 students from Mannheim University, we find that ...

The Construction and Globalization of the Knowledge Base in Social Systems

The exchange of meaning provides communication with a second layer that reflects and interacts with an underlying layer of uncertain exchanges of information. Language can be considered as the evolutionary achievement which enables us to distinguish between uncertainty and meaning in the communication. Meaning is generated as a recursive update of previous meaning, but meaning is provided to th...

“Hindsight is 20/20:” Student Perceptions of Language Learning and the Study Abroad Experience

The Effects of Disappointment on Hindsight Bias for Real-World Outcomes

Examined the effects of motivated processing on hindsight bias. One hundred fifty three college students estimated and later recalled the likelihood that 30 self-relevant events would occur during the next 2 months. Multi-level modelling was used to determine the (within-subject) effects of expectations, event valence and event controllability on hindsight bias and the extent to which these eff...

Hindsight Experience Replay

Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of impli...