Abstract
When faced with a decision, most people like to know the odds and prefer to avoid ambiguity. It has been suggested that this aversion to ambiguity is linked to people’s assumption of worst possible outcomes. We used two closely linked behavioural tasks in 78 healthy participants to investigate whether such pessimistic prior beliefs can explain ambiguity aversion. In the risk-taking task, participants had to decide whether or not they place a bet, while in the beliefs task, participants were asked what they believed would be the outcome. Unexpectedly, we found that in the beliefs task, participants were not overly pessimistic about the outcome in the ambiguity condition and in fact closer to optimal levels of decision-making than in the risk conditions. While individual differences in pessimism could explain outcome expectancy, they had no effect on ambiguity aversion. Consequently, ambiguity aversion is more likely caused by general caution than by expectation of negative outcomes despite pessimism-dependent subjective weighting of probabilities.
Highlights
Probabilistic decision-making is a crucial ability to navigate every-day situations that involve uncertainty about an outcome
That people are generally optimistic about personal outcomes[30] or that, instead of global optimism or pessimism, individual differences in attitude explain the weighting of probabilities under ambiguity[24]
The simple logic underlying these arguments is that optimism and pessimism respectively equal positive and negative outcome expectancy and that the reason for ambiguity aversion is that people expect more negative outcomes[28]
Summary
Probabilistic decision-making is a crucial ability to navigate every-day situations that involve uncertainty about an outcome. Ellsberg[8] has proposed that the level of information individuals have in a given situation influences the way they make a decision He defines ambiguity and risk as two forms of uncertainty: ambiguity as not knowing the odds and risk as knowing the odds. The idea of predictive coding is related to the broad group of Bayesian theories of decision-making These approaches model the decision-making process as updating prior beliefs by sampling from available information[17,18]. The difference between decision-making under risk and under ambiguity can be interpreted as a stronger influence of pessimistic outcome expectancy on ambiguous choices to compensate for the missing information about probabilities We approached this theory experimentally by comparing two similar paradigms in the same sample of participants but with different questions. In line with the Dual Systems Theory[3], we hypothesise that reaction times in the paradigm reflect the level of uncertainty with longest reaction times for the ambiguity condition and shortest reaction times for the low risk condition
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