In the United States, there used to be a belief that the next generation would always have it better. No more. A lingering economic malaise and non-stop apocalyptic jingo-ism about chemicals, food, medicine and the environment instead have young people suffering from pessimistic green fatigue.
Yet social scientists have continued to contend people have an 'irrational optimism bias' - a tendency to underestimate their chances of negative experiences, while overestimating their chances of positive events. 95 percent of Harvard students believe they will be on the top 50 percent of their class, for example. That makes sense, they probably were in the top 95 percent of their classes in high school, so it is not irrational. Optimism bias is thought to have contributed to past financial crises and the failure of individuals to look after themselves (e.g. eating healthily to avoid obesity) or their environment (e.g. fighting climate change). Every government considers optimism bias when planning large infrastructure projects and deciding which projects should be funded, and certainly has optimism bias when projecting the benefits of their economic policies - such as the US "cash for clunkers" program, which was touted as a success even though it cost $25,000 per car and was almost all subsidizing cars people were going to buy anyway. And sports fans. Sports fans are clearly optimism bias, right?
No, it just seems that way, finds a new paper that seeks to cast doubt on the idea that people are inherently over-optimistic or 'optimistically biased' about the future. Since psychology is all surveys and bad statistics, both sides can claim to be right. Most recently claimed that people fail to learn from bad news when told the actual chance of experiencing a negative life event (such as cancer). Such a failure to learn from bad news would result in an optimistic outlook (The Optimism Bias; Sharot, 2012). However, this new study, published today in Cognitive Psychology, demonstrates major flaws in this research supporting the existence of this optimism bias. According to the authors, prior studies have generated data patterns that look like people are being over-optimistic, when no such bias exists.
The psychologists first found that people's failure to learn from bad news was reversed when they were considering their chance of experiencing a positive event (e.g. living past 90 years old). For these events, people learned less from good news (i.e. learning their chance of living past 90 years old is higher than they originally thought).
The researchers subsequently created computerized simulations designed to behave in a completely rational way when faced with the same psychological tests used in previous research (e.g. learning from good vs bad news about future events). By definition, these simulations are not optimistic and thus will not show bias. However, these computer simulations produced the same pattern of data that is usually interpreted as showing optimism bias, due to the fact that belief scores changed more in response to good than bad news.
The study therefore shows how apparent optimism for negative events (and pessimism for positive events) can arise as a result of purely statistical processes. According to the authors, this supposed optimism bias is an artefact of the tests used to assess it, in conjunction with the rarity of negative events.
In addition to their simulations and analytical work, the researchers conducted five studies in which they corrected for these methodological problems as far as possible. In these improved studies they observed no evidence whatsoever for people learning more from good than bad news (i.e. no evidence for optimism bias).