The Less-is-More Effect is a cognitive Bias where having less information or knowledge about a subject can lead to more accurate decisions or predictions, under specific conditions. This counterintuitive phenomenon suggests that in certain situations, more information or expertise can actually hinder decision-making or lead to worse outcomes.
An example often cited to illustrate this effect involves a game where participants are asked to predict which of two cities has a larger population. When one city is well-known and the other is not, people with less knowledge about the geographical region (and thus, less likelihood to have heard of the lesser-known city) may actually perform better. They tend to assume the city they have heard of is larger, which is often the case. More knowledgeable participants, aware of both cities, might overthink or second-guess their decision, leading to less accurate predictions.
Key aspects of the Less-is-More Effect include:
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Overconfidence in Expertise: Experts or those with more information can overestimate their ability to make accurate predictions, leading to overconfidence.
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Simpler Heuristics for the Less Informed: Those with less information often use simpler decision-making heuristics, which can sometimes be more effective in certain contexts.
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Relevance of Information: Sometimes additional information is not only unhelpful but can also be misleading, leading to poorer decisions.
The Less-is-More Effect is particularly relevant in fields like behavioral economics and decision-making psychology. It highlights the importance of considering not just the quantity of information, but also its relevance and the decision-making process it informs. This effect underscores that more information or expertise is not always beneficial and can sometimes lead to worse outcomes due to factors like overthinking or misapplication of knowledge.
See also Less-is-better Effect