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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Confirmation bias | 7/11 | https://en.wikipedia.org/wiki/Confirmation_bias | reference | science, encyclopedia | 2026-05-05T13:44:04.336359+00:00 | kb-cron |
=== Optimal information acquisition === Recent research in economics has challenged the traditional view of confirmation bias as purely a cognitive flaw. Under conditions where acquiring and processing information is costly, seeking confirmatory evidence can actually be an optimal strategy. Instead of pursuing contrarian or disconfirming evidence, it may be more efficient to focus on sources likely to align with one's existing beliefs, given the constraints on time and resources. Economist Weijie Zhong has developed a model demonstrating that individuals who must make decisions under time pressure, and who face costs for obtaining more information, will often prefer confirmatory signals. According to this model, when individuals believe strongly in a certain hypothesis, they optimally seek information that confirms it, allowing them to build confidence more efficiently. If the expected confirmatory signals are not received, their confidence in the initial hypothesis will gradually decline, leading to belief updating. This approach shows that seeking confirmation is not necessarily biased but may be a rational allocation of limited attention and resources.
== Real-world effects ==
=== Social media === In social media, confirmation bias is amplified by the use of filter bubbles and echo chambers (or "algorithmic editing"), which displays to individuals only information they are likely to agree with, while excluding opposing views. Some have argued that confirmation bias is the reason why society can never escape from filter bubbles, because individuals are psychologically hardwired to seek information that agrees with their preexisting values and beliefs. Others have further argued that the mixture of the two is degrading democracy—claiming that this "algorithmic editing" removes diverse viewpoints and information—and that unless filter bubble algorithms are removed, voters will be unable to make fully informed political decisions. The rise of social media has contributed greatly to the rapid spread of fake news, that is, false and misleading information that is presented as credible news from a seemingly reliable source. Confirmation bias (selecting or reinterpreting evidence to support one's beliefs) is one of three main hurdles cited as to why critical thinking goes astray in these circumstances. The other two are shortcut heuristics (when overwhelmed or short of time, people rely on simple rules such as group consensus or trusting an expert or role model) and social goals (social motivation or peer pressure can interfere with objective analysis of facts at hand). In combating the spread of fake news, social media sites have considered turning toward "digital nudging". This can currently be done in two different forms of nudging. This includes nudging of information and nudging of presentation. Nudging of information entails social media sites providing a disclaimer or label questioning or warning users of the validity of the source while nudging of presentation includes exposing users to new information which they may not have sought out but could introduce them to viewpoints that may combat their own confirmation biases.
=== Science and scientific research ===
A distinguishing feature of scientific thinking is the search for confirming or supportive evidence (inductive reasoning) as well as falsifying evidence (deductive reasoning). Many times in the history of science, scientists have resisted new discoveries by selectively interpreting or ignoring unfavorable data. Several studies have shown that scientists rate studies that report findings consistent with their prior beliefs more favorably than studies reporting findings inconsistent with their previous beliefs. However, assuming that the research question is relevant, the experimental design adequate and the data are clearly and comprehensively described, the empirical data obtained should be important to the scientific community and should not be viewed prejudicially, regardless of whether they conform to current theoretical predictions. In practice, researchers may misunderstand, misinterpret, or not read at all studies that contradict their preconceptions, or wrongly cite them anyway as if they actually supported their claims. Further, confirmation biases can sustain scientific theories or research programs in the face of inadequate or even contradictory evidence. The discipline of parapsychology is often cited as an example. An experimenter's confirmation bias can potentially affect which data are reported. Data that conflict with the experimenter's expectations may be more readily discarded as unreliable, producing the so-called file drawer effect. To combat this tendency, scientific training teaches ways to prevent bias. For example, experimental design of randomized controlled trials (coupled with their systematic review) aims to minimize sources of bias. The social process of peer review aims to mitigate the effect of individual scientists' biases, even though the peer review process itself may be susceptible to such biases. Confirmation bias may thus be especially harmful to objective evaluations regarding nonconforming results since biased individuals may regard opposing evidence to be weak in principle and give little serious thought to revising their beliefs. Scientific innovators often meet with resistance from the scientific community, and research presenting controversial results frequently receives harsh peer review.
=== Finance ===
Confirmation bias can lead investors to be overconfident, ignoring evidence that their strategies will lose money. In studies of political stock markets, investors made more profit when they resisted bias. For example, participants who interpreted a candidate's debate performance in a neutral rather than partisan way were more likely to profit. To combat the effect of confirmation bias, investors can try to adopt a contrary viewpoint "for the sake of argument". In one technique, they imagine that their investments have collapsed and ask themselves why this might happen.