A cognitive bias where individuals overestimate their ability to control impulsive behavior, leading to overexposure to temptations. Important for designing systems that help users manage self-control and avoid overexposure to temptations.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
The tendency to overestimate how much our future preferences and behaviors will align with our current preferences and behaviors. Important for understanding user behavior and designing experiences that account for changes over time.
The tendency to search for, interpret, and remember information in a way that confirms one's preexisting beliefs or hypotheses. Crucial for understanding cognitive biases that affect user decision-making and designing interventions to mitigate them.
The tendency to give more weight to negative experiences or information than positive ones. Crucial for understanding user behavior and designing systems that balance positive and negative feedback.
A cognitive bias where people allow themselves to indulge after doing something positive, believing they have earned it. Important for understanding user behavior and designing systems that account for self-regulation.
A behavioral economics concept where people categorize and treat money differently depending on its source or intended use. Crucial for understanding financial behavior and designing systems that align with users' mental accounting practices.
A cognitive bias where people are less likely to spend large denominations of money compared to an equivalent amount in smaller denominations. Useful for designers to understand consumer behavior and design pricing strategies that consider spending biases.
The theory that people adjust their behavior in response to the perceived level of risk, often taking more risks when they feel more protected. Important for designing safety features and understanding behavior changes in response to risk perception.
A cognitive bias where individuals tend to avoid risks when they perceive potential losses more acutely than potential gains. Important for understanding decision-making behavior in users and designing systems that mitigate risk aversion.
A decision-making paradox that shows people's preferences can violate the expected utility theory, highlighting irrational behavior. Important for understanding inconsistencies in user decision-making and designing better user experiences.
A cognitive bias where individuals believe that past random events affect the probabilities of future random events. Important for designers to understand user decision-making biases related to randomness.
A cognitive bias where individuals overlook or underestimate the cost of opportunities they forego when making decisions. Crucial for understanding user decision-making behavior and designing systems that highlight opportunity costs.
A cognitive bias where individuals strengthen their beliefs when presented with evidence that contradicts them. Important for understanding user resistance to change and designing strategies to address and mitigate this bias.
A cognitive bias where individuals evaluate outcomes relative to a reference point rather than on an absolute scale. Essential for understanding decision-making and consumer behavior.
A cognitive bias where people disproportionately prefer smaller, immediate rewards over larger, later rewards. Important for understanding and designing around user decision-making and reward structures.
A cognitive bias where consumers change their preference between two options when presented with a third, less attractive option. Useful for designers to create choice architectures that effectively influence user decisions.
A psychological phenomenon where people do something primarily because others are doing it. Important for understanding social influences on user behavior and trends.
A cognitive bias where a person's subjective confidence in their judgments is greater than their objective accuracy. Crucial for understanding user decision-making and designing systems that account for overconfidence.
A decision-making strategy where individuals allocate resources proportionally to the probability of an outcome occurring, rather than optimizing the most likely outcome. Important for understanding decision-making behaviors and designing systems that guide better resource allocation.
The tendency for individuals to favor information that aligns with their existing beliefs and to avoid information that contradicts them. Crucial for understanding how users engage with content and designing systems that present balanced perspectives.
A phenomenon where individuals' preferences between options change when the options are presented in different ways or contexts. Important for understanding and designing around inconsistencies in user choices.
A logical fallacy where people assume that specific conditions are more probable than a single general one. Important for understanding and addressing cognitive biases in user behavior.
The study of psychology as it relates to the economic decision-making processes of individuals and institutions. Essential for understanding and influencing user decision-making and behavior in economic contexts.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
A cognitive bias where people overemphasize information that is placed prominently or in a way that catches their attention first. Crucial for designing interfaces and information displays that manage user attention effectively.
A cognitive bias where individuals favor others who are perceived to be similar to themselves, affecting judgments and decision-making. Crucial for understanding biases in team dynamics and decision-making processes among designers.
A cognitive bias where people's decisions are influenced by how information is presented rather than just the information itself. Crucial for designers to minimize bias in how information is presented to users.
A cognitive bias where people judge harmful actions as worse, or less moral, than equally harmful omissions (inactions). Important for understanding user decision-making and designing systems that mitigate this bias.
A cognitive bias where people overestimate the importance of information that is readily available. Essential for designers to understand and mitigate how easily accessible information can disproportionately influence decisions.
A cognitive bias where people judge the likelihood of an event based on its relative size rather than absolute probability. Important for understanding user decision-making biases and designing systems that present information accurately.
A cognitive bias where people prefer the option that seems to eliminate risk entirely, even if another option offers a greater overall benefit. Important for understanding decision-making and designing risk communication for users.
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
A cognitive bias where individuals give stronger weight to payoffs that are closer to the present time compared to those in the future. Important for understanding user time-related decision-making and designing systems that encourage long-term thinking.
A cognitive bias where individuals evaluate the value of bundled items differently than they would if the items were evaluated separately. Important for understanding user behavior and designing effective product bundles and pricing strategies.
The tendency to perceive a greater quantity as a better value, regardless of the actual utility. Important for understanding consumer behavior and designing effective marketing strategies.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
A cognitive bias where people favor members of their own group over those in other groups. Important for designing inclusive and equitable experiences for users.
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
A cognitive bias where people judge the likelihood of an event based on the size of its category rather than its actual probability. Crucial for designers to understand how category size influences user perception and decision-making processes.
A cognitive bias where group members tend to discuss information that everyone already knows rather than sharing unique information, leading to less effective decision-making. Important for understanding group dynamics and improving the quality of collaborative decision-making among designers.
A cognitive bias that causes people to believe they are less likely to experience negative events and more likely to experience positive events than others. Crucial for understanding user risk perception and designing systems that account for unrealistic optimism.
A cognitive bias where people focus on the most noticeable or prominent information while ignoring less conspicuous details. Important for understanding user decision-making and ensuring balanced presentation of information.
The tendency to believe that things will always function the way they normally have, often leading to underestimation of disaster risks. Important for understanding risk perception and designing systems that effectively communicate potential changes.
A cognitive bias where people prefer a greater variety of options when making simultaneous choices compared to sequential choices. Important for designers to consider user preferences for variety when designing choice architectures and product offerings.
A cognitive bias that occurs when conclusions are drawn from a non-representative sample, focusing only on successful cases and ignoring failures. Crucial for making accurate assessments and designing systems that consider both successes and failures.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
The tendency to recall past behavior in a way that aligns with current beliefs and attitudes. Crucial for understanding how memories and self-perception can be influenced by current perspectives.
A cognitive bias that leads individuals to prefer things to remain the same rather than change, often resisting new options or changes. Crucial for understanding resistance to change and designing strategies to overcome it among users.
A cognitive bias where individuals interpret others' behaviors as having hostile intent, even when the behavior is ambiguous or benign. Important for understanding user interactions and designing experiences that mitigate negative interpretations.
A cognitive bias where people prefer familiar things over unfamiliar ones, even if the unfamiliar options are objectively better. Useful for designing interfaces and products that leverage familiar elements to enhance user comfort.
The tendency for people's perception to be affected by their recurring thoughts at the time. Important for understanding how current thoughts influence user perception and decision-making.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
The tendency for individuals to give positive responses or feedback out of politeness, regardless of their true feelings. Crucial for obtaining honest and accurate user feedback.
A cognitive bias where people seek out more information than is needed to make a decision, often leading to analysis paralysis. Crucial for designing decision-making processes that avoid information overload for users.
The study of strategic decision making, incorporating psychological insights into traditional game theory models. Useful for understanding complex user interactions and designing systems that account for strategic behavior.
The tendency for individuals to recall information that is consistent with their current mood. Important for understanding how mood affects memory and designing experiences that account for emotional states.