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 decision-making rule where individuals choose the option with the highest perceived value based on the first good reason that comes to mind, ignoring other information. Crucial for understanding and designing for quick decision-making processes.
A psychological phenomenon where the desire for harmony and conformity in a group results in irrational or dysfunctional decision-making. Crucial for recognizing and mitigating the risks of poor decision-making in teams.
A decision-making strategy that involves choosing an option that meets the minimum requirements rather than seeking the optimal solution, balancing effort and outcome. Important for designing user experiences that accommodate decision-making under constraints.
A mental shortcut where current emotions influence decisions, often bypassing logic and reasoning. Important for understanding how emotions impact user decisions, aiding in more effective design and marketing.
A cognitive bias where people perceive past events as having been more predictable than they actually were. Important for understanding and mitigating biases in user feedback and decision-making.
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 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 overestimate the importance of information that is readily available. Essential for designers to understand and mitigate how easily accessible information can disproportionately influence decisions.
The tendency to judge the strength of arguments based on the believability of their conclusions rather than the logical strength of the arguments. Important for understanding cognitive biases that affect decision-making and user perceptions.
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
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 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.
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.
A cognitive bias where decision-making is affected by the lack of information or uncertainty. Important for understanding and mitigating user decision-making biases due to uncertainty or lack of information.
A cognitive bias where people ignore general statistical information in favor of specific information. Critical for designers to use general statistical information to improve decision-making accuracy and avoid bias.
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.
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 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.
Anchoring (also known as Focalism) is a cognitive bias where individuals rely heavily on the first piece of information (the "anchor") when making decisions. Crucial for understanding and mitigating initial information's impact on user decision-making processes.
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 mental shortcut that relies on immediate examples that come to mind when evaluating a specific topic, concept, method, or decision. Crucial for understanding how people make decisions and the biases that influence their choices.
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.
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 individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
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 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 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' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
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 tendency to overvalue new innovations and technologies while undervaluing existing or traditional approaches. Important for balanced decision-making and avoiding unnecessary risks in adopting new technologies.
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.
The tendency to overestimate the duration or intensity of the emotional impact of future events. Important for understanding user expectations and satisfaction.
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 attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
The study of how individuals make choices among alternatives and the principles that guide these choices. Important for designing decision-making processes and interfaces that help users make informed choices.
A phenomenon where group members make decisions that are more extreme than the initial inclination of its members due to group discussions and interactions. Crucial for understanding and mitigating the risks of extreme decision-making in group settings.
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 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 rely too heavily on their own perspective and experiences when making decisions. Important for designers to recognize and mitigate their own perspectives influencing design decisions.
A tendency to avoid making decisions that might lead to regret, influencing risk-taking and decision-making behaviors. Crucial for understanding decision-making processes and designing systems that minimize regret.
A cognitive bias where individuals overestimate the accuracy of their judgments, especially when they have a lot of information. Important for understanding and mitigating overconfidence in user decision-making.
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 individuals or organizations continue to invest in a failing project or decision due to the amount of resources already committed. Important for designers to recognize and mitigate their own risks of continuing unsuccessful initiatives.
A cognitive bias where people give greater weight to outcomes that are certain compared to those that are merely probable. Important for designers to consider how users weigh certain outcomes more heavily in their decision-making.
A cognitive bias where people perceive an outcome as certain while it is actually uncertain, based on how information is presented. Crucial for understanding and mitigating biased user decision-making.
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 people place too much importance on one aspect of an event, causing errors in judgment. Important for understanding decision-making and designing interfaces that provide balanced information.
The mistaken belief that a person who has experienced success in a random event has a higher probability of further success in additional attempts. Crucial for understanding and designing around user decision-making biases.
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.
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.
The study of how people make choices about what and how much to do at various points in time, often involving trade-offs between costs and benefits occurring at different times. Crucial for designing systems that account for delayed gratification and long-term planning.
A cognitive bias where people see patterns in random data. Important for designers to improve data interpretation and avoid false conclusions based on perceived random patterns.
A cognitive bias where individuals better remember the most recent information they have encountered, influencing decision-making and memory recall. Important for designing user experiences that leverage or mitigate the impact of recent information.
The cognitive bias where people treat a set of items as more significant when they are perceived as a cohesive group. Important for understanding user perception and decision-making.
The phenomenon where having too many options leads to anxiety and difficulty making a decision, reducing overall satisfaction. Important for designing user experiences that balance choice and simplicity to enhance satisfaction.
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 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 overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
A principle often used in behavioral economics that suggests people evaluate options based on relative comparisons rather than absolute values. Important for understanding decision-making and designing choices that highlight beneficial comparisons.