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 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 attribute positive qualities to one's own choices and downplay the negatives, enhancing post-decision satisfaction. Useful for understanding user satisfaction and designing experiences that reinforce positive decision outcomes.
A cognitive bias where individuals overestimate their own abilities, qualities, or performance relative to others. Important for understanding user self-perception and designing systems that account for inflated self-assessments.
The tendency to perceive and interpret information based on prior experiences and expectations, influencing how different users perceive design differently. Important for designing interfaces that meet user expectations, improving usability and intuitive navigation.
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 people prefer a smaller set of higher-quality options over a larger set with lower overall quality. Useful for designing product offerings and experiences that emphasize quality over quantity for users.
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 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.
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 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 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 individuals tend to focus on positive information or events more than negative ones, especially as they age. Useful for understanding user preferences and designing experiences that emphasize positive outcomes.
A cognitive bias that limits a person to using an object only in the way it is traditionally used. Important for designers to foster creative problem-solving and innovation.
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 cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
A cognitive bias where repeated statements are more likely to be perceived as true, regardless of their actual accuracy. Crucial for understanding how repetition influences beliefs and designing communication strategies for users.
The perception of a relationship between two variables when no such relationship exists. Crucial for understanding and avoiding biases in data interpretation and decision-making.
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
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 cognitive bias where people remember scenes as being more expansive than they actually were. Important for understanding how users perceive and recall visual information, aiding in better visual design decisions.
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 ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
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.
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 judge an experience largely based on how they felt at its peak (most intense point) and its end, rather than the total sum of the experience. Crucial for designing memorable and satisfying user experiences.
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 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.
A phenomenon where vivid mental images can interfere with actual perception, causing individuals to mistake imagined experiences for real ones. Important for ensuring that marketing and product design set realistic user expectations to avoid disappointment and maintain brand integrity.
The tendency for individuals to continue a behavior or endeavor as a result of previously invested resources (time, money, or effort) rather than future potential benefits. Important for understanding decision-making biases and designing systems that help users avoid irrational persistence.
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.
The change in opinions or behavior that occurs when individuals conform to the information provided by others. Important for understanding social dynamics and designing systems that leverage social proof and peer influence.
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 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.
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.
The tendency for the first items presented in a sequence to be remembered better than those in the middle. Crucial for designing information presentation and improving memory retention.
A cognitive bias where people tend to remember the first and last items in a series better than those in the middle, impacting recall and memory. Crucial for designing information presentation to optimize user memory and recall.
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.
The tendency to cling to one's beliefs even in the face of contradictory evidence. Important for understanding resistance to change and designing interventions that address this bias.
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 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.
A cognitive bias where bizarre or unusual information is better remembered than common information. Useful for designers to create memorable and engaging user experiences by incorporating unique elements.
A heuristic where individuals evenly distribute resources across all options, regardless of their specific needs or potential. Useful for understanding and designing around simplistic decision-making strategies.
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.
A phenomenon where people fail to recognize a repeated item in a visual sequence, impacting information processing and perception. Important for understanding visual perception and designing interfaces that avoid repetitive confusion.
A strategy where an additional, less attractive option is introduced to make other pricing options look more appealing, often steering customers towards a particular choice. Important for guiding user decisions and increasing the perceived value of targeted pricing tiers.
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 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.
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
A cognitive bias where people attribute group behavior to the characteristics of the group members rather than the situation. Crucial for understanding team dynamics and avoiding misattribution in collaborative settings.
The phenomenon where people continue a failing course of action due to the amount of resources already invested. Important for recognizing and mitigating biased decision-making.
The tendency for people to pay more attention to items placed in the center of a visual field. Crucial for designing layouts that maximize visibility and impact of key elements.
A behavioral economic theory that describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are known. Crucial for understanding decision-making under risk and designing systems that align with user behavior.
Information Visualization (InfoVis) is the study and practice of visual representations of abstract data to reinforce human cognition. Crucial for transforming complex data into intuitive visual formats, enabling faster insights and better decision-making.
A principle that suggests the simplest explanation is often the correct one, favoring solutions that make the fewest assumptions. Crucial for problem-solving and designing straightforward, efficient solutions.
A cognitive bias where people underestimate the complexity and challenges involved in scaling systems, processes, or businesses. Important for understanding the difficulties of scaling and designing systems that address these challenges.
A logical fallacy that occurs when one assumes that what is true for a part is also true for the whole. Important for avoiding incorrect assumptions in design and decision-making.
Explainable AI (XAI) are AI systems that provide clear and understandable explanations for their decisions and actions. This transparency is crucial for building trust and confidence in AI applications across various domains.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
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.