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 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 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 decision-making strategy where individuals are prompted to make a choice rather than defaulting to a pre-set option. Useful for increasing user engagement and ensuring intentional decision-making.
The process by which a measure or metric comes to replace the underlying objective it is intended to represent, leading to distorted decision-making. Important for ensuring that metrics accurately reflect true objectives and designing systems that prevent metric manipulation.
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 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.
The principle that the more a metric is used to make decisions, the more it will be subject to corruption and distort the processes it is intended to monitor. Important for understanding the limitations and potential distortions of metrics in design and evaluation.
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.
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 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 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 how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
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 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.
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 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.
A strategic approach where multiple potential solutions are tested to identify the most promising one. Crucial for innovation and reducing risk in decision-making.
A prioritization technique where stakeholders use a limited budget to "buy" features they believe are most valuable, helping to prioritize the development roadmap. Useful for involving stakeholders in the decision-making process and aligning development priorities with business value.
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.
Strengths, Weaknesses, Opportunities, and Threats (SWOT) is a strategic planning tool that is applied to a business or project. Essential for strategic planning and decision-making.
A philosophy that emphasizes reason and logic as the primary sources of knowledge and truth. Useful for understanding the foundations of logical thinking and decision-making in design and development.
A dark pattern where users are pressured to make quick decisions by creating a false sense of urgency. Designers must avoid creating artificial urgency and allow users to make decisions at their own pace.
Also known as Parkinson's Law of Triviality, is the tendency to spend excessive time on trivial details while neglecting more important issues. Crucial for improving project management and team efficiency.
A prioritization framework used in product management to evaluate features based on Reach, Impact, Confidence, and Effort. Crucial for making informed decisions about which product features to prioritize and develop.
The process of gathering and analyzing information about competitors to inform business strategy and decision-making. Essential for understanding market positioning and developing effective competitive strategies.
Impact, Confidence, and Ease of implementation (ICE) is a prioritization framework used in product management to evaluate features. Essential for making informed and strategic decisions about feature development and prioritization.
A theoretical framework in economics that assumes individuals act rationally and seek to maximize utility, used to predict economic behavior and outcomes. Important for understanding traditional economic theories and designing systems that account for rational 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 way information is presented to users, which can significantly influence their decisions and perceptions. Important for designing messages and interfaces that guide user choices effectively.
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
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 degree to which the operations and decisions of an AI system are understandable and explainable to users. Crucial for building trust and ensuring ethical AI use.
The process of predicting how one will feel in the future, which often involves biases and inaccuracies. Important for understanding user behavior and decision-making, aiding in the design of better user experiences.
Business Intelligence (BI) encompasses technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. Crucial for making data-driven decisions and improving business performance.
An organizational structure that emphasizes flexibility, employee initiative, and decentralized decision-making. Useful for fostering innovation and rapid response to changes within an organization.
Zero Moment of Truth (ZMOT) is a concept in marketing that refers to the point in the buying cycle when the consumer researches a product before the seller even knows they exist. Crucial for understanding consumer behavior and optimizing marketing strategies to influence decision-making at this early stage.
A theoretical concept in economics that portrays humans as rational and self-interested agents who aim to maximize their utility. Important for understanding economic decision-making and designing systems that align with rational behavior.
Environmental signals that influence behavior and decision-making, such as signage, prompts, or notifications. Useful for designing environments and systems that effectively guide user behavior.
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 set of cognitive processes that include working memory, flexible thinking, and self-control, crucial for planning, decision-making, and behavior regulation. Crucial for designing interfaces and experiences that support users' cognitive abilities.
A mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
The ability to intuitively understand what makes a product successful, including market needs, user experience, and competitive landscape. Important for making informed decisions that lead to successful product development.
A statistical measure that quantifies the amount of variation or dispersion of a set of data values. Essential for understanding data spread and variability, which helps in making informed decisions in product design and analysis.
Conversations with key stakeholders to gather insights, expectations, and feedback, ensuring their needs are understood and considered in the project. Essential for aligning project goals with stakeholder needs and obtaining valuable input for decision-making.
A statistical method used to predict a binary outcome based on prior observations, modeling the probability of an event as a function of independent variables. Essential for predicting categorical outcomes in digital product analysis and user behavior modeling.
A phenomenon where the winner of an auction tends to overpay due to emotional competition, leading to a less favorable outcome than anticipated. Important for understanding decision-making biases and designing systems that mitigate overbidding risks.
The process of enabling users to take control of their interactions with a product or system, enhancing their confidence and satisfaction. Crucial for designing systems that provide users with the tools and information they need to make informed decisions.
The study of finding the best solution from a set of feasible solutions. Crucial for improving efficiency and performance in design and development processes.
Data points that represent an individual's, team's, or company's performance in the sales process. Essential for tracking progress, identifying issues, and optimizing sales strategies.
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.
A theory that explains how individuals determine the causes of behavior and events, including the distinction between internal and external attributions. Crucial for understanding user behavior and designing experiences that address both internal and external factors.
Elements in a process that cause resistance or slow down user actions, which can lead to frustration or be used intentionally to prevent errors and encourage deliberate actions. Important for recognizing both the negative impact of unnecessary delays and the positive use of intentional friction to enhance user decision-making and reduce errors.
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 motivational theory suggesting that individuals are motivated to act based on the expected outcomes of their actions and the attractiveness of those outcomes. Important for understanding motivation and behavior, distinct from decision-making under uncertainty.
A theory that describes how individuals pursue goals using either a promotion focus (seeking gains) or a prevention focus (avoiding losses). Crucial for designing motivation strategies and understanding user behavior in goal pursuit.
Customer Advisory Board (CAB) is a group of key customers who provide feedback and insights to a company to help guide its strategic decisions. This group is crucial for aligning products and services with customer needs and expectations.
A principle that suggests people are more likely to comply with requests or follow suggestions from authority figures. Important for designing persuasive experiences and understanding user compliance.
The ability of users to influence the behavior and outcomes of a system or product, allowing them to interact with it according to their preferences. Essential for creating user-friendly interfaces that allow for flexibility and customization.
The use of data, algorithms, and machine learning to recommend actions that can achieve desired outcomes. Essential for optimizing decision-making and implementing effective strategies.