Decision Staging
The process of breaking down decisions into smaller, manageable stages to simplify the decision-making process. Useful for guiding users through complex decisions in a structured manner.
The process of breaking down decisions into smaller, manageable stages to simplify the decision-making process. Useful for guiding users through complex decisions in a structured manner.
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.
Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes in a workflow, using standardized symbols and notations. Essential for creating clear, standardized diagrams that facilitate understanding and communication of business processes in digital product design.
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 concept that humans make decisions within the limits of their knowledge, cognitive capacity, and available time, leading to satisficing rather than optimal solutions. Crucial for designing systems and processes that account for human cognitive limitations and decision-making processes.
Emotional states where individuals are calm and rational, often contrasted with hot states where emotions run high. Important for understanding decision-making processes and designing experiences that accommodate both states.
A mode of thinking, derived from Dual Process Theory, that is fast, automatic, and intuitive, often relying on heuristics and immediate impressions. Important for understanding how users make quick decisions and respond to design elements instinctively, aiding in the creation of intuitive and user-friendly interfaces.
A framework suggesting there are two systems of thinking: System 1 (fast, automatic) and System 2 (slow, deliberate), influencing decision-making and behavior. Crucial for understanding how users process information and make decisions.
The design of environments in which people make decisions, influencing their choices and behaviors. Important for creating user experiences that guide decision-making processes effectively.
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 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 mode of thinking, derived from Dual Process Theory, that is slow, deliberate, and analytical, requiring more cognitive effort and conscious reasoning. Crucial for designing complex tasks and interfaces that require thoughtful decision-making and problem-solving, ensuring they are clear and logical for users.
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.
Decision-making strategies that use simple heuristics to make quick, efficient, and satisfactory choices with limited information. Important for designing user experiences that support quick and efficient decision-making.
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 situation in which an individual is unable to make a decision due to the overwhelming number of options available. Important for designing interfaces that streamline decision-making processes for users.
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.
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 process where ideas are brought together to find a single, best solution to a problem. Important for problem-solving and decision-making in design processes.
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 practice of organizing the context in which people make decisions to influence the outcomes, often used to nudge users towards certain behaviors. Crucial for designing user experiences that guide decision-making and improve outcomes.
A rule-of-thumb or shortcut that simplifies decision-making and problem-solving processes. Essential for designing user-friendly interfaces that facilitate quick and efficient decision-making.
The objective analysis and evaluation of an issue in order to form a judgment. Essential for making informed and rational design decisions.
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 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 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 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.
The systematic approach to managing innovation processes, from idea generation to implementation. Crucial for effectively harnessing creativity and ensuring successful innovation outcomes.
Drivers, Approvers, Contributors, and Informed (DACI) is a responsibility assignment framework that clarifies roles and responsibilities. Essential for making clear and effective decisions in collaborative environments.
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 structured communication technique originally developed as a systematic, interactive forecasting method which relies on a panel of experts. Important for gathering expert opinions and making informed 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.
A prioritization method that assigns different weights to criteria based on their importance, helping to make informed decisions and prioritize tasks effectively. Crucial for making objective and balanced decisions in project management and product development.
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.
Business Rules Engine (BRE) is a software system that executes one or more business rules in a runtime production environment. Crucial for automating decision-making processes and ensuring consistency and compliance in digital products.
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.
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.
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.
The deteriorating quality of decisions made by an individual after a long session of decision making, due to mental exhaustion. Important for designing interfaces that minimize cognitive load and simplify decision processes.
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 state of overthinking and indecision that prevents making a choice, often due to too many options or uncertainty. Important for designing interfaces that simplify decision-making processes for users.
The series of actions or operations involved in the acquisition, interpretation, storage, and retrieval of information. Crucial for understanding how users handle information and designing systems that align with cognitive processes.
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 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 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 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.
A principle that states the time it takes to make a decision increases with the number and complexity of choices available. Crucial for designing user interfaces that minimize cognitive load and enhance decision-making efficiency.
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 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.
Representativeness is a heuristic in decision-making where individuals judge the probability of an event based on how much it resembles a typical case. Crucial for understanding biases in human judgment and improving decision-making processes.
A cognitive shortcut that relies on the recognition of one option over another to make a decision, often used when individuals have limited information. Crucial for designing interfaces and experiences that facilitate quick and effective decision-making.
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.
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.
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.
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.
The practice of using data analytics and metrics to make informed decisions, focusing on measurable outcomes and efficiency rather than intuition or traditional methods. Important for optimizing design processes, improving product performance, and making data-driven decisions that enhance user experience and business success.
A behavioral economics model that explains decision-making as a conflict between a present-oriented "doer" and a future-oriented "planner". Useful for understanding user decision-making and designing interventions that balance short-term and long-term goals.
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.
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 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.