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.
202 topics found for:
“decision support”
Choice-Supportive Bias
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.
Take the Best Heuristic
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.
McNamara Fallacy
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
Bounded Rationality
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.
Fast and Frugal Heuristics
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.
Augmented Intelligence
A design pattern that combines human and machine intelligence to enhance decision-making and problem-solving. Important for leveraging AI to support and amplify human capabilities.
Availability Heuristic
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.
Critical Thinking
The objective analysis and evaluation of an issue in order to form a judgment. Essential for making informed and rational design decisions.
Knowledge Engineering
The process of integrating knowledge into computer systems to solve complex problems, often used in AI development. Important for developing intelligent systems that can perform complex tasks and support decision-making in digital products.
OODA Loop
Observe, Orient, Decide, and Act (OODA) is a decision-making framework often used in strategic planning and rapid response situations. Crucial for agile decision-making and strategic planning in dynamic environments.
System Two
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.
Executive Function
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.
Weighted Scoring
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.
Delphi Method
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.
DACI Framework
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.
Prescriptive Analytics
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.
Mapping
The process of creating visual representations of data or information to enhance understanding and decision-making. Essential for organizing information and making complex data accessible.
Competitive Intelligence
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.
Infomediary
An intermediary that gathers and provides information to users, typically in an online context. Important for helping users make informed decisions based on aggregated data.
Standard Deviation
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.
Friction
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.
Human-AI Collaboration
The combined efforts of humans and AI systems to achieve better outcomes than either could alone. Important for leveraging the strengths of both humans and AI in various tasks.
Decision Fatigue
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.
Decision Theory
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.
Decision Paralysis
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.
Illusion of Validity
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.
Probability Matching
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.
Ambiguity Effect
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.
Sunk Cost Fallacy
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.
Cost-Benefit
An analysis comparing the costs and benefits of a decision or project to determine its feasibility and value. Important for making informed business and design decisions.
Escalation of Commitment
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.
Opportunity Cost Neglect
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.
Naive Allocation
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.
Dual Self Model
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.
Irrational Escalation
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.
Information Bias
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.
Fist To Five
A consensus-building technique where participants show their level of agreement or support by raising zero to five fingers. Useful for quickly gauging team agreement and making collaborative decisions in product design and development meetings.
Intertemporal Choice
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.
Myopic Loss Aversion
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.
Gambler’s Fallacy
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.
Dual Process Theory
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.
Viability Risk
The risk that the product will not be financially or strategically sustainable for the business, potentially leading to a lack of support or profitability. Essential for ensuring that the product aligns with business goals and can be maintained and supported long-term.
Scenario Mapping
A method used to create detailed narratives of potential future events to explore and understand possible outcomes and inform decision-making. Essential for strategic planning and anticipating the impact of different decisions or changes.
Subadditivity Effect
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.
Content-First Design
An approach to design where content is prioritized and designed before other elements like layout and visual design. Crucial for ensuring that the design supports and enhances the content.
Design Tradeoffs
The compromises made between different design options, balancing various factors like usability, aesthetics, and functionality. Essential for making informed decisions that optimize overall design effectiveness.
Overconfidence Effect
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.
Risk-as-Feelings Theory
A theory that emphasizes the role of emotions in risk perception and decision-making, where feelings about risk often diverge from cognitive assessments. Important for designing systems that account for emotional responses to risk and improve decision-making.
Framing Bias
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.
Analytics
The systematic computational analysis of data or statistics to understand and improve business performance. Essential for data-driven decision making in design, product management, and marketing.
Priming
A psychological effect where exposure to one stimulus influences the response to a subsequent stimulus, without conscious guidance or intention. Crucial for designing experiences that subtly guide user behavior and decision-making.
Crystallized Intelligence
The ability to use learned knowledge and experience, often increasing with age and accumulated learning. Important for understanding how expertise and knowledge accumulation impact design and decision-making.
Active Choice
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.
Optimism Bias
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.
Rationalism
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.
Descriptive Analytics
The interpretation of historical data to identify trends and patterns. Important for understanding past performance and informing future decision-making.
Quantitative Research
A research method that focuses on collecting and analyzing numerical data to identify patterns, relationships, and trends, often using surveys or experiments. Essential for making data-driven decisions and validating hypotheses with statistical evidence.
ICE Scoring Model
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.
Big Data Engineer
A professional who designs, builds, and maintains systems for processing large-scale data sets. Essential for enabling data-driven decision-making and supporting advanced analytics in organizations.