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.
An approach to design that explores and creates provocative scenarios of future possibilities to stimulate discussion and critical thinking about the direction of design and society. Important for pushing the boundaries of conventional design thinking and envisioning future implications.
A planning method that starts with defining a desirable future and then works backwards to identify steps to achieve that future. Important for strategic planning and setting long-term goals in design and development.
Narrative descriptions of how users might interact with a product or system to achieve specific goals, used to inform design and development. Important for understanding user needs and ensuring the design supports their tasks and goals.
The extent to which individuals or organizations plan for and consider the long-term consequences of their actions. Crucial for designing strategies and products that are sustainable and adaptable over time.
Artificial Superintelligence (ASI) is a hypothetical AI that surpasses human intelligence and capability in all areas. Important for understanding the potential future impacts and ethical considerations of AI development.
Numeronym for the term "10,000 Concurrent Clients", the challenge of optimizing network software to handle ten thousand simultaneous client connections. Important for ensuring scalability and performance in high-demand scenarios.
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.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
A research technique that explores the context in which users interact with a product, service, or environment to understand their needs and behaviors. Crucial for gaining deep insights into user contexts and designing more relevant solutions.
A method of comparing two versions of a webpage or app to see which performs better in terms of user engagement or conversions. Crucial for designers and product managers to test variations and optimize user experience and performance.
User Acceptance Testing (UAT) is the final phase of the software testing process where actual users test the software to ensure it meets their requirements. Crucial for validating that the software functions correctly in real-world scenarios before its release.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
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 tool used during brainstorming sessions to prompt and inspire creative thinking, often containing questions, scenarios, or constraints. Useful for facilitating structured ideation sessions and sparking new ideas.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
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.
A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. Important for understanding basic algorithmic principles and their applications.
Behavior-Driven Development (BDD) is a software development approach where applications are specified and designed by describing their behavior. Important for ensuring clear communication and shared understanding between developers and stakeholders.
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 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 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 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.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
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 phenomenon where people have a reduced ability to recall the last items in a list when additional, unrelated information is added at the end. Crucial for designing information presentation to optimize memory retention.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
A creative thinking technique where the typical process is reversed to generate new ideas by considering the opposite of conventional assumptions. Useful for fostering innovation and challenging existing assumptions in problem-solving.
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.
The study of structures of consciousness as experienced from the first-person point of view. Important for designing user experiences that are intuitive and empathetic.
A statistical technique that uses several explanatory variables to predict the outcome of a response variable, extending simple linear regression to include multiple input variables. Crucial for analyzing complex relationships in digital product data.
A problem-solving method that explores all possible solutions by examining the structure and relationships of different variables. Useful for generating innovative design solutions and exploring a wide range of possibilities in digital product development.
A statistical technique that uses random sampling and statistical modeling to estimate mathematical functions and simulate systems. Useful for risk assessment, decision-making, and performance optimization in digital product design.
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.
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.