Human Factors
The study of how humans interact with systems and products, focusing on improving usability and performance. Crucial for designing user-friendly systems and products.
172 topics found for:
“system behavior”
Machine Learning
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.
Present Bias
A cognitive bias where individuals give stronger weight to payoffs that are closer to the present time compared to those in the future. Important for understanding user time-related decision-making and designing systems that encourage long-term thinking.
Information Foraging
The theory that users search for information in a manner similar to animals foraging for food, aiming to maximize value while minimizing effort. Important for designing efficient and user-centered information retrieval systems.
CRM
Customer Relationship Management (CRM) is a strategy for managing an organization's relationships and interactions with current and potential customers. Essential for improving business relationships and driving sales growth.
Regret Aversion
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.
Game Theory
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.
Winner’s Curse
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.
Principle of Choices
The Principle of Choices is an information architecture guideline that emphasizes providing users with meaningful options to navigate and interact with a system. Crucial for enhancing user experience by ensuring users can easily find what they need without being overwhelmed.
Intentionality Bias
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
Omission Bias
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.
Recommendation Engine
A system that suggests products, services, or content to users based on their preferences and behavior. Essential for personalizing user experiences and increasing engagement and conversion rates.
Illusion of Control
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
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.
Goal Gradient Effect
The tendency for people to feel more motivated and accelerate their efforts as they get closer to achieving a goal. Important for designing systems that motivate users effectively.
Lead Management
The process of tracking and managing potential customers from initial contact through to sale. Important for ensuring that leads are properly engaged and converted.
Pessimism Bias
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
Self Efficacy
The belief in one's ability to succeed in specific situations or accomplish a task, influencing motivation and behavior. Crucial for designing systems that enhance user confidence and encourage goal achievement.
Crowding Out Effect
The phenomenon where external incentives diminish intrinsic motivation, leading to reduced performance or engagement. Important for designing motivational strategies that do not undermine intrinsic motivation.
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.
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.
RFM
Recency, Frequency, Monetary (RFM) analysis is a marketing technique used to evaluate and segment customers based on their purchasing behavior. Essential for targeting high-value customers and optimizing marketing strategies.
Content Ecology
The dynamic system of content creation, distribution, and interaction within an environment. Important for understanding how content flows and interacts within a system.
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.
Illusory Superiority
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.
Expected Utility Theory
A theory in economics that models how rational individuals make decisions under risk by maximizing the expected utility of their choices. Essential for understanding decision-making under risk.
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.
Decision Tree
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.
IxD
Interaction Design (IxD) focuses on creating engaging interfaces with well-thought-out behaviors. Crucial for ensuring intuitive and effective user interactions.
Zero-Risk Bias
A cognitive bias where people prefer the option that seems to eliminate risk entirely, even if another option offers a greater overall benefit. Important for understanding decision-making and designing risk communication for users.
Interactive Design
The design of interactive digital products, environments, systems, and services. Crucial for creating engaging and user-friendly digital experiences.
Information Processing
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.
User Empowerment
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.
Motivation Wave
A concept describing how motivation fluctuates over time, influenced by various factors such as goals, rewards, and external circumstances. Crucial for designing systems that align with users' motivational states to maximize engagement and productivity.
Belief Perseverance
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.
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.
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.
Backfire Effect
A cognitive bias where individuals strengthen their beliefs when presented with evidence that contradicts them. Important for understanding user resistance to change and designing strategies to address and mitigate this bias.
AI Temperature
A parameter that controls the randomness of AI-generated text, affecting creativity and coherence. Important for fine-tuning the behavior and output of AI models.
Adaptive Control of Thought
A cognitive architecture model that explains how humans can learn and adapt to new tasks. Useful for understanding user learning and behavior adaptation, informing better user experience design.
Decision Utility
The value or satisfaction derived from a decision, influencing the choices people make. Crucial for understanding user preferences and designing experiences that maximize satisfaction.
Social Loafing
The tendency for individuals to put in less effort when working in a group compared to when working alone, due to reduced accountability. Crucial for understanding group dynamics and designing systems that ensure individual accountability.
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.
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.
Pattern Recognition
The ability to identify and interpret patterns in data, often used in machine learning and cognitive psychology. Crucial for designing systems that leverage pattern recognition for predictive analytics and user interactions.
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.
Normal Data Distribution
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
Ratio Bias
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.
Proportionality Bias
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
Hard-Easy Effect
A cognitive bias where people overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
Google Effect
The tendency to forget information that can be easily found online, also known as digital amnesia. Important for understanding how access to information impacts memory and designing experiences accordingly.
Selective Exposure
The tendency for individuals to favor information that aligns with their existing beliefs and to avoid information that contradicts them. Crucial for understanding how users engage with content and designing systems that present balanced perspectives.
Reciprocity
A social norm of responding to a positive action with another positive action, fostering mutual benefit and cooperation. Important for designing user experiences and systems that encourage positive reciprocal interactions.
System One
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.
KOS
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
Turing Test
A test proposed by Alan Turing to determine if a machine's behavior is indistinguishable from that of a human. Important for evaluating the intelligence of AI systems.
Anomaly Detection
The process of identifying unusual patterns or outliers in data that do not conform to expected behavior. Crucial for detecting fraud, errors, or other significant deviations in various contexts.
POLA
Principle of Least Astonishment (POLA) is a design guideline stating that interfaces should behave in a way that users expect to avoid confusion. Crucial for enhancing user experience and reducing the learning curve in digital products.
Reflexion
The process of self-examination and adaptation in AI systems, where models evaluate and improve their own outputs or behaviors based on feedback. Crucial for enhancing the performance and reliability of AI-driven design solutions by fostering continuous learning and improvement.
RLHF
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.