An inference method used in AI and expert systems where reasoning starts from the goal and works backward to determine the necessary conditions. Important for developing intelligent systems that can solve complex problems by working from desired outcomes.
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.
An inference method used in AI and expert systems where reasoning starts from known facts and applies rules to derive new facts. Important for developing intelligent systems that can build knowledge and solve problems incrementally in digital products.
A problem-solving process that includes logical reasoning, pattern recognition, abstraction, and algorithmic thinking. Important for developing efficient and effective solutions in digital product design and development.
A logical fallacy in which it is assumed that qualities of one thing are inherently qualities of another, due to an irrelevant association. Important for avoiding incorrect associations in user research and data interpretation.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
The persistence of misinformation in memory and influence on reasoning, even after it has been corrected. Crucial for understanding and mitigating the impact of misinformation in design and communication.
The study of mental processes such as perception, memory, reasoning, and problem-solving. Important for designing interfaces that align with how users process information and make 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 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 principle that suggests the simplest explanation is often the correct one, favoring solutions that make the fewest assumptions. Crucial for problem-solving and designing straightforward, efficient solutions.
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.
Situation-Complication-Resolution (SCR) is a communication and problem-solving framework used to structure information clearly and logically. Crucial for effectively conveying complex ideas and solutions in business and design contexts.
A psychological theory that predicts an individual's behavior based on their intention, which is influenced by their attitudes and subjective norms. Important for understanding and predicting user behavior and designing interventions to influence actions.
Case-Based Reasoning (CBR) is an AI method that solves new problems based on the solutions of similar past problems. This approach is essential for developing intelligent systems that learn from past experiences to improve problem-solving capabilities.
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.
Reasons to Believe (RTB) is a marketing concept that refers to the evidence or arguments that support a product's claims and persuade consumers of its benefits. Essential for building trust and credibility with customers.
A marketing concept that describes brands that inspire loyalty beyond reason, creating an emotional connection with consumers. Crucial for building strong brand loyalty and emotional engagement.
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.
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.
The objective analysis and evaluation of an issue in order to form a judgment. Essential for making informed and rational design decisions.
Minimum Marketable Feature (MMF) is the smallest set of functionality that delivers significant value to users and can be marketed effectively. Crucial for prioritizing development efforts and releasing valuable product increments quickly, balancing user needs with business objectives.
Jobs-To-Be-Done (JTBD) is a framework that focuses on understanding the tasks users are trying to accomplish with a product, emphasizing their goals and motivations over product features. Crucial for designing products that meet real user needs and motivations.
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 logical fallacy that occurs when one assumes that what is true for a part is also true for the whole. Important for avoiding incorrect assumptions in design and decision-making.
The tendency for people to defer purchasing decisions to a later time, often leading to procrastination. Important for understanding consumer behavior and optimizing sales strategies.
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 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.
Net Promoter Score (NPS) is a metric used to measure customer loyalty and satisfaction based on their likelihood to recommend a product or service to others. Crucial for gauging overall customer sentiment and predicting business growth through customer advocacy.