Definition of value noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Value engineering
Value engineering (VE) is a systematic analysis of the functions of various components and materials to lower the cost of goods, products and services with a tolerable loss of performance or functionality. Value, as defined, is the ratio of function to cost. Value can therefore be manipulated by either improving the function or reducing the cost. It is a primary tenet of value engineering that basic functions be preserved and not be reduced as a consequence of pursuing value improvements. The term "value management" is sometimes used as a synonym of "value engineering", and both promote the planning and delivery of projects with improved performance.
Visualize Value — Ideas, made visible
An ongoing practice of turning ideas into images. Visual ideas about work, markets, technology, culture, and human behavior by Jack Butcher.
values based software talks
Talk #3 Files, algorithms, media, software as lived practice, and personal software
Money Illusion
Abstract. The term “money illusion” refers to a tendency to think in terms of nominal rather than real monetary values. Money illusion has significant impl
Value and vulnerability: a framework for understanding the complexity of misinformation use
Why does misinformation influence some and not others? Vulnerability is often mischaracterized as personal weakness or deficiency, or as susceptibility to “infection” or “pollution”—dehumanizing descriptions that highlight supposed shortcomings of media consumers. We propose a broader perspective that focuses on the value of misinformation to persons, groups, and platforms that adopt or share it. The Vulnerability and Value (VV) framework conceptualizes vulnerability as arising from interconnected scales and systems of valuation. Using a complex adaptive systems framing, we describe how misinformation can generate value for individuals, groups, and platforms. When misinformation spreads successfully, it often does so because it serves purposes that extend beyond veracity. Its spread indicates something significant is at stake for those who believe or facilitate it. A key contribution of the VV framework is to formalize the tradeoff individuals face: whether to question and evaluate misinformation or to accept and share it.
Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions
AI assistants can impart value judgments that shape people's decisions and worldviews, yet little is known empirically about what values these systems rely on in practice. To address this, we develop a bottom-up, privacy-preserving method to extract the values (normative considerations stated or demonstrated in model responses) that Claude 3 and 3.5 models exhibit in hundreds of thousands of real-world interactions. We empirically discover and taxonomize 3,307 AI values and study how they vary by context. We find that Claude expresses many practical and epistemic values, and typically supports prosocial human values while resisting values like "moral nihilism". While some values appear consistently across contexts (e.g. "transparency"), many are more specialized and context-dependent, reflecting the diversity of human interlocutors and their varied contexts. For example, "harm prevention" emerges when Claude resists users, "historical accuracy" when responding to queries about controversial events, "healthy boundaries" when asked for relationship advice, and "human agency" in technology ethics discussions. By providing the first large-scale empirical mapping of AI values in deployment, our work creates a foundation for more grounded evaluation and design of values in AI systems.
Valuing What Counts: Framework to Progress Beyond Gross Domestic Product: Our Common Agenda Policy Brief 4
On the Impact of the Utility in Semivalue-based Data Valuation
Semivalue–based data valuation uses cooperative‐game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the...
Data Valuation using Reinforcement Learning
Quantifying the value of data is a fundamental problem in machine learning and has multiple important use cases: (1) building insights about the dataset and task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. We propose Data Valuation using Reinforcement Learning (DVRL), to adaptively learn data values jointly with the predictor model. DVRL uses a data value estimator (DVE) to learn how likely each datum is used in training of the predictor model. DVE is trained using a reinforcement signal that reflects performance on the target task. We demonstrate that DVRL yields superior data value estimates compared to alternative methods across numerous datasets and application scenarios. The corrupted sample discovery performance of DVRL is close to optimal in many regimes (i.e. as if the noisy samples were known apriori), and for domain adaptation and robust learning DVRL significantly outperforms state-of-the-art by 14.6% and 10.8%, respectively.
CMV: The labor theory of value is flawed
72 votes, 407 comments. This might be an obscure topic, however, in some—largely Marxist circles— the approach seems to motivate much of the dialogue…
Dynamic Pricing: What It Is & Why It's Important | HBS Online
Are you reevaluating your digital platform’s pricing model? Here’s an overview of dynamic pricing and why it’s important to your business.
No cost, no value | jola.dev
Why generating code makes it meaningless, why writing code by hand has value, and why toil is a critical part of the human experience.
On being valued - Emelia’s Ramblings
What does it mean to be valued, have I found the right room?
