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.
Store Of Value vs Payment System - AVC
One of the debates that has raged inside and outside of the Bitcoin community since I got involved back in 2012 is whether Bitcoin was a store of value vs a means of payment. When I first started buying and owning Bitcoin, I would use it as a means of payment all the time. I […]
“Objective Economics” Isn’t.
Some time ago, in a Facebook argument, I encountered someone who repeated the old “exchange of unequals” argument (i.e., that people never exchange equivalents because each party values what they got more than what they traded for it). I responded that this argument confuses exchange value with use value and that, regardless of subjective valuation,...
Leadership: Values
A question I get quite frequently is “What makes a good leader?” One of the most important aspects of a good leader is that they have values. This may at first sound noncontroversial, but we are surrounded today by leaders who do not have values or whose only values are to get rich personally or to be powerful (or both). Tech investors have been especially guilty of enabling charismatic leaders, and sometimes even non-charismatic but wildly successful ones, who are fundamentally hollow and wi...
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.
Agents, Alignment, and the Many Faces of Autonomy
This article investigates the prospect of aligning AI agents with human autonomy. We show that doing so is a challenge: while there is general agreement on the concept’s broad contours, its components can be specified in a number of conflicting ways, making it difficult to decide how an autonomy-enhancing agent should act. In response, we introduce three strategies for dealing with disagreements about interpretations of autonomy. Each offers a coherent approach that captures a different set of considerations, with its own strength and trade-offs. The liberal approach is committed to user integrity and promotes human autonomy by acting strictly on what a person says they want, without trying to change their mind. The capability-boosting approach is built around the idea of flourishing and promotes human autonomy by empowering the user to act on their goals. The meta-autonomy approach prioritizes choice by giving the user the kind of autonomy they want in relation to their interactions with an AI agent. By highlighting the multifaceted nature of human autonomy, this article contributes to recent efforts seeking to identify and operationalize targets of AI value alignment. It also illustrates the challenges and trade-offs that can arise not just between—but even within—the values selected as targets for alignment and offers strategies to navigate them.
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…