Trace the map of reasonable disagreement. Break arguments into statements, rate confidence and importance, and find the crux.
Argumentation theory | Communication and Mass Media | Research Starters | EBSCO Research
<p>Argumentation theory explores the processes and methods of reasoning and debate used by individuals in both formal and informal contexts. The theory has roots in ancient philosophical discourse, particularly from figures like Aristotle, and has evolved through the contributions of modern philosophers such as Chaïm Perelman and Stephen Toulmin. It highlights how arguments are structured, identifying key components such as claims, grounds (or data), and warrants, which collectively help participants make their case. </p> <p>Additionally, arguments can be categorized into three main types: factual claims, which are verifiable; judgment or value claims, which are subjective; and policy claims, which pertain to proposed courses of action. This framework acknowledges the influence of personal biases, often shaping the reasoning process, and emphasizes the importance of logical support, backing, qualifiers, and rebuttals in strengthening arguments. In academic contexts, the theory suggests that creating valid topics should focus on policy arguments, while also addressing counterarguments to foster a comprehensive debate. Overall, argumentation theory serves as a critical tool for understanding how reasoning and persuasive communication function in various scenarios.</p>
Debate Maps — The Society Library
While The Society Library is dedicated to enabling access to information through our libraries, we recognize the difficult and tricky work of organizing that content into formal deliberation. There are hundreds of cognitive biases and logical pitfalls that can get in our way as human beings. Therefore, as a part of our service, The Society Library endeavors to map the knowledge we collect into “debate maps,” which essentially means we are organizing the arguments, claims, evidence, and opinions from all points of view into a formal debate - and effectively enabling “societal-scale debate.” A feat which may be otherwise impossible to organize in comparable levels of comprehensiveness with the existing limits of media, language, and human bandwidth to which we are confined.
Back-to-basics: on poor conceptualizations in AI work - (Un)rigorous AI
TL;DR — Poor conceptual foundations can severely undermine the credibility and reliability of knowledge claims. (And, no, your metric is not your construct.)
Operadic consistency: a label-free signal for compositional...
Detecting LLM reasoning failures at inference time without ground-truth labels has motivated a wide range of confidence baselines, including self-consistency, semantic entropy, and P(True), built...
We argue badly, and nothing accumulates. How could we do better? | Reason Commons — Issue Trees & Logical Thinking Process
Millions of people argue every day about the things that matter most — climate change, what to do about AI, how we might build a better world. Some of it is sharp, even insightful. And almost none of it accumulates.
Most arguments scatter across papers, posts, and threads — and evaporate. A claim tree gives them a stable structure to gather against, the way a cathedral gathers centuries of work into a single, standing thing.
All 12 Tips for Concision | LEGIBLE
Since July 2015 I’ve been sporadically posting a series of tips for concision in legal writing. I suggested a total of twelve, and links to all of them are collected here:
Ask don't tell: Reducing sycophancy in large language models
Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-stakes advisory and...
Establishing trust in automated reasoning - MetaROR
Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by early practitioners of computational science, but was later forgotten. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures.
The Case Against Formal Verification, 50 Years Later - Ivan Gavran
Writings on software correctness, AI, formal verification, and other technical topics.
When benchmarks go bad - what I learned from measuring performance wrong - Holly Cummins
The world of performance analysis is littered with flawed claims, cognitive biases, dangerous intuitions, and beguiling fallacies. Sadly…
Ok, here's my map of this whole argument including my assumption of how the participants would rate each claim. fenc.es/Flo/infrasound-harms-at-dc-le… If you want to convince me that infrasound is harmful, start here to figure out which is the actual crux between us: fenc.es/Flo/infrasound-harms-at-dc-le…
Flo 🔶
If infrasound causes harm, I want to believe that it causes harm. If infrasound is not harmful I want to believe that it is not harmful. Do not let me believe things that aren’t true.
