Tag: Philosophy of Language

Compilers vs. Conversation: Formal vs. Informal Logic

For the latest entry on iversonsoftware.com, we are looking under the hood of human reasoning to examine the two primary “engines” that drive our conclusions: Formal vs. Informal Logic. While one operates like a strict mathematical compiler, the other functions like a flexible natural language processor. Understanding the difference is the key to both writing perfect code and winning a high-stakes debate.

At Iverson Software, we deal with both strict syntax and user intent. In the world of philosophy, this same divide exists in how we process arguments. Formal Logic is the study of the structure of arguments, while Informal Logic is the study of arguments as they are used in everyday communication.

1. Formal Logic: The Mathematical Syntax

Formal logic (often called Symbolic Logic) is concerned entirely with the form or structure of an argument, rather than its specific content.

    • The Logic of Variables: It replaces words with symbols ($P$, $Q$, $\rightarrow$). It doesn’t care if $P$ stands for “The server is down” or “The moon is made of cheese”; it only cares if the relationship between $P$ and $Q$ is valid.

    • Deductive Certainty: If a formal argument is valid and the premises are true, the conclusion is 100% certain. There is no “opinion” involved—it is a mathematical necessity.

    • The Truth Table: In formal logic, we use tools like truth tables to map out every possible scenario for a set of propositions to ensure the logic never “breaks.”

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2. Informal Logic: The Semantic Processor

Informal logic deals with “Natural Language Arguments.” It’s the logic we use in legal cases, political debates, and business meetings.

  • The Power of Context: Unlike formal logic, informal logic cares deeply about the content, the tone, and the context of the speaker. It deals with nuances that symbols can’t capture.

  • Inductive Probability: Most informal arguments aren’t meant to be “certain”; they are meant to be cogent or persuasive. They provide a high degree of probability rather than an absolute proof.

  • Practical Application: Informal logic is where we study “Fallacies”—common errors in reasoning like the Straw Man or Slippery Slope—which occur because of how language is used, not just how it’s structured.

3. Key Differences: Accuracy vs. Utility

Feature Formal Logic Informal Logic
Medium Symbols and Math Natural Language
Focus Structural Validity Persuasive Strength
Output Certainty (True/False) Probability (Strong/Weak)
Environment Math, CS, Philosophy Law, Media, Daily Life

4. Which One Should You Use?

  • Use Formal Logic when “Bugs” are Fatal: When you are designing an algorithm, building a database schema, or constructing a mathematical proof, you need the absolute rigour of formal logic. A single “syntax error” in your logic can crash the entire system.

  • Use Informal Logic when “Nuance” is King: When you are negotiating a contract, leading a team, or analyzing a news report, you need informal logic. You must be able to detect emotional manipulation, evaluate the credibility of sources, and understand the “implied” meanings that symbols miss.

Why This Matters Today

In 2025, the gap between these two is closing. Neurosymbolic AI is the attempt to build machines that use Formal Logic (to be accurate) and Informal Logic (to understand human intent) simultaneously. By mastering both, you become a “Full-Stack Thinker”—someone who can build rigorous systems and navigate complex human environments with equal skill.

The Source Code of Morality: An Introduction to Meta-ethics

Continuing our philosophical journey on iversonsoftware.com, we move from the practical applications of Ethics to the deepest layer of moral inquiry: Meta-ethics. If Ethics is the “application layer” that tells us how to act, Meta-ethics is the “compiler” that examines the very nature, language, and logic of moral claims.

At Iverson Software, we are used to looking beneath the interface to understand the underlying logic of a system. Meta-ethics does exactly this for morality. Instead of asking “Is this action right?”, it asks: What does “right” even mean? Is morality a set of objective facts hard-coded into the universe, or is it a social construct we’ve developed to manage human behavior?

1. Moral Realism vs. Anti-Realism: Is Truth “Hard-Coded”?

The first major divide in meta-ethics concerns the existence of moral facts.

  • Moral Realism: The belief that moral truths are objective and independent of our opinions. Just as 2 + 2 = 4 is a mathematical fact, a realist believes that “murder is wrong” is a moral fact that exists whether we agree with it or not.

  • Moral Anti-Realism: The belief that there are no objective moral facts. Morality might be a matter of cultural preference (Relativism), individual feelings (Subjectivism), or a useful fiction we’ve created (Error Theory).

2. Cognitivism vs. Non-Cognitivism: The Language of Values

This debate focuses on what we are actually doing when we make a moral statement.

  • Cognitivism: When you say “stealing is wrong,” you are making a claim that can be true or false. You are describing a feature of the world.

  • Non-Cognitivism (Emotivism): When you say “stealing is wrong,” you aren’t stating a fact; you are expressing an emotion—essentially saying “Boo to stealing!” This is often called the “Ayc/Boo” theory of ethics.

3. Hume’s Guillotine: The “Is-Ought” Problem

One of the most famous logical barriers in meta-ethics was identified by David Hume. He noted that many thinkers move from descriptive statements (what is) to prescriptive statements (what ought to be) without any logical justification.

  • The Gap: You can describe every physical fact about a situation (e.g., “This program has a security flaw”), but those facts alone do not logically prove the moral claim (“You ought to fix it”).

  • The Bridge: Meta-ethics seeks to find the “bridge” that allows us to move from data to duty.

4. Why Meta-ethics Matters in the 2020s

As we build increasingly autonomous systems, meta-ethical questions have moved from the classroom to the laboratory:

  • AI Value Alignment: If we want to program an AI with “human values,” whose meta-ethical framework do we use? Is there a universal moral “source code” we can all agree on?

  • Moral Progress: If anti-realism is true, how do we justify the idea that society has “improved” over time? Meta-ethics provides the tools to argue for the validity of our progress.

Why Meta-ethics Matters to Our Readers

  • Foundation Building: Understanding meta-ethics helps you recognize the hidden assumptions in every ethical argument you encounter.

  • Critical Rigor: It prevents “lazy” moral thinking by forcing you to define your terms and justify your underlying logic.

  • Conflict Resolution: By identifying whether a disagreement is about facts or definitions, you can more effectively navigate complex cultural and professional disputes.