Dogmas in physics and IT: how to distinguish science

Anti-Scientific Dogmas in Physics, Mathematics, and Programming

Scientific knowledge is valuable only when it’s grounded in observation and enables predictions about natural phenomena. Usefulness is the key hallmark of science—what separates it from philosophical speculation. In physics, mathematics, and programming, a growing number of dogmas have emerged that contradict this principle, stifling progress.

The author, a physicist by training and a programmer by passion, emphasizes: absurd beliefs aren’t just useless—they’re harmful. They arise from historical biases, commercial interests, and community inertia.

Dogmatism in Physics

Physics studies nature in all its complexity—from elementary particles to social interactions. Models are built on measurements and tested statistically. Hypotheses are acceptable when data is scarce, but they must never be elevated to unquestionable truths.

Problems begin when successful hypotheses are declared 'divine.' The Copenhagen interpretation of quantum mechanics asserts incompatibility with classical physics without solid justification. General Relativity contradicts certain experiments yet remains dominant due to 'mathematical elegance.'

The scientific community shows resistance to change: alternative interpretations of quantum mechanics are rarely published, and criticizing General Relativity risks one’s career. Journal editorial policies, sensationalist media, and grants for speculative topics like string theory reinforce these dogmas.

Many 'universe' theories rest on ideas with no experimental foundation. Discrepancies with data are masked with makeshift fixes. This points to a lack of a fundamental theory—premature dogma is anti-scientific.

Mathematics: Aesthetics vs. Constructiveness

Mathematics is often called the 'queen of sciences,' highlighting internal beauty over practical application. Most textbooks avoid real-world utility, focusing instead on abstraction. This diminishes its value as a scientific tool.

The core divide lies between constructive (intuitionistic) math and classical math. In constructive math, a proof must provide an algorithm that builds evidence of truth. The Axiom of Choice and the Law of Excluded Middle are rejected.

Example:

• Statement: 'All kittens I’ve seen are cute.'
• Non-constructive: 'Some unloved kittens might exist' (useless).
• Constructive: Provide a specific 'scary' kitten.

Non-constructive methods offer intuition, but only constructive conclusions hold scientific value. They form the foundation of programming.

Programming as an Experimental Discipline

Programming solves real-world problems—closer to physics than abstract mathematics. Yet many developers don’t see it as science, relying instead on 'best practices' without scrutiny.

Anti-scientific dogmas stem from marketing: acronyms like SOLID, corporate promotion of OOP (e.g., Java). Ideas spread not because they work, but because they’re hyped. Result? Heavy legacy code, restructured education, and a distorted job market.

• Marketing vs. Science: Books and conferences promote dubious techniques.
• Consequences: Codebases overflow with redundancy; developers waste time on 'standards' instead of efficiency.
• Solution: Evaluate techniques based on measurable benefits—performance, maintainability, maintenance cost.

Programming demands experimentation: measure, model, discard what doesn’t work.

What Matters

• Usefulness is the universal criterion of scientific validity: knowledge must predict and improve the real world.
• Dogmas in physics (General Relativity, Copenhagen interpretation) hinder progress due to community inertia.
• Constructive mathematics delivers algorithmic proofs—essential for reliable programming.
• In IT, marketing-driven dogma creates legacy code; empirical testing of techniques is essential.
• Progress requires skepticism toward authority and a focus on experimentation.

— Editorial Team