Gettiers in software engineering (2019)

Access to the paper

• Original Gettier PDF link was overloaded; several alternates were shared.
• Commenters note the paper is extremely short, plainly written, and worth reading directly.

Gettier problem & justified true belief (JTB)

• Many discuss classic Gettier cases (cow/papier-mâché, stopped clock, misleading TV, Zoom background) and how they challenge “knowledge = justified true belief.”
• Some propose fixes (e.g., justification must itself be a justified true belief; justification must be causally connected to the truth; evidence must be true) but others note these lead to regress or further complications.
• Disagreement over whether Gettier’s argument is deep, overblown, or mostly semantic hairsplitting about “know.”

Knowledge, belief, and probability

• Repeated tension between “you can’t know falsehoods” vs. “people clearly ‘know’ false things in everyday language.”
• Several argue knowledge is better seen as degrees of belief or probabilities, not binary truth.
• Bayesianism and pragmatism are cited as more realistic: we manage uncertainty and update beliefs, not chase absolute certainty.
• Popperian falsifiability is raised: universal claims can’t be proven, only falsified; particular claims can be supported once a counterexample appears.
• Physics is framed as replacing JTB with “testability and predictive success” rather than justification.

Parallels to software engineering & debugging

• Many relate Gettier-style coincidences to debugging: “coincidental failures,” red herrings, and bugs that appear linked to the wrong change.
• Stories: multiple independent faults conspiring, legacy systems “haunted forests,” malware-laden forgotten clusters, code that worked only by accident, tests masking paired defects.
• Emphasis on:
  • Strong monitoring, metrics, and observability instead of trusting mental models.
  • Bisection, reverting to known-good states, and “introduce a known error” to verify you’re editing the right thing.
  • Accepting that large systems can’t be fully understood; you need instrumentation, not just reasoning.

Meta: philosophy, models, and LLMs

• Some see Gettier and JTB debates as classic but somewhat quaint or narrow, especially compared to probabilistic or model-based views of knowledge.
• Others defend philosophy as foundational “front trench” work preceding formal science and logic.
• LLMs are likened to “papier-mâché cows” or “cargo culting as a service”: outputs that look right without grounded epistemic guarantees.