Philosophy
Gettier Problems and the Definition of Knowledge
Quick fact
Edmund Gettier's 1963 paper was barely three pages long, but it overturned a 2,000-year-old definition of knowledge that dates back to Plato.
Why this is interesting
You know you're supposed to know what you know, but what if your confidence is just a lucky guess? Is a true belief that you're justified in holding always knowledge?
Read the full explanation
Understanding Gettier Problems and the Definition of Knowledge
Imagine you have a friend named Alice. You've always known her to drive a blue car, and you see her parking in the driveway. You form the belief: 'Alice's car is blue.' That belief is justified (you have strong evidence) and true (it is indeed blue). According to the classical definition, that counts as knowledge. Now, imagine a twist: you see Alice's car, but you're actually seeing a reflection that makes it appear blue, while in reality it's green. But wait, by sheer coincidence, Alice's car is actually blue—you just can't see it from your angle. Your belief is still justified (you think you see a blue car) and true (the car is blue), but it's only true by luck. Would you say you really know it's blue? Probably not. This is the core of Gettier problems: they show that justified true belief isn't enough for knowledge.
A deeper explanation
The mechanism behind Gettier problems is epistemic luck—the idea that a belief can be true by chance rather than through a reliable connection between evidence and truth. In classic Gettier cases, a person forms a belief based on strong evidence, but that evidence is actually misleading, and the belief's truth is accidental. This exposes a gap in the traditional definition: knowledge requires not just justification, but a non-accidental link between belief and truth. This has led philosophers to explore alternative definitions, such as reliabilism (belief produced by reliable processes) and virtue epistemology (belief formed through intellectual virtue). Gettier problems matter because they force us to refine our understanding of what it means to know something, making epistemology more rigorous and realistic.