Overview

Epistemic luck refers to the role of chance in the acquisition of true belief. The central intuition in epistemology is that knowledge is incompatible with luck. If you are right just by accident, you don’t “know.”

Core Idea

The core idea is to define knowledge in a way that excludes “lucky guesses.” This is the driving force behind Gettier problems and many modern theories of knowledge (safety, sensitivity).

Formal Definition

Epistemic luck is usually analyzed in two forms:

  1. Veritic Luck: It is a matter of luck that the belief is true (e.g., a lucky guess). This is incompatible with knowledge.
  2. Reflective Luck: It is a matter of luck that the agent has the evidence they do (e.g., luckily turning your head at the right moment). This is usually considered compatible with knowledge.

Intuition

  • Lottery Ticket: You buy a lottery ticket. The odds are 1 in a million. You believe “I will lose.” You are almost certainly right. But do you know you will lose? Most say no, because if you won, it would just be a matter of luck. Your belief isn’t “safe” from error.
  • Broken Clock: (See Gettier Problems). You are right by accident.

Examples

  • The Lucky Guesser: Someone guesses the coin flip correctly. They have a true belief, but no knowledge.
  • Environmental Luck: (Barn Facade case). You are in a fake barn county. You happen to look at the one real barn. You are lucky you didn’t look at a fake one. This “environmental luck” often defeats knowledge.

Common Misconceptions

  • Misconception: Knowledge requires eliminating all luck.
    • Correction: We are lucky to be alive, lucky to have eyes, lucky to be in a universe with laws. This “benign” luck doesn’t prevent knowledge. Only “malignant” veritic luck does.
  • Safety Condition: A belief is safe if, in nearby possible worlds, you wouldn’t believe it falsely. (Designed to exclude luck).
  • Sensitivity Condition: A belief is sensitive if, had it been false, you wouldn’t have believed it.
  • Anti-Luck Epistemology: A research program focused on eliminating luck from the definition of knowledge.

Applications

  • Lottery Paradox: Explaining why we don’t claim to “know” lottery results before the draw, even with high probability.
  • Machine Learning: If an AI gets the right answer but for the wrong reasons (e.g., picking up on background noise in an image), is it “lucky”?

Criticism and Limitations

  • Vagueness: It is hard to define exactly where “benign” luck ends and “malignant” luck begins.
  • Skepticism: If we are too strict about eliminating luck, we might find we know very little.

Further Reading

  • Epistemic Luck by Duncan Pritchard
  • Knowledge and its Limits by Timothy Williamson