Notes on Fallible Ideas Essays

Fallible Ideas Notes on Knowledge:

Why is Knowledge Important?

  • Knowledge is information adapted to a purpose
  • Knowledge is solutions to problems
    • Always contains errors
    • Open to improvement
  • Creativity and criticism make knowledge
  • Problems are opportunities for improvement
  • Physics doesn’t prevent knowledge creation/problem solving
  • Happy life is possible with progress
    • Don’t need to know everything
  • Justified, true belief mistake encourages appeals to authority
  • “True” beliefs are immune from criticism and stop progress
  • Justification leads to infinite regress or circular argument (self-evidence is circular)
  • Pursue error corrections and improvements instead of justification
  • Support (evidence in favor) and proof are justificationist
  • Act on your best, or non-refuted, ideas

Knowledge Creation

  • Knowledge comes from guesses and criticism, or conjectures and refutations
    • Criticism eliminates the false ideas
  • Criticism: either guess is wrong, criticism is wrong, or both are wrong
  • Source of guesses isn’t important because criticism does the work
  • Knowing more criticisms restricts possible guesses
  • Unconscious mind filters ideas with criticism
  • Filters create blind spots, so criticism need improvements
  • A library of generic criticisms is a type of knowledge
    • Knowing categories of guesses that are mistaken
  • Evidence is the main form of criticism in science

Evolution and Knowledge

  • William Paley pointed out the “appearance of design” problem
  • How does complexity or purposeful adaptation arise?
    • It’s the same problem as, where does knowledge come from?
    • People create knowledge and it can’t just come from randomness
    • Where did the knowledge in living organisms come from?
  • Evolution is the only answer anyone has come up with
  • Evolution is accepted based on its non-refuted status, not the evidence (the role of evidence is to refute)
  • Evolution involves replicators; in biology they’re genes
    • Replicators create imperfect copies of themselves
    • There’s copying with small changes, then selection according to some criteria
    • Changes meeting the criteria are adaptations
  • Adaptations are solutions to problems
    • Most random changes make the adaptation worse as a solution
  • Animals embody knowledge without understanding; knowledge without a knowing subject
  • People select ideas according to their problem criteria just as the environment selects genes according reproductive ability
  • All knowledge literally comes from evolution

Knowledge Structure

  • Two multiplication algorithms with same inputs/outputs are not necessarily equal
  • There can be important differences in knowledge structure
  • Different types of multiplication algorithms: repeated addition, counting areas with geometry, lookup tables
  • Structure differences matter when you need to make modifications
    • Sub-system reuse and modularity
    • Ease of examining the structure, finding flaws, and repairs
    • Example: single motor car is better than multi-motor
  • Knowledge structure deals with all the internal variations that give same results
  • Structure is important to programming
    • Code readability, isolation, and reusability all effect code maintenance
  • A mind’s knowledge structure affects its ability to learn
    • Ease of application, integration, and making changes
  • Getting correct answers matters less than getting a good structure
  • Enjoyable, high effort, voluntary learning creates a better structure

Is There Objective Truth?

  • One truth that’s the same for everyone
  • Confusion due to ambiguous questions/lack of specificity
  • Relativism: truth differs between cultures
    • Mistake to view all knowledge as dependent on cultural context
    • Some knowledge is universal, like physics
    • COMMENT: Relativism is a variant of subjectivism
  • Any piece of knowledge has limited reach/applicability
  • Denying truth denies possibility of mistakes
  • Mistake to think knowledge is justified, true belief
    • Justification is wrong (leads to infinite regress or circularity)
    • Justificationism is susceptible to relativism
  • Communication is possible via convergence to objective truth


  • Definitions aren’t important
  • Start with problems and address ambiguities as the arise
  • Defining terms wastes time/energy
  • Definitions can never be perfect
  • Optimizing definitions beyond breakpoint wastes resources
  • Concepts/ideas are what’s important, not terminology
  • Can’t define everything; leads to circularity
  • Better understanding from discussion interactions than definitions
1 Like

The article says:

In science, evidence plays a major role.

These are different.

I agree. This jogged my memory of the BoI point about evidence in science (on pg. 119-120).

As I explained in Chapter 1, even in science, almost all rejected theories are rejected for being bad explanations, without ever being tested. Experimental testing is only one of many methods of criticism used in science, and the Enlightenment has made progress by bringing those other methods to bear in non-scientific fields too. The basic reason that such progress is possible is that good explanations about philosophical issues are as hard to find as in science – and criticism is correspondingly effective.

Evidence isn’t the main form of criticism in science. Using evidence in empirical tests is what separates science from math and philosophy, but science makes use of mathematical and philosophical criticisms too.