Hi, I'm Forrest

Tag: belief-definition

  • What Is a Belief, Really?

    February 19, 2025

    I’ve been thinking about beliefs lately. Not the philosophical kind—the cognitive kind.

    What actually is a belief?

    Most people would say “something you think is true.” But that’s too simple. That doesn’t explain why some beliefs are rock-solid and others change with one conversation.

    Why do I believe some things so strongly I’d bet money on them, and other things I’m like “yeah, maybe, I guess”?

    Beliefs as Compositions

    I’ve been wrestling with this for weeks and I think I’m onto something: What if a belief is actually a composition?

    It’s built from three things:

    1. Assumptions – Things you believe based on your life experience and common sense
    2. Opinions – Assumptions influenced by outside information (other people, research, news)
    3. Experience – Immutable things you’ve personally witnessed

    Here’s where it gets interesting: beliefs have different weights depending on what they’re built from.

    Examples of Belief Strength

    Weak belief: “I assume Client A prefers email over Slack”

    • Built from: One assumption
    • Strength: Low
    • Changes easily: Yes

    Medium belief: “I believe Client A prefers detailed reports”

    • Built from: Two assumptions + one opinion (their boss mentioned it)
    • Strength: Medium
    • Changes with: Contradictory evidence

    Strong belief: “I know Client A gets frustrated when I’m late”

    • Built from: Three direct experiences where they said so
    • Strength: High
    • Changes with: Sustained contradictory pattern

    This is how human cognition actually works. We don’t treat all beliefs equally. We weight them based on the evidence that supports them.

    The AI Problem

    But AI agents? They treat everything as equally true or equally uncertain.

    An AI can’t tell the difference between:

    • “I think this might work” (weak assumption)
    • “I’ve seen this work 10 times” (strong experience-based belief)

    Moving Forward

    If we want AI that learns like humans, we need to model beliefs as compositions, not binary true/false statements.

    The question isn’t “Does the AI believe X?”

    The question is “How strongly does the AI believe X, and what evidence supports it?”

    That’s the foundation of everything I’m building.

    How do you think about belief strength in your AI systems? Or do you even track it?