Skip to content
Carmelics
TopicsThinkersChangesContributorsLoading account…

    Carmelics

    A reasoning platform. Break down any belief into clear reasons, explore both sides, and weigh the evidence honestly.

    Navigate

    • Topics
    • Search
    • Recent Changes
    • Contribute
    • How It Works
    • Glossary
    • Thinkers
    • Contributors
    • About
    • Statistics
    • Terms
    • Privacy

    Database

    Statements
    —
    Perspectives
    —
    Topics
    —

    Press ? for keyboard shortcuts

    LoyalLoyalJusticeJustice
    Made withinDC&Austin
    Statements
    321,452
    Perspectives
    108,905
    Topics
    42
    Home/Original/inverse
    See Original
    Inverse View

    It is not the case that Chaos models provide limited but important predictive insight, not a complete failure of prediction.

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    2 perspectives
    Reason for 1 of 2
    ?
    • 1.Predictions of global invariants like Lyapunov exponents and attractors depend on ensemble assumptions that may not hold for any single realized system.
      ?

      Think about whether this reason is strong or weak

    • 2.Pierre Duhem argued that physical theories require auxiliary hypotheses whose errors compound, making the isolation of 'global' from 'local' prediction philosophically untenable.
      ?

      Think about whether this reason is strong or weak

    • 3.If the epistemic value of prediction requires applicability to particular outcomes, statistical descriptions of attractors fail the standard set by Hempel's covering-law model of scientific explanation.
      ?

      Think about whether this reason is strong or weak

    Reason for 2 of 2
    ?
    • 1.Nancy Cartwright's critique of idealization holds that models abstracting away trajectory specifics trade predictive content for mathematical tractability, not genuine insight.
      ?

      Think about whether this reason is strong or weak

    • 2.A prediction that specifies only the topology of possible behaviors without determining which behavior obtains provides no discriminating power between competing hypotheses about the actual world.
      ?

      Think about whether this reason is strong or weak

    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Detailed predictions of individual trajectories fail rapidly for chaotic models when there is any error in the specification of the initial state.
      ?

      Think about whether this reason is strong or weak

    • 2.Instead of individual trajectory prediction, chaos models predict global behaviors and account for limited predictability.
      ?

      Think about whether this reason is strong or weak

    • 3.Many global behaviors of chaotic models can be precisely predicted, such as control parameter values at which bifurcations occur, the onset of chaos, and the return of n-periodic orbits.
      ?

      Think about whether this reason is strong or weak

    Next step

    Based on where you are in your exploration

    Strongest counterpoint
    Explore the most compelling reason on the other side.