4 ms·

That's like saying that causation doesn't matter; correlation will suffice. But the causal directionality of separating subject from object in a relation is es

That's like saying that causation doesn't matter; correlation will suffice. But the causal directionality of separating subject from object in a relation is essential to forming or sharing an idea usefully. In the synthetic game worlds you mention, causality is decided by play sequence. But at no point does the agent ever have to employ abstraction, or 'think about thinking', which diminishes the needed depth of thought greatly. No, probability alone simply isn't expressive enough to support higher cognition. At best it will deliver a purely reactive agent, no more cognizant than Kahnemann's knee-jerk level of thinking.

EGreg 8y ago
Funny that you say that. David Hume said that you can’t meaningfully prove causation, only describe things and speak about about correlation. Also time is a local phenemenon. You can’t simply compare A and B time if A and B are really far apart. When you get to really small scales you would be hard pressed to find which event happened before the other, so you get uncaused events like Virtual Particles in quantum mechanics. And when you have tons of complexity then what do you mean by cause and effect instead of correlation? https://www.quantamagazine.org/omnigenic-model-suggests-that-all-genes-affect-every-complex-trait-20180620/ https://www.quantamagazine.org/omnigenic-model-suggests-that...
- randcraw 8y ago
  I won't challenge Hume; I'm referring only to the fact that a causal relation implies necessity: B can't happen unless A acted to cause it. Probability can't provide this insight, it can only encourage or discourage it. The agent must formulate a mental model of the relation in order to hypothecate the presence of necessity / causality between a pair of events. A model that lacks that ability (e.g. probability) can lead to all sorts of nonsense in interpreting events, like proposing causality. Or at least meaning by connecting a baseball pitcher's hat color with the weather on game day. BTW, precision isn't necessary for relative time to have great value in a huge fraction of possible world events. Obviously all you need is relative temporal sequence to disprove causality - since the batter can't hit until the pitcher throws. In a great many event pairs, a lot of temporal precision Is unnecessary. Often it's only approximate relative time that matters, since the mental task intuits all the temporal constraints needed for understanding event coupling, like knowing the parent child relation implies the relative ages of each and much more latent info that may or not be relevant to a given interpretive task at hand.
  - EGreg 8y ago
    And what I am saying is that when you say A implies B you’re making a ton of assumptions. If those assumptions are violated (eg all swans are white, all men are taller than all women) then suddenly you have caveats and hedges. So what you actually use is not Propositional Logic / Boolean Algebra but Bayes’ Theorem. You only use rules of inference because you make assumptions. I am saying that may work sometimes but those models are not exact.

EGreg 8y ago

Funny that you say that. David Hume said that you can’t meaningfully prove causation, only describe things and speak about about correlation. Also time is a local phenemenon. You can’t simply compare A and B time if A and B are really far apart. When you get to really small scales you would be hard pressed to find which event happened before the other, so you get uncaused events like Virtual Particles in quantum mechanics. And when you have tons of complexity then what do you mean by cause and effect instead of correlation? https://www.quantamagazine.org/omnigenic-model-suggests-that-all-genes-affect-every-complex-trait-20180620/ https://www.quantamagazine.org/omnigenic-model-suggests-that...

randcraw 8y ago
I won't challenge Hume; I'm referring only to the fact that a causal relation implies necessity: B can't happen unless A acted to cause it. Probability can't provide this insight, it can only encourage or discourage it. The agent must formulate a mental model of the relation in order to hypothecate the presence of necessity / causality between a pair of events. A model that lacks that ability (e.g. probability) can lead to all sorts of nonsense in interpreting events, like proposing causality. Or at least meaning by connecting a baseball pitcher's hat color with the weather on game day. BTW, precision isn't necessary for relative time to have great value in a huge fraction of possible world events. Obviously all you need is relative temporal sequence to disprove causality - since the batter can't hit until the pitcher throws. In a great many event pairs, a lot of temporal precision Is unnecessary. Often it's only approximate relative time that matters, since the mental task intuits all the temporal constraints needed for understanding event coupling, like knowing the parent child relation implies the relative ages of each and much more latent info that may or not be relevant to a given interpretive task at hand.
- EGreg 8y ago
  And what I am saying is that when you say A implies B you’re making a ton of assumptions. If those assumptions are violated (eg all swans are white, all men are taller than all women) then suddenly you have caveats and hedges. So what you actually use is not Propositional Logic / Boolean Algebra but Bayes’ Theorem. You only use rules of inference because you make assumptions. I am saying that may work sometimes but those models are not exact.