We have probabilistic models of the weather; ensemble forecasts. They’re fairly accurate. You can plan a picnic using them. You can not use probabilistic models to predict the conversation at the picnic (beyond that it will be about “the weather”, “the food”, etc.)
What I mean by computable probability distribution is that it’s tractable to build a probabilistic simulation that gives useful predictions. An uncomputable probability distribution is intractable to build such a simulation for. Knightian Uncertainty is a good name for the state of not being able to model something, but not a very quantitative one (and arguably I haven’t really quantified what makes a probabilistic model “useful” either).
I think the computability of probability distributions is probably the right way to classify relative agency but we also tend to recognize agency through goal detection. We think actions are “purposeful” because they correspond to actions we’re familiar with in our own goal-seeking behavior: searching, exploring, manipulating, energy-conserving motion, etc. We may even fail to recognize agency in systems that use actions we aren’t familiar with or whose goals are alien (e.g. are trees agents? I’d argue yes, but most people don’t treat them like agents compared to say, weeds). The weather’s “goal” is to reach thermodynamic equilibrium using tornadoes and other gusts of wind as its actions. It would be exceedingly efficient at that if it weren’t for the pesky sun. The sun’s goal is to expand, shed some mass, then cool and shrink into its own final thermodynamic equilibrium. It will Win unless other agents interfere or a particularly unlikely collision with another star happens.
Before modern science no one would have imagined those were the actual goals of the sun and the wind and so the periodic, meaningful-seeming actions suggested agency toward an unknown goal. After physics the goals and actions were so predictable that agency was lost.
We have probabilistic models of the weather; ensemble forecasts. They’re fairly accurate. You can plan a picnic using them. You can not use probabilistic models to predict the conversation at the picnic (beyond that it will be about “the weather”, “the food”, etc.)
What I mean by computable probability distribution is that it’s tractable to build a probabilistic simulation that gives useful predictions. An uncomputable probability distribution is intractable to build such a simulation for. Knightian Uncertainty is a good name for the state of not being able to model something, but not a very quantitative one (and arguably I haven’t really quantified what makes a probabilistic model “useful” either).
I think the computability of probability distributions is probably the right way to classify relative agency but we also tend to recognize agency through goal detection. We think actions are “purposeful” because they correspond to actions we’re familiar with in our own goal-seeking behavior: searching, exploring, manipulating, energy-conserving motion, etc. We may even fail to recognize agency in systems that use actions we aren’t familiar with or whose goals are alien (e.g. are trees agents? I’d argue yes, but most people don’t treat them like agents compared to say, weeds). The weather’s “goal” is to reach thermodynamic equilibrium using tornadoes and other gusts of wind as its actions. It would be exceedingly efficient at that if it weren’t for the pesky sun. The sun’s goal is to expand, shed some mass, then cool and shrink into its own final thermodynamic equilibrium. It will Win unless other agents interfere or a particularly unlikely collision with another star happens.
Before modern science no one would have imagined those were the actual goals of the sun and the wind and so the periodic, meaningful-seeming actions suggested agency toward an unknown goal. After physics the goals and actions were so predictable that agency was lost.