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There seem to be broadly two types of people. Those who tend to ask "what's the evidence for this theory?" and those who tend to ask "what theory is the best explanation for this evidence?"
I think the second one is more fundamental. We usually ask a question of the first kind in order to answer a question of the second kind. We are mostly not interested in theories for themselves, but only insofar they explain some observation.
E.g. we are mainly interested in relativity theory because it successfully explains a lot of phenomena, rather than being interested in phenomena because they confirm relativity theory.
The second question also fits the epistemic direction. We don't start out with a theory which we then try to confirm or disconfirm. We usually start out with a lot of evidence (observable facts), and only afterwards do we try to find theories to explain this evidence.
If we seek new evidence it is usually to distinguish between multiple competing explanations of the evidence we already have, or if we think the available explanations aren't very good and might be wrong.
Only thinking about the first question can also lead to confusion about strength of evidence. From the question-one perspective we may ask: "Is there weak or strong evidence for theory X?" But what does "weak" or "strong" mean here? Weak or strong compared to which alternative explanations? The real question is whether theory X is a good explanation for the evidence it tries to explain, and a major part of this consideration is whether or not there are better alternative explanations.
To be sure, questions of the first kind are often sensible, but they can be misleading if we lose sight of the corresponding question of the second kind.
1I feel like there's a third way people tend to act regarding theories and evidence. "I'm curious about this topic, so I'm going to gather observations about it, and seek out new ways to make observations". I feel like this is actually the attitude that should preceed either of the other two when approaching a novel area of research.
An example that comes to mind for me is the invention of the microscope, and the revolution in biology that this led to. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4422127/#s1title I would argue that the first microscopes were made because people were curious. And then observations were made and shared using these new tools. I think it's important that knowledge gathering and sharing tends to preceed that knowledge being considered evidence for our against a hypothesis. So I'd describe these steps:
- Unfocused curiosity-driven knowledge gathering and sharing
- Tentative hypothesis formation
- Focused knowledge gathering to explore the tentative hypotheses
- After a critical mass of evidence has been gathered, and many tentative hypotheses resolved, a possible theory takes shape. This is the step of asking "What theory best explains this evidence?"
- Then you actively seek evidence which would disprove the theory. As you rule out possible ways the theory could be disproven you gradually become more confident the theory is correct.
- Then you teach others about the theory. This is where you explain how the evidence gathered so far fits the theory. At this step someone might ask, "What's the evidence for this theory?" in order to evaluate whether to accept your theory. That seems to me to fundamentally be a question asked by a theory-learner not a theory-creator. A wisely critical consumer of education asks this about a theory before accepting the theory as part of their worldview.
It seems "unfocused curiosity-driven knowledge gathering" makes sense if it is driven by some form of instrumental goal, as in invention and engineering, which produce useful stuff rather than general explanations of the world. At least if I imagine it as gathering data from trial and error tinkering, as presumably the invention of the microscope was done.
But for science it is mostly the case that we already have far more data than we can make sense of. So the main problem is to explain known phenomena, and additional data gathering is only necessary to distinguish between competing explanations or to potentially rule out existing ones. There are exceptions though -- e.g. astronomy and history and paleontology try to create big catalogues of data in some subject area
That's so interesting. Such a very different view of science from mine. I feel like it seems like there's a lot of data sometimes, but then getting down into the weeds on some particular narrow question and suddenly I always give myself lacking the exact data I would need. Or a new method or tool opens up a new type of data...
Solutions to a prisoner's dilemma are typically assumed to involve "coordination" in some sense. But what kinds of mechanism are appropriate examples for coordination? For an N-person prisoner's dilemma, one form of coordination is implementing voting. Say, everyone is forced to cooperate when the majority votes "cooperate". Nobody has a selfish interest to cooperate, but everyone has a selfish interest to vote for "cooperate".
This is interesting because economists often see voting as irrational for decision theoretic reasons. But from the game theoretic perspective above, it appears to be rational. This is probably not a new insight, but I haven't seen voting being portrayed as a type of solution to N-person prisoner's dilemmas.
A semi-relevant example that your take reminded me of: https://braineaser.com/brainteasers/5-pirates-puzzle/
Note that the game-theoretic "true" prisoner's dilemma is formulated to make coordination (both communication and outside-of-game considerations like reputation, side-payments, self-image, etc.) are ignored. All of the non-Nash "solutions" are by introducing factors into the game that change the game very significantly.
Voting (especially when defectors aren't penalized, just forced to cooperate) is a pretty big variation, and needs to be explicitly modeled in order to determine what equilibra are rational.
This is almost unrelated to real-world voting, which has SO MANY complicating and interfering factors that simple models just don't tell us much.
Of course for a "real" prisoners dilemma any form of coordination is ruled out from the start. But in real world instances, coordination can sometimes be introduced into systems that previously were prisoner's dilemmas. That's what I mean with "solving" a prisoner's dilemma. Making the dilemma go away.
The thing I'm pointing out here is that "coordination" is a very unspecific term, and one concrete form of coordination is being able to vote for cooperation. (Example: voting on a climate change bill instead of trying to minimize your personal carbon footprint, which would make you personally significantly worse of with hardly any benefit on the whole, which is why you would defect but vote on cooperate.) I think voting is usually not appreciated as a method of coordination, only as a method of choosing the most popular policy/party, which doesn't need to involve solving a prisoner's dilemma.
"coordination" is a very unspecific term, and one concrete form of coordination is being able to vote for cooperation
Ah. I'd say that "voting" is pretty non-specific as well. It's the enforcement mechanisms that bind behaviors after the votes are counted that are actually the coordination mechanisms. Voting is the easy, un-impactful part, enforcing (socially as well as legally/violently) the result is impactful.
Voting is well-known and OFTEN used as a mechanism for determining the most agreeable (or least likely to result in riots) result. It's a key prerequisite to many coordination mechanisms. But it isn't a complete mechanism on its own.
It's often said that controlling the ballot is more important than controlling the vote. The pre-voting process to figure out how to coordinate the options to choose among (and the pre-pre-voting decisions for preliminary votes) matter a whole lot.