I like your description here a lot. I am no expert but I agree with your characterization that Peirce's pragmatic maxim offers something really valuable even for those committed to correspondence and, more generally, to analytic philosophy.
On Rorty, his last book was just published posthumously and it offers an intriguing and somewhat different take on his thinking. The basics haven't changed, but he frames his version of pragmatism in terms of the Enlightenment and anti-authoritarianism. I won't try to summarize; your mileage might vary but I've fou... (read more)
Thanks for this. Here's Stanford Encyclopedia of Philosophy's first paragraph:"Pragmatism is a philosophical tradition that – very broadly – understands knowing the world as inseparable from agency within it. This general idea has attracted a remarkably rich and at times contrary range of interpretations, including: that all philosophical concepts should be tested via scientific experimentation, that a claim is true if and only if it is useful (relatedly: if a philosophical theory does not contribute directly to social progress then it is not worth much), ... (read more)
Maybe to try and see if I understand I should try to answer: it'd be a mix of judgment, empirical evidence (but much broader than the causal identification papers), deductive arguments that seem to have independent force, and maybe some deference to an interdisciplinary group with good judgment, not necessarily academics?
Makes sense on 3 and 4. Out of curiosity, what would change your mind on the minimum wage? If you don't find empirical economics valuable nor the views of experts (or at least don't put much stock in them) how would you decide whether supply and demand was better or worse theory than an alternative? The premises underlying traditional economic models are clearly not fully 100% always-and-everywhere true, so their conclusions need not be either. How do you decide about a theory's accuracy or usefulness if not by reference to evidence or expertise?
Thanks for this. I don't agree for scientists, at least in their published work, but I do agree that to an extent it's of course inevitable to bring in various other forms of reasoning to make subjective assessments that allow for inferences. So I think we're mostly arguing over extent.
My argument would basically be:
2. I would disagree on economics. I view the turn of economics towards high causal identification and complete neglect of theory as a major error, for reasons I touch on here. The discipline has moved from investigating important things to trivial things with high causal identification. The trend towards empirical behavioural economics is also in my view a fad with almost no practical usefulness. (To reiterate my point on the minimum wage - the negative findings are almost certainly false: it is what you would expect to find for a small treatme... (read more)
Philosopher Michael Strevens argues that what you're calling myopic empiricism is what has made science so effective:
I'd like to see more deference to the evidence as you say, which isn't the same as to the experts themselves, but more deference to either theory or common sense is an invitation for motivated reasoning and for most people in most contexts would, I suspect, be a step backward.
But ultimately I'd like to see this question solved by some myopic empiricism!... (read more)
I lean toward: When in doubt, read first and read more. Ultimately it's a balance and the key is having the two in conversation. Read, then stop and think about what you read, organize it, write down questions, read more with those in mind.
But thinking a lot without reading is, I'd posit, a common trap that very smart people fall into. In my experience, smart people trained in science and engineering are especially susceptible when it comes to social problems--sometimes because they explicitly don't trust "softer" social science, and sometimes because they... (read more)
I take this post to raise both practical/strategic and epistemological/moral reasons to think EAs should avoid being too exclusive or narrow in what they say "EAs should do." Some good objections have been raised in the comments already.
Is it possible this post boils down to shifting from saying what EAs should do to what EAs should not do?
That sounds maybe intuitively unappealing and un-strategic because you're not presenting a compelling, positive message to the outside world. But I don't mean literally going around telling people what not to... (read more)
This kind of debate is why I'd like to see the next wave of Tetlock-style research focus on the predictive value of different types of evidence. We know a good bit now about the types of cognitive styles that are useful for predicting the future, and even for estimating causal effects in simulated worlds. But we still don't know that much about the kinds of evidence that help. (Base rates, sure, but what else?) Say you're trying to predict the outcome of an experiment. Is reading about a similar experiment helpful? Is descriptive data helpful? Is interview... (read more)
Here's the case that the low-fidelity version is actually better. Not saying I believe it, but trying to outline what the argument would be...Say the low-fidelity version is something like: "Think a bit about how you can do the most good with your money and time, and do some research."
Could this be preferable to the real thing?
It depends on how sharply diminishing the returns are to the practice of thinking about all of this stuff. Sometimes it seems like effective altruists see no diminishing returns at all. But it's plausible that they are steeply ... (read more)
Unfortunately I think the importance of EA actually goes up as you focus on better and better things. My best guess is the distribution of impact is lognormal, this means that going from, say, the 90th percentile best thing to the 99th could easily be a bigger jump than going from, say, the 50th percentile to the 80th.
You're right that at some point diminishing returns to more research must kick in and you should take action rather than do more research, but I think that point is well beyond "don't do something obviously bad", and more like "after you've thought really carefully about what the very top priority might be, including potentially unconventional and weird-seeming issues".
Do you see this area as limited to cases where participants in a decision are trying and failing to make "good" decisions by their own criteria (ie where incentives are aligned but performance isn't there because of bad process or similar) or are you also thinking of cases where participants have divergent goals and suboptimal decisions from an EA standpoint are driven by conflict and misaligned incentives rather than by process failures?
Agree on both points. Economist's World in 2021 partnership with Good Judgment is interesting here. I also think as GJ and others do more content themselves, other content producers will start to see the potential of forecasts as a differentiated form of user-generated content they could explore. (My background is media/publishing so more attuned to that side than the internal dynamics of the social platforms.) If there are further discussions on this and you're looking for participants let me know.
This is a very good idea. The problems in my view are biggest on the business model and audience demand side. But there are still modest ways it could move forward. Journalism outlets are possible collaborators but they need the incentive perhaps by being able to make original content out of the forecasts.
To the extent prediction accuracy correlates with other epistemological skills you could task above average forecasters in the audience with tasks like up- and down-voting content or comments, too. And thereby improve user participation on news sites even if journalists did not themselves make predictions.
Convergence to best practice produces homogeneity.
As it becomes easier to do what is likely the best option given current knowledge, fewer people try new things and so best practices advance more slowly.
For example, most organizations would benefit from applying "the basics" of good management practice. But the frontier of management is furthered by experimentation -- people trying unusual ideas that at any given point in time seem unlikely to work.
I still see the project of improving collective decision-making as very positive on net. But if it succeeds, it becomes important to think about new ways of creating space for experimentation.
If you're good at forecasting it's reasonable to expect you'll be above average at reasoning or decision making tasks that require making predictions.
But judgment is potentially different. In "Prediction Machines" Agrawal et al separate judgment and prediction as two distinct parts of decision making where the former involves weighing tradeoffs. That's harder to measure but a potentially distinct way to think about the difference between judgment and forecasting. They have a theoretical paper on this decision making model too.