A related view is that less advanced/more narrow AI will do be able to do a fair number of tasks, but not enough to create widespread technological unemployment until very late, when very advanced AI quite quickly causes lots of people to be unemployed.
One consideration is how long time it will take for people to actually start using new AI systems (it tends to take some time for new technologies to be widely used). I think that some have speculated that that time lag may be shortened as AI become more advanced (as AI becomes involved in the deployment of other AI systems).
Scott Alexander has written an in-depth article about Hreha’s article:
The article itself mostly just urges behavioral economists to do better, which is always good advice for everyone. But as usual, it’s the inflammatory title that’s gone viral. I think a strong interpretation of behavioral economics as dead or debunked is unjustified.
See also Alex Imas’s and Chris Blattman’s criticisms of Hreha (on Twitter).
I think that though there’s been a welcome surge of interest in conceptual engineering in recent years, the basic idea has been around for quite some time (though under different names). In particular, Carnap argued that we should “explicate” rather than “analyse” concepts already in the 1940s and 1950s. In other words, we shouldn’t just try to explain the meaning of pre-existing concepts, but should develop new and more useful concepts that partially replace the old concepts.
Carnap’s understanding of explication was influenced by Karl Menger’s conception of the methodological role of definitions in mathematics, exemplified by Menger’s own explicative definition of dimension in topology....Explication in Carnap’s sense is the replacement of a somewhat unclear and inexact concept C, the explicandum, by a new, clearer, and more exact concept undefined, the explicatum.
See also Logical Foundations of Probability, pp. 3-20.
The link doesn’t seem to work.
Potentially relevant new paper:
The logic of universalization guides moral judgment
To explain why an action is wrong, we sometimes say: “What if everybody did that?” In other words, even if a single person’s behavior is harmless, that behavior may be wrong if it would be harmful once universalized. We formalize the process of universalization in a computational model, test its quantitative predictions in studies of human moral judgment, and distinguish it from alternative models. We show that adults spontaneously make moral judgments consistent with the logic of universalization, and report comparable patterns of judgment in children. We conclude that alongside other well-characterized mechanisms of moral judgment, such as outcome-based and rule-based thinking, the logic of universalizing holds an important place in our moral minds.
A new paper may give some support to arguments in this post:
The smart intuitor: Cognitive capacity predicts intuitive rather than deliberate thinking
Cognitive capacity is commonly assumed to predict performance in classic reasoning tasks because people higher in cognitive capacity are believed to be better at deliberately correcting biasing erroneous intuitions. However, recent findings suggest that there can also be a positive correlation between cognitive capacity and correct intuitive thinking. Here we present results from 2 studies that directly contrasted whether cognitive capacity is more predictive of having correct intuitions or successful deliberate correction of an incorrect intuition. We used a two-response paradigm in which people were required to give a fast intuitive response under time pressure and cognitive load and afterwards were given the time to deliberate. We used a direction-of-change analysis to check whether correct responses were generated intuitively or whether they resulted from deliberate correction (i.e., an initial incorrect-to-correct final response change). Results showed that although cognitive capacity was associated with the correction tendency (overall r = .13) it primarily predicted correct intuitive responding (overall r = .42). These findings force us to rethink the nature of sound reasoning and the role of cognitive capacity in reasoning. Rather than being good at deliberately correcting erroneous intuitions, smart reasoners simply seem to have more accurate intuitions.
An economist friend said in a discussion about sleepwalk bias 9 March:
In the case of COVID, this led me to think that there will not be that much mortality in most rich countries, but only due to drastic measures.
The rest of the discussion may also be of interest; e.g. note his comment that “in economics, I think we often err on the other side—people fully incorporate the future in many models.”
I agree people often underestimate policy and behavioural responses to disaster. I called this “sleepwalk bias”—the tacit assumption that people will sleepwalk into disaster to a greater extent than is plausible.
Jon Elster talks about “the younger sibling syndrome”:
A French philosopher, Maurice Merleau-Ponty, said that our spontaneous tendency is to view other people as ‘‘younger siblings.’’ We do not easily impute to others the same capacity for deliberation and reflection that introspection tells us that we possess ourselves, nor for that matter our inner turmoil, doubts, and anguishes. The idea of viewing others as being just as strategic and calculating as we are ourselves does not seem to come naturally.
Thanks, Lukas. I only saw this now. I made a more substantive comment elsewhere in this thread. Lodi is not a village, it’s a province with 230K inhabitants, as are Cremona (360K) and Bergamo (1.11M). (Though note that all these names are also names of the central town in these provinces.)
In the province of Lodi (part of Lombardy), 388 people were reported to have died of Covid-19 on 27 March. Lodi has a population of 230,000, meaning that 0.17% of _the population_ of Lodi has died. Given that everyone hardly has been infected, IFR must be higher.
The same source reports that in the province of Cremona (also part of Lombardy), 455 people had died of Covid-19 on 27 March. Cremona has a population of 360,000, meaning that 0.126% of the population of Cremona has died, according to official data.
Note also that there are reports of substantial under-reports of deaths in the Bergamo province. Some reports estimate that the true death rates in some areas may be as much as 1%. However, those reports are highly uncertain. And they may be outliers.
Here is a new empirical paper on folk conceptions of rationality and reasonableness:
Normative theories of judgment either focus on rationality (decontextualized preference maximization) or reasonableness (pragmatic balance of preferences and socially conscious norms). Despite centuries of work on these concepts, a critical question appears overlooked: How do people’s intuitions and behavior align with the concepts of rationality from game theory and reasonableness from legal scholarship? We show that laypeople view rationality as abstract and preference maximizing, simultaneously viewing reasonableness as sensitive to social context, as evidenced in spontaneous descriptions, social perceptions, and linguistic analyses of cultural products (news, soap operas, legal opinions, and Google books). Further, experiments among North Americans and Pakistani bankers, street merchants, and samples engaging in exchange (versus market) economy show that rationality and reasonableness lead people to different conclusions about what constitutes good judgment in Dictator Games, Commons Dilemma, and Prisoner’s Dilemma: Lay rationality is reductionist and instrumental, whereas reasonableness integrates preferences with particulars and moral concerns.
Thanks, this is interesting. I’m trying to understand your ideas. Please let me know if I represent them correctly.
It seems to me that at the start, you’re saying:
1. People often have strong selfish preferences and weak altruistic preferences.
2. There are many situations where people could gain more utility through engaging in moral agreements or moral trade—where everyone promises to take some altruistic action conditional on everyone else doing the same. That is because the altruistic utility they gain more than makes up for the selfish utility they lose.
These claims in themselves seem compatible with “altruism being about consequentialism”.
To conclude that that’s not the case, it seems that one has to add something like the following point. I’m not sure whether that’s actually what you mean, but in any case, it seems like a reasonable idea.
3. Fairness considerations loom large in our intuitive moral psychology: we feel very strongly about the principle that everyone should do and have their fair share, hate being suckers, are willing to altruistically punish free-riders, etc.
It’s known from dictator game studies, prisoner’s dilemma studies, tragedies of the common, and similar research that people have such fairness-oriented dispositions (though there may be disagreements about details). They help us solve collective action problems, and make us provide for public goods.
So in those experiments, people aren’t always choosing the action that would maximise their selfish interests in a one-off game. Instead they choose, e.g. to punish free-riders, even at a selfish cost.
Similarly, when people are trying to satisfy their altruistic interests (which is what you discuss), they aren’t choosing the actions that, at least on the face of it (setting indirect effects of norm-setting, etc, aside), maximally satisfy their altruistic interests. Instead they take considerations of fairness and norms into account—e.g. they may contribute in contexts where others are contributing, but not in contexts where others aren’t. In that sense, they aren’t (act)-consequentialists, but rather do their fair share of worthwhile projects/slot into norms they find appropriate, etc.
I think this is a kind of question where our intuitions are quite weak and we need empirical studies to know. It is very easy to get annoyed with poor epistemics and to conclude, in exasperation, that things must have got worse. But since people normally don’t remember or know well what things were like 30 years ago or so, we can’t really trust those conclusions.
One way to test this would be to fact-check and argument-check (cf. https://www.lesswrong.com/posts/k54agm83CLt3Sb85t/clearerthinking-s-fact-checking-2-0 ) opinion pieces and election debates from different eras, and compare their relative quality. That doesn’t seem insurmountably difficult. But of course it doesn’t capture all aspects of our epistemic culture.
One could also look at features that one may suspect are correlated with poor epistemics, like political polarisation. On that, a recent paper gives evidence that the US has indeed become more polarised, but five out of the other nine studied OECD countries rather had become less polarised.
How about a book that has a whole bunch of other scenarios, one of which is AI risk which takes one chapter out of 20, and 19 other chapters on other scenarios?
It would be interesting if you went into more detail on how long-termists should allocate their resources at some point; what proportion of resources should go into which scenarios, etc. (I know that you’ve written a bit on such themes.)
Unrelatedly, it would be interesting to see some research on the supposed “crying wolf effect”; maybe with regards to other risks. I’m not sure that effect is as strong as one might think at first glance.
Associate professor, not assistant professor.
One of those concepts is the idea that we evolved to “punish the non-punishers”, in order to ensure the costs of social punishment are shared by everyone.
Before thinking of how to present this idea, I would study carefully whether it’s true. I understand there is some disagreement regarding the origins of third-party punishment. There is a big literature on this. I won’t discuss it in detail, but here are some examples of perspectives which deviate from that taken in the quoted passage.
Joe Henrich writes:
This only makes sense as cultural evolution. Not much third party punishment in many small-scale societies .
So in Henrich’s view, we didn’t even (biologically) evolve to punish wrong-doers (as third parties), let alone non-punishers. Third-party punishment is a result of cultural, not biological, evolution, in his view.
Another paper of potential relevance by Tooby and Cosmides and others:
A common explanation is that third-party punishment exists to maintain a cooperative society. We tested a different explanation: Third-party punishment results from a deterrence psychology for defending personal interests. Because humans evolved in small-scale, face-to-face social worlds, the mind infers that mistreatment of a third party predicts later mistreatment of oneself.
Another paper by Pedersen, Kurzban and McCullough argues that the case for altruistic punishment is overstated.
Here, we searched for evidence of altruistic punishment in an experiment that precluded these artefacts. In so doing, we found that victims of unfairness punished transgressors, whereas witnesses of unfairness did not. Furthermore, witnesses’ emotional reactions to unfairness were characterized by envy of the unfair individual’s selfish gains rather than by moralistic anger towards the unfair behaviour. In a second experiment run independently in two separate samples, we found that previous evidence for altruistic punishment plausibly resulted from affective forecasting error—that is, limitations on humans’ abilities to accurately simulate how they would feel in hypothetical situations. Together, these findings suggest that the case for altruistic punishment in humans—a view that has gained increasing attention in the biological and social sciences—has been overstated.
A recent paper developed a statistical model for predicting whether papers would replicate.
We have derived an automated, data-driven method for predicting replicability of experiments. The method uses machine learning to discover which features of studies predict the strength of actual replications. Even with our fairly small data set, the model can forecast replication results with substantial accuracy — around 70%. Predictive accuracy is sensitive to the variables that are used, in interesting ways. The statistical features (p-value and effect size) of the original experiment are the most predictive. However, the accuracy of the model is also increased by variables such as the nature of the finding (an interaction, compared to a main effect), number of authors, paper length and the lack of performance incentives. All those variables are associated with a reduction in the predicted chance of replicability.
The first result is that one variable that is predictive of poor replicability is whether central tests describe interactions between variables or (single-variable) main effects. Only eight of 41 interaction effect studies replicated, while 48 of the 90 other studies did.
Another, unrelated, thing is that authors often make inflated interpretations of their studies (in the abstract, the general discussion section, etc). Whereas there is a lot of criticism of p-hacking and other related practices pertaining to the studies themselves, there is less scrutiny of how authors interpret their results (in part that’s understandable, since what counts as a dodgy interpretation is more subjective). Hence when you read the methods and results sections it’s good to think about whether you’d make the same high-level interpretation of the results as the authors.
One aspect may be that the issues we discuss and try to solve are often at the limit of human capabilities. Some people are way better at solving them than others, and since those issues are so often in the spotlight, it looks like the less able are totally incompetent. But actually, they’re not; it’s just that the issues they are able to solve aren’t discussed.
On first blush this looks like a success story, but it’s not. I was only able to catch the mistake because I had a bunch of background knowledge about the state of the world. If I didn’t already know mid-millenium China was better than Europe at almost everything (and I remember a time when I didn’t), I could easily have drawn the wrong conclusion about that claim. And following a procedure that would catch issues like this every time would take much more time than ESCs currently get.
Re this particular point, I guess one thing you might be able to do is to check arguments, as opposed to statements of facts. Sometimes, one can evaluate whether arguments are valid even when one isn’t too knowledgeable about the particular topic. I previously did some work on argument-checking of political debates. (Though the rationale for that wasn’t that argument-checking can require less knowledge than fact-checking, but rather that fact-checking of political debates already exists, whereas argument-checking does not).
I never did any systematic epistemic spot checks, but if a book contains a lots of arguments that appear fallacious or sketchy, I usually stop reading it. I guess that’s related.
Thanks for this. In principle, you could use KBCs for any kind of evaluation, including evaluation of products, texts (essay grading, application letters, life plans, etc), pictures (which of my pictures is the best?), etc. The judicial system is very high-stakes and probably highly resistant to reform, whereas some of the contexts I list are much lower stakes. It might be better to try out KBCs in such a low-stakes context (I’m not sure which one would be best). I don’t know what extent KBCs have tested for these kinds of purposes (it was some time since I looked into these issues, and I’ve forgotten a bit). That would be good to look into.
One possible issue that one would have to overcome is explicit collusion among subsets of raters. Another is, as you say, that people might converge on some salient characteristics that are easily observable but don’t track what you’re interested in (this could at least in some cases be seen as a form of “tacit collusion”).
My impression is that collusion is a serious problems for ratings or recommender systems (which KBCs can be seen as a type of) in general. As a rule of thumb, people might be more inclined to engage in collusion when the stakes are higher.
To prevent that, one option would be to have a small number of known trustworthy experts, who also make evaluations which function as a sort of spot checks. Disagreement with those experts could be heavily penalised, especially if there are signs that the disagreement is due to (either tacit or explicit) collusion. But in the end only any anti-collusion measure needs to be tested empirically.
Relatedly, once people have a history of ratings, you may want to give disproportionate weights to those with a strong track record. Such epistocratic systems can be more efficient than democratic systems. See Thirteen Theorems in Search of the Truth.
KBCs can also be seen as a kind of prediction contests, where you’re trying to predict other people’s judgements. Hence there might be synergies with other forms of work on predictions.