Consequentialism, Cluelessness, Clumsiness, and Counterfactuals
Alan Hájek (Australian National University)
GPI Working Paper No. 4-2024
According to a standard statement of objective consequentialism, a morally right action is one that has the best consequences. More generally, given a choice between two actions, one is morally better than the other just in case the consequences of the former action are better than those of the latter. (These are not just the immediate consequences of the actions, but the long-term consequences, perhaps until the end of history.) This account glides easily off the tongue—so easily that one may not notice that on one understanding it makes no sense, and on another understanding, it has a startling metaphysical presupposition concerning counterfactuals. I will bring this presupposition into relief. Objective consequentialism has faced various objections, including the problem of “cluelessness”: we have no idea what most of the consequences of our actions will be. I think that objective consequentialism has a far worse problem: its very foundations are highly dubious. Even granting those foundations, a worse problem than cluelessness remains, which I call “clumsiness”. Moreover, I think that these problems quickly generalise to a number of other moral theories. But the points are most easily made for objective consequentialism, so I will focus largely on it.
Other working papers
Economic growth under transformative AI – Philip Trammell (Global Priorities Institute, Oxford University) and Anton Korinek (University of Virginia)
Industrialized countries have long seen relatively stable growth in output per capita and a stable labor share. AI may be transformative, in the sense that it may break one or both of these stylized facts. This review outlines the ways this may happen by placing several strands of the literature on AI and growth within a common framework. We first evaluate models in which AI increases output production, for example via increases in capital’s substitutability for labor…
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)
The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …
On two arguments for Fanaticism – Jeffrey Sanford Russell (University of Southern California)
Should we make significant sacrifices to ever-so-slightly lower the chance of extremely bad outcomes, or to ever-so-slightly raise the chance of extremely good outcomes? Fanaticism says yes: for every bad outcome, there is a tiny chance of of extreme disaster that is even worse, and for every good outcome, there is a tiny chance of an enormous good that is even better.