Summary thread: 'Essays on Longtermism' Chapters

Hi everyone! I’m Shakked, a PhD student in economics at MIT, and this comment summarizes The Short-Termism of ‘Hard’ Economics, a chapter in Essays on Longtermism that I coauthored with my dad Ilan, a Professor of Economics at Victoria University of Wellington in New Zealand.

The chapter is about the academic economics profession. We think the profession has not yet, and will not in the foreseeable future, produce much useful longtermist research as part of the mainstream research it systematically produces, publishes in top journals, and rewards professionally. (Individual economists might still produce useful longtermist research as voluntary side-projects, as we note below.) In the chapter, we argue that this is because the profession is subject to a constricting set of methodological norms that preclude the kinds of research that might be useful from a longtermist perspective.

Specifically, we think that useful longtermist research - by virtue of its speculative nature and the thin historical evidence base it has to rely on - will tend to have a few attributes. It will draw on a variety of sources of empirical evidence, including expert forecasts, narrative historical commentaries, quantitative projections, interviews and focus groups, and so on. It will make theoretical arguments that are often institutionally-specific or draw on a diversity of concepts. As a consequence of these diversities of evidence and argument, it will tend to be multi-disciplinary.

Almost all of the above are ruled out by the current methodological norms of the academic economics profession. An obsession with methodological “hardness” - a concept which George Akerlof says is used to “classify sciences according to a hard–soft hierarchy, with physics at the top and sociology, cultural anthropology, and history at the bottom” - results in the imposition of severe restrictions on the kinds of work economists accept. On the empirical side, this means that only carefully identified causal estimates are permissible forms of empirical evidence. This rules out descriptive, explanatory, or predictive work and narrows attention to topics where sufficiently large micro datasets are available, implicitly narrowing the geographic and temporal scope of research. On the theoretical side, this involves a focus on mathematical generality and technical sophistication and difficulty, which precludes both arguments that are impossible to formalize mathematically and arguments that are trivial to formalize.

The chapter goes into a lot of detail about the exact shape of the norms and applies them to three areas of longtermist interest: long-term decision-making, climate change, and AI.

The chapter was written in mid-2022, before the release of ChatGPT, so the past 3 years have constituted an interesting out-of-sample test of the arguments and predictions we make. We think the chapter has held up pretty well. There’s been an enormous growth in research on AI in economics; the majority of this new research has taken the form of the kind of short-termist empirical or backwards-looking theoretical work we describe in the chapter. There’s also been an increase in interest in longtermist perspectives on AI, including an upcoming NBER conference on the Economics of Transformative AI, as well as some examples of genuinely useful research. But so far our sense is these developments show no sign of penetrating the top journals or professional reward processes.

Hi everyone! Below is a summary of the chapter that Maya Eden and I wrote for Essays on Longtermism.

What socially beneficial causes should altruists prioritize if they give equal ethical weight to the welfare of current and future generations? Many have argued that, because human extinction would result in a permanent loss of all future generations, extinction risk mitigation should be the top priority. We call this the long-run argument for extinction risk mitigation.

In Alexandrie and Eden (2025), we evaluate the long-run argument for extinction risk mitigation through the lens of population models. Below we outline what we take to be the two key takeaways of the paper.

Takeaway 1: The long-run argument for extinction risk mitigation relies on the assumption that the global population would partly recover after a non-extinction catastrophe
We present a theoretical framework for quantifying the cost-effectiveness of interventions aimed at preventing negative shocks to the size of the global population. A heuristic implied by this framework is that the undiscounted cost-effectiveness of reducing the risk of a negative population shock is proportional to the ratio of future lives lost (in percentage terms) to current lives lost (in percentage terms). We call this the long-term value ratio.

Let us compare the cost-effectiveness of reducing extinction risk with that of reducing the risk of a catastrophe that would kill 10% of the population. Since human extinction would result in a loss of 100% of current lives and 100% of future lives, the long-term value ratio of extinction risk mitigation is 1. Now, a catastrophe that would kill 10% of current lives may result in a loss of less than 10% of future lives (if there is recovery) and more than 10% of future lives (if there is amplification). Therefore, if there is recovery, the long-term value ratio of mitigating the 10%-catastrophe is higher than 1; if there is amplification, it is less than 1. This example shows that (partial) recovery is necessary for extinction risk mitigation to be more cost-effective than other types of catastrophic risk mitigation.

Takeaway 2: Population models suggest that nothing guarantees recovery after a negative population shock
We distinguish between two different negative shocks to the size of the global population. A pure population shock is an event that kills some fraction of the world population without having much direct impact on other factors of production (e.g., a pandemic that kills people, but doesn't destroy physical capital). An all-factor shock is an event that destroys all factors of production in equal proportion (e.g., an asteroid that kills the same fraction of people as it destroys physical capital). 

Pure population shocks and all-factor shocks have different implications for recovery dynamics after the shock. We consider three models of such fertility dynamics: the social determinants model, the Barro-Becker model, and the Malthusian model. For pure population shocks, only the Malthusian model unambiguously implies that the population would recover after a shock. For all-factor shocks, none of the models imply that such recovery would occur, at least if natural resources are destroyed in the same proportion as population and physical capital. This is because the population models we consider imply that the economic determinants of fertility are invariant to the scale of the economy, which is all that is affected by all-factor shocks.

Conclusion
The long-run argument for prioritizing extinction risk mitigation relies on the assumption that the global population would partly recover after a non-extinction catastrophe (Takeaway 1). However, population models suggest that nothing guarantees that such recovery would occur after a non-extinction catastrophe (Takeaway 2). Together, these two takeaways provide a challenge to the long-run argument for prioritizing extinction risk mitigation.