🤖 AI Summary
This paper identifies a paradox wherein heightened existential risk—such as from climate change, pandemics, or AI misalignment—undermines intergenerational cooperation, leading to underinvestment in long-term public goods. Method: We extend the intergenerational discounting framework by formally distinguishing individual mortality risk from species-level extinction risk, integrating intergenerational altruism theory with evolutionary “selfish gene” logic to construct a rigorous theoretical model. Contribution/Results: We demonstrate that existential risk compresses individuals’ expected future lifespan and attenuates intergenerational utility transmission, thereby endogenously increasing the effective discount rate. This generates an incentive failure—“the more dangerous, the less action”—where escalating extinction threats exacerbate short-termism. Our analysis provides a novel, time-preference–based explanation for the persistent global shortfall in provisioning long-term public goods, offering theoretical grounding for policy interventions targeting discount-rate recalibration.
📝 Abstract
We investigate the salience of extinction risk as a source of impatience. Our framework distinguishes between human extinction risk and individual mortality risk while allowing for various degrees of intergenerational altruism. Additionally, we consider the evolutionarily motivated "selfish gene" perspective. We find that the risk of human extinction is an indispensable component of the discount rate, whereas individual mortality risk can be hedged against - partially or fully, depending on the setup - through human reproduction. Overall, we show that in the face of extinction risk, people become more impatient rather than more farsighted. Thus, the greater the threat of extinction, the less incentive there is to invest in avoiding it. Our framework can help explain why humanity consistently underinvests in mitigation of catastrophic risks, ranging from climate change mitigation, via pandemic prevention, to addressing the emerging risks of transformative artificial intelligence.