Time Bias and Altruism
Leora Urim Sung (University College London)
GPI Working Paper No. 17-2023, winner of the ECCP 2022 Paper Prize
We are typically near-future biased, being more concerned with our near future than our distant future. This near-future bias can be directed at others too, being more concerned with their near future than their distant future. In this paper, I argue that, because we discount the future in this way, beyond a certain point in time, we morally ought to be more concerned with the present well- being of others than with the well-being of our distant future selves. It follows that we morally ought to sacrifice our distant-future well-being in order to relieve the present suffering of others. I argue that this observation is particularly relevant for the ethics of charitable giving, as the decision to give to charity usually means a reduction in our distant-future well-being rather than our immediate well-being.
Other working papers
Existential risks from a Thomist Christian perspective – Stefan Riedener (University of Zurich)
Let’s say with Nick Bostrom that an ‘existential risk’ (or ‘x-risk’) is a risk that ‘threatens the premature extinction of Earth-originating intelligent life or the permanent and drastic destruction of its potential for desirable future development’ (2013, 15). There are a number of such risks: nuclear wars, developments in biotechnology or artificial intelligence, climate change, pandemics, supervolcanos, asteroids, and so on (see e.g. Bostrom and Ćirković 2008). …
Simulation expectation – Teruji Thomas (Global Priorities Institute, University of Oxford)
I present a new argument for the claim that I’m much more likely to be a person living in a computer simulation than a person living in the ground-level of reality. I consider whether this argument can be blocked by an externalist view of what my evidence supports, and I urge caution against the easy assumption that actually finding lots of simulations would increase the odds that I myself am in one.
Measuring AI-Driven Risk with Stock Prices – Susana Campos-Martins (Global Priorities Institute, University of Oxford)
We propose an empirical approach to identify and measure AI-driven shocks based on the co-movements of relevant financial asset prices. For that purpose, we first calculate the common volatility of the share prices of major US AI-relevant companies. Then we isolate the events that shake this industry only from those that shake all sectors of economic activity at the same time. For the sample analysed, AI shocks are identified when there are announcements about (mergers and) acquisitions in the AI industry, launching of…