In defence of fanaticism
Hayden Wilkinson (Australian National University)
GPI Working Paper No. 4-2020, published in Ethics
Which is better: a guarantee of a modest amount of moral value, or a tiny probability of arbitrarily large value? To prefer the latter seems fanatical. But, as I argue, avoiding such fanaticism brings severe problems. To do so, we must (1) decline intuitively attractive trade-offs; (2) rank structurally identical pairs of lotteries inconsistently, or else admit absurd sensitivity to tiny probability differences;(3) have rankings depend on remote, unaffected events (including events in ancient Egypt); and often (4) neglect to rank lotteries as we already know we would if we learned more. Compared to these implications, fanaticism is highly plausible
Other working papers
Longtermism, aggregation, and catastrophic risk – Emma J. Curran (University of Cambridge)
Advocates of longtermism point out that interventions which focus on improving the prospects of people in the very far future will, in expectation, bring about a significant amount of good. Indeed, in expectation, such long-term interventions bring about far more good than their short-term counterparts. As such, longtermists claim we have compelling moral reason to prefer long-term interventions. …
Consciousness makes things matter – Andrew Y. Lee (University of Toronto)
This paper argues that phenomenal consciousness is what makes an entity a welfare subject, or the kind of thing that can be better or worse off. I develop and motivate this view, and then defend it from objections concerning death, non-conscious entities that have interests (such as plants), and conscious subjects that necessarily have welfare level zero. I also explain how my theory of welfare subjects relates to experientialist and anti-experientialist theories of welfare goods.
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…