Simulation expectation
Teruji Thomas (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 16-2021, published at Erkenntnis
I present a new argument that we are much more likely to be living in a computer simulation than in the ground-level of reality. (Similar arguments can be marshalled for the view that we are more likely to be Boltzmann brains than ordinary people, but I focus on the case of simulations.) I explain how this argument overcomes some objections to Bostrom’s classic argument for the same conclusion. I also consider to what extent the argument depends upon an internalist conception of evidence, and I refute the common line of thought that finding many simulations being run—or running them ourselves—must increase the odds that we are in a simulation.
Other working papers
How to resist the Fading Qualia Argument – Andreas Mogensen (Global Priorities Institute, University of Oxford)
The Fading Qualia Argument is perhaps the strongest argument supporting the view that in order for a system to be conscious, it does not need to be made of anything in particular, so long as its internal parts have the right causal relations to each other and to the system’s inputs and outputs. I show how the argument can be resisted given two key assumptions: that consciousness is associated with vagueness at its boundaries and that conscious neural activity has a particular kind of holistic structure. …
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Paris School of Economics)
Estimating long-term impacts of actions is important in many areas but the key difficulty is that long-term outcomes are only observed with a long delay. One alternative approach is to measure the effect on an intermediate outcome or a statistical surrogate and then use this to estimate the long-term effect. …
Towards shutdownable agents via stochastic choice – Elliott Thornley (Global Priorities Institute, University of Oxford), Alexander Roman (New College of Florida), Christos Ziakas (Independent), Leyton Ho (Brown University), and Louis Thomson (University of Oxford)
Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that doesn’t happen. A key part of the IPP is using a novel ‘Discounted REward for Same-Length Trajectories (DREST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose evaluation metrics…