Abstract
It is possible that you are living in a simulation—that your world is computer-generated rather than physical. But how likely is this scenario? Bostrom and Chalmers each argue that it is moderately likely—neither very likely nor very unlikely. However, they adopt an unorthodox form of reasoning about self-location uncertainty. Our main contention here is that Bostrom’s and Chalmers’ premises, when combined with orthodoxy about self-location, yields instead the conclusion that you are almost certainly living in a simulation. We consider how this (surprising) conclusion might be resisted, and show that the analogy between Sleeping Beauty cases and simulation cases provides a new way of evaluating approaches to self-location uncertainty. In particular, we argue that some conditionalization-based approaches to self-location are problematically limited in their applicability.
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Notes
See Thomas (2021, p. 5). Thomas assumes that the probability that you are simulated can be equated with the frequency of simulated experiences among experiences like yours, across all worlds. Thomas’s goal is to resist Birch’s (2013) selective skepticism objection; we make no claim here about the strength of this objection.
See Lewis (2013, p. 4018). Lewis’s main concern here, though, is to compare the strength of the doomsday argument and the simulation argument.
According to Musk, the odds that you are not living in a computer simulation is “one in billions” (quoted in Chalmers, 2022, p. 83).
Note that there is a distinction between Lewisian halfers (Lewis, 2001; Bradley, 2011) and double-halfers (Bostrom, 2007; Meacham, 2008; Pust, 2012). Rebuttals of one position are not necessarily rebuttals of the other, as witnessed by the fact that double-halfers offer rebuttals of the Lewisian position (Meacham, 2008, p. 262), and vice versa (Bradley, 2011, p. 408). Of particular note in the present context: Bostrom (2007) offers a double-halfer solution to Sleeping Beauty, and Neal (2006, p. 14) supplies a powerful thirder rebuttal.
Elga (2004) argues for the indifference principle at the heart of his (2000) thirder approach, rather than applying thirder reasoning. However, the crucial feature of this paper for present purposes is that it makes clear that uncertainty about which person you are should be treated in the same way as uncertainty about what time it is (2004, p. 396).
Not everyone shares the view that conclusions of this kind are absurd, e.g. Hansen (2023, p. 720).
We do not wish to imply that Milano intended her approach to be taken literally. Nor should we be understood as endorsing either Milano’s (2022) or Horgan & Mahtani’s (2013) approach to conditionalization in cases involving self-location uncertainty. We merely wish to stress that updating approaches can be applied to simulation scenarios. Similar considerations apply to Dutch book and scoring rule approaches (Briggs, 2010, and references therein). If one thinks of the profit/loss or the score as belonging to an individual, one might worry that these approaches do not carry over from Sleeping Beauty to cases involving distinct people. But one can think instead of the expected score of a person, given that the person is unsure which individual she is.
This case also has the virtue that it doesn’t involve memory loss, duplicates, or any other “fantastic assumption” that might lead us astray (Neal, 2006, p. 11).
Bostrom (2007) uses this “extreme Sleeping Beauty” scenario as part of an objection to thirder reasoning, but he does not dispute that thirder reasoning has the mathematical consequence in this case that we rely on.
Neal doesn’t rely on this argument strategy, but only takes it to be “further evidence” for the thirder solution (2006, p. 15).
Recall from Sect. 5.2 that Milano’s (2022) updating approach seemed unduly limited in that, taken literally, it presupposes a temporal possibility structure. This limitation is avoidable: we don’t have to interpret updating in a literal, temporal sense. However, the present limitation seems unavoidable: we do have to understand updating in terms of additional information, and that feature is missing in many cases in which the thirder conclusion follows.
For example, Dutch book approaches and scoring rule approaches assess the profit/loss or the score without reference to any unexperienced locations. Briggs (2010) argues for a thirder conclusion on the basis of these approaches.
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Acknowledgements
The authors are grateful to Dave Chalmers, Jonathan Fiat, Kay Mathiesen, and anonymous referees for this journal, for comments that improved this paper immensely.
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Lewis, P.J., Fallis, D. Simulation and self-location. Synthese 202, 180 (2023). https://doi.org/10.1007/s11229-023-04413-x
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DOI: https://doi.org/10.1007/s11229-023-04413-x