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Reversal Test

Edited by Vladimir_Nesov, Swimmer963 (Miranda Dixon-Luinenburg), et al. last updated 23rd Sep 2020

The reversal test is a technique for fighting status quo bias in judgments about the preferred value of a continuous parameter. If one deems the change of the parameter in one direction to be undesirable, the reversal test is to check that either the change of that parameter in the opposite direction (away from status quo) is deemed desirable, or that there are strong reasons to expect that the current value of the parameter is (at least locally) the optimal one.

For example, if it became possible to increase the human lifespan, some would argue that it would be undesirable for people to live longer because, say, overpopulation would be difficult to manage. The reversal test is then to check that the same people accept that shorter lifespan is desirable, or that there are really strong reasons to believe that the current lifespan happens to be optimal.

The rationale of the Reversal Test is simple: if a continuous parameter admits of a wide range of possible values, only a tiny subset of which can be local optima, then it is prima facie implausible that the actual value of that parameter should just happen to be at one of these rare local optima [...] the burden of proof shifts to those who maintain that some actual parameter is at such a local optimum: they need to provide some good reason for supposing that it is so.

Obviously, the Reversal Test does not show that preferring the status quo is always unjustified. In many cases, it is possible to meet the challenge posed by the Reversal Test

—The reversal test: eliminating status quo bias in applied ethics

Main article

  • Nick Bostrom, Toby Ord (2006). "The reversal test: eliminating status quo bias in applied ethics". Ethics (University of Chicago Press) 116 (4): 656-679. (PDF)

See also

  • Status quo bias, Privileging the hypothesis
  • Shut up and multiply
  • Absurdity heuristic

External links

  • "The Reversal Test and Status Quo Bias" (John Danaher)
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