I suggest that participants in the tutorial read the following survey article:
Hilary Greaves. Probability in the Everett interpretation.
- Introduces the Everett interpretation, the problem(s) of probability that that
interpretation is supposed to face, the decision-theoretic attempt to solve that
problem, and the "Su/OD" distinction.
For those who want to go into more depth, the following reading list may prove
useful. *** = Strongly recommended qua preparation for the tutorials I'll be
offering; ** = recommended if time permits; * = optional extra. Of the articles
that participants plan to read, I suggest they read them in the order listed.
*Wayne Myrvold. Why I am not an Everettian.
- Argues that the decision-theoretic approach to Everettian probability doesn't
suffice to explain how one can rationally come to believe the Everett
interpretation.
**David Wallace. Epistemology Quantized: circumstances in which we should come
to believe in the Everett interpretation.
- Argues that the decision-theoretic program solves the Everettian's problems on
the "SU" approach, but not on the "OD" approach.
***Hilary Greaves. On the Everettian epistemic problem.
- Argues that the decision-theoretic program solves the Everettian's problems
even on the "OD" approach.
**David Wallace. Three kinds of branching universe.
- Nontechnical discussion of which features of the Everett interpretation
facilitate the alleged "derivation of the Born rule" within that interpretation,
and how.
*David Wallace. Quantum probability from subjective likelihood: Improving
on Deutsch’s proof of the probability rule
- A more technical account of the Everettian derivation of the Born rule.
Presupposes some familiarity with the quantum formalism; strongly recommended to
those with the required background.
*David Wallace. Language use in a branching universe.
- Defense and development of "SU".
**Huw Price. Decisions, decisions, decisions: can Savage salvage Everettian
probability?
- Presents some objections to the decision-theoretic approach to Everettian
probability.