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Fair Countable Lotteries and Reflection

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Abstract

The main conclusion is this conditional: If the principle of reflection is a valid constraint on rational credences, then it is not rational to have a uniform credence distribution on a countable outcome space. The argument is a variation on some arguments that are already in the literature, but with crucial differences. The conditional can be used for either a modus ponens or a modus tollens; some reasons for thinking that the former is most reasonable are given.

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Notes

  1. That is, for all X and Y such that NAX and NBY are defined, P(NBY ) = P(NBYNAX), and similarly for A and B reversed. Since zero-probability propositions will be on the table, conditional probability must be understood as primitive (as opposed to defined as a fraction, cf. Hájek 2013), and dictate how an ideally rational agent must update her credences when receiving new information.

  2. Some deny this principle because they reject the idea that credences can (in ideal cases) be represented by exact probabilities. See, e.g., Fine (1988, 400).

  3. The argument is taken from Howson (2014, section 8). It is only a slight variation on an argument from de Finetti (1972, 205–206) (where it is attributed to Lester Dubins), but the slight variation is exactly that it uses reflection (explicitly), which makes it better suited for comparison with the above argument. The original argument by de Finetti is instead concerned with conglomerability, the principle that a probability P(A) is, for any countable partition {Bi}iI of the outcome space, in the interval spanned by the conditional probabilities {P(ABi)}iI.

  4. This will perhaps be clearer if we change the scenario slightly. Assume that no matter the outcome of the coin toss, both of the two sub-procedures are carried out: the stochastic variable Nh has the 2n-distribution and the stochastic variable Nt has the 0-distribution. And N has the outcome of Nh if heads comes up, while it has the outcome of Nt if tails comes up. Then we can phrase it more clearly: “It might at least seem that this piece of data is epistemically relevant to the agent’s assessment of the probability that n was the realization of Nt.”

  5. A popular idea for how to reform probability theory is to allow events to have infinitesimal non-zero probabilities: see Benci et al. (2018) and references therein. Countable fair lotteries are an important part of the motivation for this move, as infinitesimals allow for the reconciliation of the countable additivity principle with uniformity. However, it does little to address the problem raised here. Since each event of the form {0, … , n} in a fair countable lottery is assigned an infinitesimal probability, the above argument goes through if we replace the exact-value version of the reflection principle with this interval version: if an agent at an instant of time t0 knows that she will, at a later instant of time t1, assign credence belonging to the interval I to a given proposition p, then she ought to have a credence that belongs to I regarding p at t0 (and similarly for the other reflection principles to be discussed in Section 4).

  6. That instant of time must be specified in such a way that the future self will know when it has arrived (Schervish et al., 2004). Otherwise, the current self may be able to infer extra information from the condition about the future probability assignment in such a way that it is in a better position to assign probabilities than the future self, and should therefore not defer to it. However, t1 and t2 clearly are so specified.

  7. In fact, the alternative reflection principle is only one element of a comprehensive theory that covers the problematic cases mentioned above. (Another significant element is a generalization of conditionalization.) However, only his reflection principle is relevant for present purposes.

  8. The reason that this statement of the principle is rough is that the word “rationally” has to be defined carefully to yield a precise version of the principle. It should be clear enough what it means in cases like drunkenness, but it is very complicated to explain exactly how to interpret it in the case of self-locating problems. Since that kind of problem is not relevant to the scenarios considered in this paper, I will just refer the interested reader to Titelbaum’s book.

  9. Huisman (2015) argues for saving the permissiveness of mere finite additivity by proposing a weakened form of reflection which does not limit a rational agent’s current credence for a proposition to what she knows that credence will be updated to in the future (when she does know that), but only to an interval that is determined by what she would update it to in all of a range of scenarios with counter-factual limitations on the knowledge she is going to obtain. I do not see any motivation for this proposed weakening of reflection, which amounts to ignoring the actual knowledge, except as an ad hoc means to avoid countable additivity.

  10. It seems reasonable to suppose that it is something like this amended principle that Howson (2014, 1006) refers to when he writes “So amended, the principle itself seems sound enough: indeed it would, I believe, be virtually self-contradictory to deny it”.

  11. Note that being a certainty cannot be identified with having credence 1, because for each \(n\in \mathbb {N}\), we have P0(N1n) = 1, even though it is not certain that N1n.

  12. This is part of the rationality prerequisite for reflection mentioned above; see Titelbaum (2012, 134).

  13. The argument was first made in de Finetti (1930).

  14. The question of how far uniformity can be taken before this problem kicks in is explored by Kerkvliet and Meester (2016) and (using infinitesmal probabilities) Wenmackers and Horsten (2013).

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Acknowledgements

I have benefitted from discussion with and feedback from Claire Benn, Sharon Berry, Elvira Di Bona, Silvia Jonas, Leora Katz, Timber Kerkvliet, Øystein Linnebo, Olla Solomyak, Georgie Statham, and the anonymous referees.

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Correspondence to Casper Storm Hansen.

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Appendices

Appendix

This Appendix contains a longer and more precise version of the argument in Section 1.

The first thing to make more precise is the character of the functions. P0 is stipulated to be a probability function on \(\mathbb {N}^{2}\). Making the dependence of P1 and P2 on the outcomes of the lotteries explicit, these are not simply probability functions on \(\mathbb {N}^{2}\), but rather functions from \(\mathbb {N}^{2}\) into the space of probability functions on \(\mathbb {N}^{2}\). For \(n,m\in \mathbb {N}\), \(s\subseteq \mathbb {N}^{2}\), and i = 1, 2, Pi(n, m)(s) represents the agent’s credence at ti for the proposition that the ordered pair of outcomes belongs to s, if the ordered pair of outcomes is actually (n, m).

In continuity with the notation used above, I will abbreviate “{(N1, N2)∣ϕ}” as just “ϕ”. So, for instance, N2 < l is the set {(N1, N2)∣N2 < l}, i.e. \(\mathbb {N}\times \{0,\ldots ,l-1\}\).

Premises

In the statements of the premises, all free variables are implicitly bound by initial universal quantifiers, restricted to

  • ℕ for the variables “n”, “m”, “k”, and “l”,

  • subsets of ℕ2 for “s” and “t”,

  • [0, 1] for “x”,

  • {1, 2} for “i”, and

  • the set of finite sets of mutually disjoint subsets of ℕ2 for “S”.

The first four premises are uncontroversial principles of probability, including finite additivity:

  1. 1.

    \(P_{i}(n,m)(\bigcup S)={\sum }_{s\in S}P_{i}(n,m)(s)\)

  2. 2.

    \(P_{i}(n,m)(\mathbb {N}^{2}\setminus s)=1-P_{i}(n,m)(s)\)

  3. 3.

    Pi(n, m)(s) = 1 → Pi(n, m)(t) = Pi(n, m)(st)

  4. 4.

    \(P_{0}(\mathbb {N}^{2})=1\)

The next group of premises encode the scenario stipulations concerning the events at t1 and t2:

  1. 5.

    P1(n, m)(N1 = n) = 1

  2. 6.

    P1(n, m)(N2 = l) = 0

  3. 7.

    P2(n, m)(N1 = k) = 0

  4. 8.

    P2(n, m)(N2 = m) = 1

The final premise comprises the relevant instances of the reflection principle:

  1. 9.

    P0({(n, m)∣Pi(n, m)(s) = x}) = 1 → P0(s) = x

In fact, this premise is a little more than the reflection principle. The premise does not say that if the t0 credence of the ti credence of s being x is 1, then the t0 credence of s is x; but rather, that if the t0 credence of an event that leads to the ti credence of s being x is 1, then the t0 credence of s is x. This is a strengthening of reflection that is justified when the agent knows which events will lead to which credences. Thus, this formal premise combines what is informally more easily thought of as several premises: the weak reflection principle, the ideal rationality of the agent, and that the agent knows at t0 what will happen at t1 and t2 (except for the specific winning numbers).

Deduction

From 1 and 6:

  1. 10.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{2}\leq n)={\sum }_{i=0}^{n} P_{1}(n,m)(N_{2}=i)=0\)

From 2 and 10:

  1. 11.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{2}>n)=1-P_{1}(n,m)(N_{2}\leq n)=1\)

From 3:

  1. 12.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{1}=n)=1\)P1(n, m)(N2 > N1) = P1(n, m)(N2 > N1N1 = n)

Because for all \(n\in \mathbb {N}\), N2 > N1N1 = n is the same set as N2 > nN1 = n:

  1. 13.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{2}>N_{1}\mathrel \cap N_{1}=n)\)= P1(n, m)(N2 > nN1 = n)

From 3:

  1. 14.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{1}=n)=1\)P1(n, m)(N2 > nN1 = n) = P1(n, m)(N2 > n)

From 12, 13, and 14:

  1. 15.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{1}=n)=1\)P1(n, m)(N2 > N1) = P1(n, m)(N2 > n)

From 5, 11, and 15:

  1. 16.

    \(\forall n,m\in \mathbb {N}:P_{1}(n,m)(N_{2}>N_{1})=1\)

From 16:

  1. 17.

    \(\{(n,m)\mid P_{1}(n,m)(N_{2}>N_{1})=1\}=\mathbb {N}^{2}\)

From 4 and 17:

  1. 18.

    P0({(n, m)∣P1(n, m)(N2 > N1) = 1}) = 1

From 9 and 18:

  1. 20.

    P0(N2 > N1) = 1

By analogous reasoning, using 7 and 8 instead of 5 and 6:

  1. 20.

    P0(N2 > N1) = 0

From 19 and 20:

  1. 21.

    Contradiction

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Hansen, C.S. Fair Countable Lotteries and Reflection. Acta Anal 37, 595–610 (2022). https://doi.org/10.1007/s12136-021-00499-5

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