Aggregating Infinitely Many Probability Measures

Abstract

Abstract. The problem of how to rationally aggregate probability measures occurs in particular (i) when a group of agents, each holding probabilistic beliefs, needs to rationalise a collective decision on the basis of a single 'aggregate belief system' and (ii) when an individual whose belief system is compatible with several (possibly infinitely many) probability measures wishes to evaluate her options on the basis of a single aggregate prior via classical expected utility theory (a psychologically plausible account of individual decisions).We investigate this problem by first recalling some negative results from preference and judgment aggregation theory which show that the aggregate of several probability measures should not be conceived as the probability measure induced by the aggregate of the corresponding expected-utility preferences. We describe how McConway's (Journal of the American Statistical Association, vol. 76, no. 374, pp. 410-414, 1981) theory of probabilistic opinion pooling can be generalised to cover the case of the aggregation of infinite profiles of finitelyadditive probability measures, too; we prove the existence of aggregation functionals satisfying responsiveness axioms à la McConway plus additional desiderata even for infinite electorates. On the basis of the theory of propositional-attitude aggregation, we argue that this is the most natural aggregation theory for probability measures.Our aggregation functionals for the case of infinite electorates are neither oligarchic nor integral-based and satisfy (at least) a weak anonymity condition. The delicate set-theoretic status of integral-based aggregation functionals for infinite electorates is discussed.

Probabilistic Opinion Pooling with Imprecise Probabilities

“…The question of how the probabilistic opinions of different individuals should be aggregated to form a group opinion is controversial. But one assumption seems to be pretty much common ground: for a group of Bayesians, the representation of group opinion should itself be a unique probability distribution (Madansky, 1964;Lehrer and Wagner, 1981;McConway, 1981;Bordley, 1982;Genest and Zidek, 1986;Mongin, 1995;Clemen and Winkler, 1999;Dietrich and List, 2014;Herzberg, 2014). We argue that this assumption is not always in order.…”

Aggregation for potentially infinite populations without continuity or completeness

“…Chambers (2007) extends that approach to axiomatize linear pooling for ordinal probabilities. Extending McConway's approach, linear pooling results for finitely additive probability measures and an infinite population are given in Herzberg (2015) and Nielsen (2019); the latter giving an equivalent result using analogues of P1 and P2.…”

“…In the context of values, see e.g Zhou (1997),. attributing the idea toHarsanyi (1967-68); for beliefs, see e.g Herzberg (2015)Ramsey (1928) (opposing impatience), and ofKoopmans (1960) andDiamond (1965) (requiring impatience).…”