Erratum to: Non-Measurability, Imprecise Credences, and Imprecise Chances

Imprecise Evidence without Imprecise Credences

A traditional theory of uncertainty says that beliefs come in degrees. Degrees of belief (“credences”) have real number values between  and , where  conventionally represents certain belief,  represents certain disbelief, and values in between represent degrees of uncertainty. We have elegant, well-understood normative theories for credences: norms for how credences should hang together at ...

Scoring imprecise credences: A mildly immodest proposal

Jim Joyce argues for two amendments to probabilism. The first is the doctrine that credences are rational, or not, in virtue of their accuracy or “closeness to the truth” (1998). The second is a shift from a numerically precise model of belief to an imprecise model represented by a set of probability functions (2010). We argue that both amendments cannot be satisfied simultaneously. To do so, w...

A Defense of Imprecise Credences in Inference and Decision Making1

Some Bayesians have suggested that beliefs based on ambiguous or incomplete evidence are best represented by families of probability functions. I spend the first half of this essay outlining one version of this imprecise model of belief , and spend the second half defending the model against recent objections, raised by Roger White and others, which concern the phenomenon of probabilistic dilat...

Non-discretionary imprecise data in efficiency Measurement

This paper introduces discretionary imprecise data in Data Envelopment Analysis (DEA) and discusses the efficiency evaluation of Decision Making Units (DMUs) with non-discretionary imprecise data. Then, suggests a method for evaluation the efficiency of DMUs with non-discretionary imprecise data. When some inputs and outputs are imprecise and non-discretionary, the DEA model becomes non-linear ...

Non convex technologies and economic efficiency measure with imprecise data