Irrelevant and independent natural extension for sets of desirable gambles
This work offers incremental tools for AI and other fields requiring coherent reasoning under uncertainty with partial probability assessments.
The paper tackles the problem of combining marginal coherent sets of desirable gambles into a joint set using epistemic irrelevance and independence, providing formulas for the smallest such joint called the independent natural extension and extending these results to conditional versions.
The results in this paper add useful tools to the theory of sets of desirable gambles, a growing toolbox for reasoning with partial probability assessments. We investigate how to combine a number of marginal coherent sets of desirable gambles into a joint set using the properties of epistemic irrelevance and independence. We provide formulas for the smallest such joint, called their independent natural extension, and study its main properties. The independent natural extension of maximal coherent sets of desirable gambles allows us to define the strong product of sets of desirable gambles. Finally, we explore an easy way to generalise these results to also apply for the conditional versions of epistemic irrelevance and independence. Having such a set of tools that are easily implemented in computer programs is clearly beneficial to fields, like AI, with a clear interest in coherent reasoning under uncertainty using general and robust uncertainty models that require no full specification.