 Instead, many approaches consider features that are desirable for rational aggregation, and then ask which simpler aggregation rules satisfy them. For instance, one thing a rational aggregation method should do (to prevent Dutch booking) is yield the same credence regardless of whether information is obtained before or after aggregating. For instance, if we all have credences about the rain, and someone comes in wearing shorts, it should not matter to the final group output whether 1) they entered and we all updated our credences (in a Bayesian way) and then aggregated them, or 2) we aggregated our credences, they entered, and we updated the aggregated credence (in a Bayesian way). Geometric methods, which take the geometric average of probabilities over worlds, yield this desirable property in many cases (Genest 1984; Dietrich and List  2015).[3]  These methods proceed by multiplying (weighted) credences over worlds that might obtain and then renormalizing them to sum to 1.