= Conditional diagnosticity in attribution
Behavior $B$ is evidence for a disposition $D$, given a situation $S$, to the extent that its conditional <likelihood ratio> $P(B\mid D,S)/P(B\mid\neg D,S)$ differs from one. By <Bayes' theorem> this ratio multiplies the prior odds to give the <posterior odds>. If a constraint forces the same behavior regardless of the disposition, the ratio is one and the behavior leaves those odds unchanged. This does not exclude diagnostic information in other features, such as wording or voluntary choices. The argument identifies the assumptions needed to call a <dispositional attribution> biased.
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