Search

We show that the incorporation of any new piece of information allows for improved decision making in the sense that the expected costs of an optimal decision decrease (or, in boundary cases where no or not enough new information is incorporated, stays the same) whenever this is done by the appropriate update of the probabilities of the hypotheses. Versions of this result have been stated before. However, previous proofs rely on auxiliary constructions with proper scoring rules. We, instead, offer a direct and completely general proof by considering elementary properties of likelihood ratios only. We do point out the relation to proper scoring rules. We apply our results to make a contribution to the debates about the use of score based/feature based and common/specific source likelihood ratios. In the literature these are often presented as different ``LR-systems''. We argue that deciding which LR to compute is simply a matter of the available information. There is no such thing as different ``LR-systems'', there are only differences in the available information. In particular, despite claims to the contrary, scores can very well be used in forensic practice and we illustrate this with an extensive example in DNA kinship context.

In forensics it is a classical problem to determine, when a suspect $S$ shares a property $\Gamma$ with a criminal $C$, the probability that $S=C$. In this paper we give a detailed account of this problem in various degrees of generality. We start with the classical case where the probability of having $\Gamma$, as well as the a priori probability of being the criminal, is the same for all individuals. We then generalize the solution to deal with heterogeneous populations, biased search procedures for the suspect, $\Gamma$-correlations, uncertainty about the subpopulation of the criminal and the suspect, and uncertainty about the $\Gamma$-frequencies. We also consider the effect of the way the search for $S$ is conducted, in particular when this is done by a database search. A returning theme is that we show that conditioning is of importance when one wants to quantify the "weight" of the evidence by a likelihood ratio. Apart from these mathematical issues, we also discuss the practical problems in applying these issues to the legal process. The posterior probabilities of $C=S$ are typically the same for all reasonable choices of the hypotheses, but this is not the whole story. The legal process might force one to dismiss certain hypotheses, for instance when the relevant likelihood ratio depends on prior probabilities. We discuss this and related issues as well. As such, the paper is relevant both from a theoretical and from an applied point of view.

Familial Searching is the process of searching in a DNA database for relatives of a certain individual. It is well known that in order to evaluate the genetic evidence in favour of a certain given form of relatedness between two individuals, one needs to calculate the appropriate likelihood ratio, which is in this context called a Kinship Index. Suppose that the database contains, for a given type of relative, at most one related individual. Given prior probabilities for being the relative for all persons in the database, we derive the likelihood ratio for each database member in favour of being that relative. This likelihood ratio takes all the Kinship Indices between the target individual and the members of the database into account. We also compute the corresponding posterior probabilities. We then discuss two methods to select a subset from the database that contains the relative with a known probability, or at least a useful lower bound thereof. One method needs prior probabilities and yields posterior probabilities, the other does not. We discuss the relation between the approaches, and illustrate the methods with familial searching carried out in the Dutch National DNA Database., Comment: Changes w.r.t. v2: paper shortened for better readability; erroneous result on searching for half-siblings with sibling index (due to simulation error) replaced with correct one