Deciding what to replicate: A decision model for replication study selection under resource and knowledge constraints

Robust scientific knowledge is contingent upon replication of original findings. However, replicating researchers are constrained by resources, and will almost always have to choose one replication effort to focus on from a set of potential candidates. To select a candidate efficiently in these cases, we need methods for deciding which out of all candidates considered would be the most useful to replicate, given some overall goal researchers wish to achieve. In this article we assume that the overall goal researchers wish to achieve is to maximize the utility gained by conducting the replication study. We then propose a general rule for study selection in replication research based on the *replication value* of the set of claims considered for replication. The *replication value* of a claim is defined as the maximum expected utility we could gain by conducting a replication of the claim, and is a function of (1) the value of being certain about the claim, and (2) uncertainty about the claim based on current evidence. We formalize this definition in terms of a causal decision model, utilizing concepts from decision theory and causal graph modeling. We discuss the validity of using *replication value* as a measure of expected utility gain, and we suggest approaches for deriving quantitative estimates of *replication value*. Our goal in this article is not to define concrete guidelines for study selection, but to provide the necessary theoretical foundations on which such concrete guidelines could be built.