Integration information in the judicial field: adding versus averaging models.

In the course of a trial, the main task that every judge or juror has to face concerns the evaluation of various pieces of evidence from a variety of different sources, with the aim of integrating such data into a single, final verdict. Algebraic models have tried to explain and predict decisional paths by identifying formal, mathematical combinatory rules. The aim of the present research was to test two main integration information models, namely adding and averaging, when combining items of judicial evidence. In the first study, we investigated how the probability of guilt varied as a function of the value of the pieces of evidence and information presented, in legal and not legal professional samples. In the second study, we analysed combinatory rules with more complex and realistic experimental material. Results indicated that participants summed the values of pieces of evidence in a linear fashion when they had to provide estimates of guilt. We found evidence of an adding rule among both legal and not legal professionals as well as in simple and more complex judicial cases, thus providing even stronger support for the use and the generalization of a summative model. Theoretical and practical implications are discussed.