To err is human: Forgiveness across childhood and adolescence

This study examined children's and adolescents' narrative accounts and evaluations of an instance when they forgave a peer and an instance when they did not forgive, as well as their definitions of what it means to forgive. The sample included 100 participants in three age groups (7‐, 11‐, and 16‐year olds). Regardless of age, forgiveness and non‐forgiveness accounts differed in interpersonal features, such as how they responded when hurt and whether the peer apologized. The psychological features of the experiences involving their own thoughts and feelings also distinguished between events that were forgiven and those that were not, but did so for 16‐year olds and, sometimes, for 11‐year olds, but never for 7‐year olds. The distinct ways in which younger and older children narrated their experiences also were reflected in their evolving definitions of what it means to forgive, though children's definitions revealed aspects of their thinking not captured in their narratives. Finally, children at all ages judged forgiving favorably but, with age, their evaluations of not forgiving became less negative. These findings challenge the narrow conceptual and methodological lenses through which forgiveness had been examined, and underscore meaningful age differences in the ways children make sense of and evaluate forgiveness and non‐forgiveness.     

An Interactive Framework for Multi-Person,Multiobjective Decisions

Group decision making in the presence of multiple conflicting objectives is complex and difficult. This paper describes and evaluates an iterative technique to facilitate multiple objective decision making by multiple decision makers. The proposed method augments an interactive multiobjective optimization procedure with a preference ranking tool and a consensus ranking heuristic. Two multiple objective linear programming (MOLP) solution approaches, the SIMOLP method of Reeves and Franz [39] and the interactive weighted Tchebycheff procedure of Steuer and Choo [49], are recommended optimization strategies to be used independently or in concert. Computational experience suggests that the proposed framework is an effective decision-making tool. The procedure quickly located excellent compromise solutions in a series of test problems with hypothetical decision makers. In addition, human decision makers gave positive evaluations of the procedure and the production plans the procedure provided for a resource allocation case problem.