This article focuses on the ways that role playing improves forecasting accuracy in decision-making. The author, J. Scott Armstrong, provides evidence from previous studies, along with findings from his own experiments, to demonstrate how role playing is more beneficial than expert opinions in forecasting. Armstrong explains that role playing is carried out when "a forecaster ask subjects to put themselves in specified roles and then to either imagine how they would act, act out their responses alone, or interact with others in the situation". Armstrong includes specific recommendations on how to execute successful role playing. These recommendations highlight the key factors within role playing including casting subjects, the role playing instructions, a description of the situation, administration, coding, and the number of sessions.
First, Armstrong suggests that casting subjects similar to the decision-makers being portrayed has little weight on the role playing operation. This is due to previous experiments, such as Zimbardo (1972), that received realistic results from employing students as subjects. Armstrong says it is appropriate to use somewhat similar subjects if it is difficult to find role players who are very similar to the decision-makers. Next, Armstrong explains the importance of describing the roles of the subjects before they read the description of the situation being played out. The subjects should then be asked to improvise while acting either as themselves, or as how they believe the actual decision-maker would act in the given situation. Armstrong states that the situation should be described as accurately and briefly as possible so the subject is able to comprehend the situation. In order to make analysis easier, Armstrong also suggests that it is useful to provide subjects with possible decisions if this makes sense to the situation. Because role playing should be realistic, Armstrong highlights that it is important for the subjects to act out their responses and to interact in ways that correspond with the role playing situation. Finally, to properly analyze the role playing, Armstrong recommends having subjects write down their views of the decision and to have more than one person code their responses. Forecasts should be based on the number of decisions made through role playing. It is advantageous for the forecaster to perform at least five role playing sessions with one description, and five with another description.
Armstrong explains the ideal situations to use role playing, as well. Role playing is most useful in situations where there are two interacting parties. This contrasts from situations where parties do not interact, and in situations where there are too many parties involved. Secondly, role playing works best when the interacting parties are in conflict with one another. Lastly, role playing is beneficial for forecasting situations that involve situations with considerable changes. Armstrong states that role playing works well for these situations because of its ability to produce valid situations, and in turn, accurate forecasting. This is because role playing can make decision-makers aware of outcomes that were previously unknown to them. Additionally, role playing is able to provide decision-makers with a greater understanding of the situation since it acknowledges the perspectives of each party involved.
Armstrong gives extensive information on how and when to perform role playing, making it easy to understand the role playing process. One thing, however, that was hard to follow is why Armstrong proposes allowing subjects to either act as themselves or as the portrayed decision-maker. It seems unproductive to have someone act as their self if the purpose is to have them take on the role of a specific decision-maker. Armstrong also does not fully explain how to code and analyze the results of the role playing exercise, which would have been helpful to include.
Armstrong, J.S. (2001). Role playing: A method to forecast decisions. Marketing Papers, 152. Retrieved from http://repository.upenn.edu/cgi/viewcontent.cgi?article=1175&context=marketing_papers