A linguistic approach to assess the dynamics of design team preference in concept selection

This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an individual’s evaluation of an alternative through natural language based on the Systemic-Functional Linguistics system of APPRAISAL. The linguistic model accounts for both the degree of intensity and the uncertainty of expressed evaluations. Second, this multi-dimensional linguistic model is converted into a scalar to represent the degree of intensity and a probability distribution function for the stated evaluation. Finally, we present a Markovian model to calculate the time-varying change in preferential probability, the probability that an alternative is the most preferred alternative. We further demonstrate how preferential probability toward attributes of alternatives correspond to preferential probability toward alternatives. We illustrate the method on two case studies to highlight the time-variant dynamics of preferences toward alternatives and attributes. This research contributes to process tracing in descriptive decision science to understand how engineers actually take decisions.National Science Foundation (U.S.) (Award CMMI-0900255

When Does Diversity Trump Ability (and Vice Versa) in Group Decision Making? A Simulation Study

It is often unclear which factor plays a more critical role in determining a group's performance: the diversity among members of the group or their individual abilities. In this study, we addressed this “diversity vs. ability” issue in a decision-making task. We conducted three simulation studies in which we manipulated agents' individual ability (or accuracy, in the context of our investigation) and group diversity by varying (1) the heuristics agents used to search task-relevant information (i.e., cues); (2) the size of their groups; (3) how much they had learned about a good cue search order; and (4) the magnitude of errors in the information they searched. In each study, we found that a manipulation reducing agents' individual accuracy simultaneously increased their group's diversity, leading to a conflict between the two. These conflicts enabled us to identify certain conditions under which diversity trumps individual accuracy, and vice versa. Specifically, we found that individual accuracy is more important in task environments in which cues differ greatly in the quality of their information, and diversity matters more when such differences are relatively small. Changing the size of a group and the amount of learning by an agent had a limited impact on this general effect of task environment. Furthermore, we found that a group achieves its highest accuracy when there is an intermediate amount of errors in the cue information, regardless of the environment and the heuristic used, an effect that we believe has not been previously reported and warrants further investigation

The Ecological Rationality of Simple Group Heuristics: Effects of Group Member Strategies on Decision Accuracy

The notion of ecological rationality implies that the accuracy of a decision strategy depends on features of the information environment in which it is tested. We demonstrate that the performance of a group may be strongly affected by the decision strategies used by its individual members and specify how this effect is moderated by environmental features. Specifically, in a set of simulation studies, we systematically compared four decision strategies used by the individual group members: two linear, compensatory decision strategies and two simple, noncompensatory heuristics. Individual decisions were aggregated by using a majority rule. To assess the ecological rationality of the strategies, we varied (a) the distribution of cue validities, (b) the quantity, and (c) the quality of shared information. Group performance strongly depended on the distribution of cue validities. When validities were linearly distributed, groups using a compensatory strategy achieved the highest accuracy. Conversely, when cue validities followed a J-shaped distribution, groups using a simple lexicographic heuristic performed best. While these effects were robust across different quantities of shared information, the quality of shared information exerted stronger effects on group performance. Consequences for prescriptive theories on group decision making are discussed Copyright Springer 2006compensatory and noncompensatory decision strategies, group decision making, group performance, simple heuristics,

Knowledge and Performance in Knowledge-Worker Teams: A Longitudinal Study of Transactive Memory Systems

This study examined how transactive memory systems (TMSs) emerge and develop to affect the performance of knowledge-worker teams. Sixty-four MBA consulting teams (261 members) participated in the study. I proposed that the role and function of TMSs change to meet different task and knowledge demands during a project. Hypotheses predicting that TMSs emerge during a project-planning phase as a function of a team's initial conditions, and later develop and mature as a function of the nature and frequency of communication were generally supported, as were hypothesized relationships between TMSs and team performance and viability. Findings suggest that teams with initially distributed expertise and familiar members are more likely to develop a TMS. Frequent face-to-face communication also led to TMS emergence, but communication via other means had no effect. Teams with more established TMSs later benefited from face-to-face communication, but they were less helped by frequent communication via other means, suggesting that transactive retrieval processes may have been triggered during face-to-face communication and suppressed during other types of communication. TMSs were positively related to team viability and team performance, suggesting that developing a TMS is critical to the effectiveness of knowledge-worker teams.knowledge-worker teams, transactive memory