Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

Abstract

The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams that has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. McComb MD-16-1497 2These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problemproperties. Further analysis reveals hypotheses about how the problem properties can influence a team's search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

Designing Like a Human: Simulating Cognitive Style in Teamwork with an Agent-Based Model

“…The CISAT framework [33,53,42] is another agent-based model which uses contextualized problems to study how problem characteristics affect the optimal team process and team characteristics. The CISAT framework reflects eight characteristics of both team activity and individual cognition, namely: organic interaction timing, quality-informed solution sharing, quality bias re-duction, self-bias, operational learning, breadth versus depth solution search, and satisficing [33].…”

“…In McComb et al [42], the CISAT model is used to find optimal team characteristics based on properties of the problem being addressed. These two models provide much of the inspiration for KABOOM, the agent-based model presented in the following chapter.…”

Designing Improved Teams for Crowdsourced Competitions

“…In the current work, nominal teams are simulated via a bootstrapping approach in which several individuals from the Meta Kaggle dataset are randomly selected and the best solution found by any individual in that group is taken as the team solution. This methodology has been used in several other studies [11,43,44]. Although other work on concept generation tends to sum or average the outputs of individuals in nominal teams, the approach used here of taking the best solution depicts a reasonable approach to using nominal teams for solving tasks with well-defined and quantitative objectives.…”

“…Even though teams are common in industry [1,2], there is little agreement between definitions of the word team that have been proposed in the literature (for examples see [7][8][9][10]). However, most definitions share two concepts: multi-agency (the composition of a team as two or more individuals) and communication (the ability of those individuals to exchange information) [11]. These two concepts are fundamental to understanding team-based solutions.…”

Collaborating With Style: Using an Agent-Based Model to Simulate Cognitive Style Diversity in Problem Solving Teams

“…Bernstein et al [23] showed that intermittent (rather than constant) collaboration can provide the benefits of constant collaboration, as well as the benefits of individual work. In some cases, zero communication yields optimal performance [7,24]. It is important to note that these results are often highly problem dependent.…”