Abstract
The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton Adaption-Innovation construct, which places each individual somewhere along the spectrum of cognitive style preference. Using the model, we investigate the interacting effects of a team’s communication patterns, specialization, and cognitive style composition on design performance. By simulating cognitive style in the context of team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.
Highlights
Current design research frequently draws conclusions based on small-scale behavioral experiments
After reviewing related work in adaption–innovation theory and agent-based modeling, this paper describes the development of Kirton Adaption–Innovation Inventory agentbased organizational optimization model (KABOOM) and discusses the results of several computational experiments on team specialization, communication, and cognitive style composition
Results from the specialization study suggest that the optimal amount of team specialization depends strongly on the cognitive styles of its members
Summary
Current design research frequently draws conclusions based on small-scale behavioral experiments. Though valuable, these studies are severely limited in scope, and the results are difficult to generalize. Design cognition research must continue its trend toward more rigorous modeling of individual cognitive differences among designers to support more accurate simulations; cognitive style, or one’s preference for managing structure in solving problems, is one key example. More adaptive problem solvers aim to do things better by using incremental changes to continuously improve a system or solution. Problems that require adherence to a given structure, meticulous attention to detail, and conformance to specific rules or standards (e.g., repairing an antique grandfather clock, tuning a nuclear reactor) will tend to favor a more adaptive approach, more innovative methods will still yield some kind of solution. Problems that require spanning multiple disciplines, taking a systems view, and challenging current practice
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