Collaborative problem solving can be successful or coun- terproductive. The performance of collaborative teams depends not only on team members’ abilities, but also on their cognitive styles. Cognitive style measures differences in problem-solving behavior: how people generate solutions, manage structure, and interact. While teamwork and problem solving have been stud- ied separately, their interactions are less understood. This pa- per introduces the KAI Agent-Based Organizational Optimiza- tion Model (KABOOM), the ﬁrst model to simulate cognitive style in collaborative problem solving. KABOOM simulates the performance of teams of agents with heterogeneous cognitive styles on two contextualized design problems. Results demon- strate that, depending on the problem, certain cognitive styles may be more effective than others. Also, intentionally aligning agents’ cognitive styles with their roles can improve team per- formance. These experiments demonstrate that KABOOM is a useful tool for studying the effects of cognitive style on collabo- rative problem solving. 1 INTRODUCTION Cognitive style can play an important role in collaborative problem solving [1], but its effects are not well understood. Cog- nitive style is deﬁned as an individual’s preferred manner of man- aging structure as they solve problems, make decisions, and seek to bring about change. In one of the few studies examining in- Address all correspondence to this author.teractions of cognitive style with team problem-solving perfor- mance, Hammerschmidt [2] found that aligning team members’ cognitive styles with tasks improved performance. This suggests that if the effects of cognitive style were better understood, the formation of engineering teams could be informed by the cog- nitive style of team members, as well as their domain expertise and ability. This paper addresses a gap in design research by studying the effects of cognitive style on collaborative problem solving using an agent-based model. Most previous research on collaboration and team perfor- mance is based on qualitative descriptions of small studies [3], as large in-vivo studies over long periods of time are expensive. The results of these small-scale studies are difﬁcult to generalize and tend to be applicable only in speciﬁc contexts [4]. Computa- tional methods can be used to supplement traditional behavioral studies, advancing the pace of scientiﬁc development and provid- ing better tools for managers. Speciﬁcally, agent-based models can provide rapid insight into the effects of team composition and structure on team performance, which enables the comparison of many different team scenarios [5]. Several recent publications demonstrate that simulation can be an effective tool for studying human systems in engineering design [4, 6–9]. Because human systems are messy and hard to decompose for scientiﬁc study, simulations like these are useful for isolating independent vari- ables when studying cognition and social interaction [10]. However, translating the results of computational experi- ments to a real-world context can be challenging, especially when the simulated problem and environment are abstract. Some 1 Copyright c 2019 by ASME existing models of engineering teamwork incorporate contextu- alized, real-world problems [6, 8, eg], while others use more ab- stract objectives [10, 11, eg]. Abstract problems offer simplicity and ﬂexibility, which can mean less development time and faster simulations. An abstract problem can also be modiﬁed arbitrarily because it is not tied to physical systems, so one abstract problem can have inﬁnite variations (for instance, a parabolic objective function could have any linear and quadratic coefﬁcients). How- ever, abstract problems often lack the complex multivariate inter- actions and constraints of real-world problems. Using an abstract objective makes it difﬁcult to tie meaning to performance, but the results of a contextualized problem can be directly compared to real-world outcomes. Ideally, simulations should closely reﬂect real-world problems and scenarios so their results can be com- pared directly to the performance of human teams. This paper implements two contextualized problems (a simple beam-design problem and a multi-objective race car-design problem) in order to evaluate the performance of simulated teams in an agent-based model. Additionally, modeling human systems is difﬁcult because human problem solvers are diverse, unpredictable, and some- times irrational. Some problem-solving traits can be captured by cognitive style, a construct deﬁned by cognitive p