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

Author(s):  
Samuel Lapp ◽  
Kathryn Jablokow ◽  
Christopher McComb

Abstract Collaborative problem solving can be successful or counterproductive. 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 studied separately, their interactions are less understood. This paper introduces the KAI Agent-Based Organizational Optimization 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 demonstrate 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 performance. These experiments demonstrate that KABOOM is a useful tool for studying the effects of cognitive style on collaborative problem solving.

2019 ◽  
Author(s):  
Christopher McComb ◽  
Kathryn Jablokow ◽  
Samuel Lapp

Collaborative problem solving can be successful or counterproductive. 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 studied separately, their interactions are less understood. This paper introduces the KAI Agent-Based Organizational Optimization 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 demonstrate 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 performance. These experiments demonstrate that KABOOM is a useful tool for studying the effects of cognitive style on collaborative problem solving.


2019 ◽  
Author(s):  
Samuel Lapp

This thesis describes the development of an agent-based model for simulating cognitive style in the context of collaborative problem solving. Cognitive style describes the diverse ways in which people solve problems. Individuals’ cognitive styles can impact the success or failure of a design team. However, the effects of cognitive style in collaborative problem solving are not well understood. To address this gap, this thesis presents KABOOM (KAI Agent-Based Organizational Optimization Model), the first agent-based model of teamwork to incorporate cognitive style. In this thesis, experiments using KABOOM investigate the interacting effects of a design team’s communication patterns, specialization, and cognitive style composition on a team’s performance. Testing the model with a race car design problem reveals that teams can strategically leverage diversity of cognitive style to improve performance. By simulating cognitive style and team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.


2021 ◽  
Vol 58 (2) ◽  
pp. 841-848
Author(s):  
Agus Setiawan Et al.

This study aims at identifying the effect of collaborative problem solving (CPS) learning strategies on students' mathematical reasoning abilities with different cognitive styles, namely field-dependent (FD) and field-independent (FD). This study is a quasi-experimental study with a 2x2 factorial design. A total of 103 students of SMPN 3 Mesuji, Indonesia as research subjects. Mathematical reasoning ability data were obtained from essay tests and cognitive style data were obtained from the GEFT test. Data analysis used two-way analysis of variance (ANOVA) test. The results of this study are: 1) there were significant differences in mathematical reasoning abilities between students who received collaborative problem solving and direct instruction learning strategies, 2) there were significant differences in mathematical reasoning abilities between students who had field dependent and field independent cognitive styles, 3) there was no significant interaction between different learning strategies (collaborative problem solving and direct instruction) and cognitive styles (field dependent and field independent) on mathematical reasoning abilities.


2021 ◽  
Vol 10 (7) ◽  
pp. 247
Author(s):  
Elizabeth McGhee Hassrick ◽  
Wendy Shih ◽  
Heather Nuske ◽  
Sarah Fulton Vejnoska ◽  
Samantha Hochheimer ◽  
...  

Children with autism situated in lower income families often receive intensive educational interventions as their primary form of treatment, due to financial barriers for community interventions. However, the continuity of care can be disrupted by school transitions. The quality of social relationships during the transition to a new school among parents, school staff and community providers, called the team-around-the-child (TAC), can potentially buffer a child with autism from the adverse effects caused by care disruptions. Qualities of social relationships, including trust and collaborative problem solving, can be measured using social network analysis. This study investigates if two different types of TAC relationships, defined as (1) the level of trust among team members and (2) the degree of collaborative problem solving among team members, are associated with perceived successful transitions for children with autism from lower income families. Findings suggested that TAC trust is significantly associated with the outcome of transition success for children with autism immediately post-transition.


2016 ◽  
Vol 2016 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Yoav Bergner ◽  
Jessica J. Andrews ◽  
Mengxiao Zhu ◽  
Joseph E. Gonzales

2015 ◽  
Vol 8 (2) ◽  
pp. 281-284 ◽  
Author(s):  
Ronald E. Riggio ◽  
Karan Saggi

In only a very few places, Neubert, Mainert, Kretzschmar, & Greiff (2015) mention the role of communication and coordination among team members in collaborative problem solving. Although complex and collaborative problem solving is indeed an imperative for team and organizational success in the 21st century, it is easier said than done. Collaborative problem solving is critically dependent on the communication and interaction skills of the team members and of the team leader. The intent of this commentary is to shine a light on the critical role of interpersonal and communication skills in complex and collaborative problem solving.


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