The potential for social contextual and group biases in team decision-making: biases, conditions and psychological mechanisms

Jones, Paul; Roelofsma, Peter

doi:10.1080/00140130050084914

Cited by 137 publications

(100 citation statements)

References 54 publications

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“…Computational simulations indicate that excessively frequent interaction can be detrimental to teams, resulting in the implicit prioritization of consensus over the search for good solutions [30]. This shift in priorities within a team is similar to a psychological phenomenon known as groupthink which can be harmful to decision-making teams [31]. In contrast, less frequent interaction may lead to the formation of weak ties between members of a team [30].…”

Section: Md-16-1497mentioning

confidence: 99%

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

McComb¹,

Kotovsky²,

Cagan³

2018

Preprint

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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.

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Section: Md-16-1497mentioning

confidence: 99%

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

McComb¹,

Kotovsky²,

Cagan³

2018

Preprint

View full text Add to dashboard Cite

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“…They propose that a decision error refers to 'those occasions when the team's decisionmaking activities fail to achieve its intended outcome'. 131 From this perspective, a DMC decision would be considered to be an error either if current trial participants were exposed to harm from the experimental drug or procedure, or if future patients were unable to benefit from it.…”

Section: Error and Bias In Small Group Decision-makingmentioning

confidence: 99%

Issues in data monitoring and interim analysis of trials

Grant

Altman²,

Babiker³

et al. 2005

Health Technol Assess

146

175

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“…Such a state is similar in many ways to groupthink, a psychological phenomena characterized by the search for consensus with little regard for critical evaluation of concepts (Janis, 1971). It has been theorized that groupthink may detrimentally affect decision-making teams (Jones & Roelofsma, 2000).…”

Section: Investigating Team Strengths With Cisatmentioning

confidence: 99%

Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

2015

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Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena, thus balancing simplicity and direct applicability. This paper discusses the model's composition, and demonstrates its utility through simulating human design teams in a cognitive study. Simulation results are compared directly to the results from human designers. The CISAT model is also used to identify the most beneficial characteristics in the cognitive study. Keywords: computational model, design cognition, teamwork, engineering design Much cognitive research in engineering design has focused on individuals, despite the fact that most engineering design is actually performed by teams (Paulus, Dzindolet, & Kohn, 2011). This work focuses on developing a better understanding of team-based design through computationally simulating the team design process. Empirical studies are a common means for exploring design cognition and for testing new design methodologies. However, these studies can incur a high personnel cost while only returning a limited amount of data. It can also be difficult to isolate the effects of specific characteristics. This work introduces a computational framework that simulates teambased engineering design through creating software agents that directly solve engineering problems. In addition to offering a resource efficient test bed for evaluating design strategies, this framework can be used to test the conclusions from cognitive studies. It can be used to peel apart aspects of human design, and provides a succinct representation of designer behavior. The purpose of the framework is not to replace cognitive studies, but rather to augment traditional methods of investigation, accelerating the discovery of improved design methodologies.A significant amount of work has attempted to simulate the performance of both teams and individuals (Fan & Yen, 2004). For instance, both the Virtual Design Team model, and another model applied to teams at NASA's Jet Propulsion Laboratory, incorporate detailed descriptions of design team organization and interaction (Jin & Levit, 1996;Olson, Cagan, & Kotovsky, 2009). Both models were used to simulate complex design tasks, but were also burdened by high model complexity. For instance, the model by Olson et al. (2009) used approximately 1000 distinct variables, and required nearly 100000 lines of code for implementation. Still other work has utilized agent-based models to explore the formation of mental models during team problem-solving with respect to both interaction structure (Dionne, Sayama, Hao, & James, 2010) and agent memory (Sayama, Farrell, & Dionne, 2011). Mental ...

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The potential for social contextual and group biases in team decision-making: biases, conditions and psychological mechanisms

Cited by 137 publications

References 54 publications

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

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

Issues in data monitoring and interim analysis of trials

Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

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