Individuals and their interactions in demand planning processes: an agent-based, computational testbed

Hauke, Jonas; Lorscheid, Iris; Meyer, Matthias

doi:10.1080/00207543.2017.1377356

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…Team cognition emerges fairly naturally when people work face-to-face in shared workspaces (Gutwin and Greenberg 2004). But team cognition is complex and more than the mere sum of the individual team members' cognition; it emerges from the interplay of each team member's individual cognition and the team processes (Cooke et al 2013;Hollan et al 2000;Hauke et al 2018). In this paper, both teams and groups in general are seen as dynamic systems of interacting individuals, modeled as agents.…”

Section: Related Literature and Methodsmentioning

confidence: 99%

Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

Lorscheid¹,

Meyer

2021

J Bus Econ

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Despite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.

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Section: Related Literature and Methodsmentioning

confidence: 99%

Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

Lorscheid¹,

Meyer

2021

J Bus Econ

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“…From a cognitive perspective, we frame risk workshops as an example of distributed cognition. Distributed cognition means that groups make use of individuals' knowledge by combining their expertise (Hauke et al, 2018). Specifically, we rely on transactive memory, a mechanism through which risk workshop participants learn about each other's expertise (i.e.…”

Section: Risk Assessment Process: Ideal Conditions and Deviationsmentioning

confidence: 99%

Talking about the likelihood of risks: an agent-based simulation of discussion processes in risk workshops

Harten

Meyer

Bellora-Bienengräber

2021

JAOC

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Purpose This paper aims to explore drivers of the effectiveness of risk assessments in risk workshops. Design/methodology/approach This study uses an agent-based model to simulate risk assessments in risk workshops. Combining the notions of transactive memory and the ideal speech situation, this study establishes a risk assessment benchmark and then investigates real-world deviations from this benchmark. Specifically, this study models limits to information transfer, incomplete discussions and potentially detrimental group characteristics, as well as interaction patterns. Findings First, limits to information transfer among workshop participants can prevent a correct consensus. Second, increasing the required number of stable discussion rounds before an assessment improves the correct assessment of high but not low likelihood risks. Third, while theoretically advantageous group characteristics are associated with the highest assessment correctness for all risks, theoretically detrimental group characteristics are associated with the highest assessment correctness for high likelihood risks. Fourth, prioritizing participants who are particularly concerned about the risk leads to the highest level of correctness. Originality/value This study shows that by increasing the duration of simulated risk workshops, the assessments change – as a rule – from underestimating to overestimating risks, unraveling a trade-off for risk workshop facilitators. Methodologically, this approach overcomes limitations of prior research, specifically the lack of an assessment and process benchmark, the inability to disentangle multiple effects and the difficulty of capturing individual cognitive processes.

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Framework for Simulation-Based Decision Making in Semiconductor Value Chains

Mönch

Ehm

Ponsignon

2020

Lecture Notes in Electrical Engineering

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Individuals and their interactions in demand planning processes: an agent-based, computational testbed

Cited by 7 publications

References 41 publications

Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

Talking about the likelihood of risks: an agent-based simulation of discussion processes in risk workshops

Framework for Simulation-Based Decision Making in Semiconductor Value Chains

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