Application of Agent-Based Modeling (ABM) in Organizational Research on Teams and Groups

Archibold, Estelle; Bao, Lili; Coen, Corinne A.; Grand, James A.; Gupta, Pranav; Trinh, Mai P.

doi:10.5465/ambpp.2019.15008symposium

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“…As it has been shown that behavioral mechanisms within the HoneyComb paradigm can be explained by basic optimization processes (epsilon greedy: Chapter 3.2; future state optimization: Hornischer et al, 2022), other optimization methods might hold interesting insights as well (e.g., structural transitions; Hornischer et al, 2019). Next to applying differing variants of reinforcement learning algorithms Hązła et al, 2021;Tickle et al, 2021), approaches of agent-based modeling might create the opportunity to test boundary conditions and influence parameters to inform the designs of empirical studies (Archibold et al, 2019).…”

Section: Future Directionsmentioning

confidence: 99%

Emergent processes as means of uncertainty reduction in group decision making

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This dissertation investigates how emergent processes can reduce uncertainty in group decision making. Emergent processes are processes that arise from individual characteristics and social interactions and result in higher level properties of a group. The following three research questions are addressed: Can groups reduce uncertainty through emergent processes? Which emergent processes can be identified? How can we analyze and visualize these emergent processes? An interdisciplinary theoretical framework was developed (Chapter 2), spanning theory and empirical evidence from biology, machine-learning, and psychology. Uncertainty is distinguished into informational uncertainty (i.e., uncertainty about the environment) and personal uncertainty (i.e., uncertainty about relationships and group characteristics). The effects of three emergent processes (collective cognition, group cohesion, and leader-/followership) on uncertainty reduction are investigated in groups.Chapter 3 presents two empirical papers in which groups were faced with informational uncertainty in an intellective problem-solving task within the HoneyComb paradigm . The HoneyComb paradigm is a virtual game platform in which participants, represented by an avatar on the playing field, can interact only through movement on the playing field while solving group tasks. Specifically, groups had to infer the best out of four options presented to them on the playing field by repeatedly choosing from the options (exploration). Groups in these studies reduced informational uncertainty by inferring the best option through collective cognition (i.e., pooling of individual information on the collective level) and exhibited exploration/exploitation patterns. The quality of information available to the groups impacted the group decision process: Information that was corrupted by group incentives (i.e., rewards for staying close to other group members on the playing field) led to sub-optimal decisions. A simulation study using the ε-greedy algorithm exhibited similar results. Behavioral leader-/followership patterns emerged spontaneously within most groups and were associated with self-reported leader-/followership. However, behavioral leadership was not associated with typical personality trait correlates (selfconfidence, achievement maximization, decisiveness, and risk propensity). Behavioral group cohesion was associated with self-reported group entitativity and interactivity. Chapter 3 concludes that groups effectively reduce informational uncertainty using emergent processes (i.e., group cohesion, leader-/followership), although the differential contribution of single processes remains to be investigated.Chapter 4 presents empirical findings on the reduction of personal uncertainty through the emergence of collective trust. Collective trust is defined as a collective cognitive construct that emerges through repeated interactions of a group and reflects the shared level of trust a group holds for another individual, group, or organization. In Chapter 4, groups h...

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Section: Future Directionsmentioning

confidence: 99%

Emergent processes as means of uncertainty reduction in group decision making

Ritter¹

View full text Add to dashboard Cite

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Application of Agent-Based Modeling (ABM) in Organizational Research on Teams and Groups

Cited by 1 publication

References 0 publications

Emergent processes as means of uncertainty reduction in group decision making

Emergent processes as means of uncertainty reduction in group decision making

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