Search behavior of individuals working in teams: A behavioral study on complex landscapes

Giannoccaro, Ilaria; Galešić, Mirta; Massari, Giovanni Francesco; Barkoczi, Daniel; Carbone, Giuseppe

doi:10.1016/j.jbusres.2019.10.045

Cited by 20 publications

(22 citation statements)

References 56 publications

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“…Previous research points out that the key to improving performance is a proper balance between exploration and exploitation [20,73,122]. Further factors, such as the managerial initiative [90], feedback [46,49], information about the environment [70], task complexity [115], and organizational policies [108] need to be taken into account.…”

Section: Results Related To Interaction Effectsmentioning

confidence: 99%

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Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation

Blanco-Fernández¹,

Leitner²,

Rausch³

2022

Preprint

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Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and adaptation. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group adaptation either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group adaptation too far might backfire and decrease performance significantly.

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Section: Results Related To Interaction Effectsmentioning

confidence: 99%

“…x Indicates interdependencies -Indicates no interdependencies be experts in, e.g., marketing and production management, respectively. Once placed in an area of expertise, the agents' respective capabilities are limited to performing tasks that are within this area [46,59,95].…”

Section: Subtasksmentioning

confidence: 99%

Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation

Blanco-Fernández¹,

Leitner²,

Rausch³

2022

Preprint

View full text Add to dashboard Cite

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“…For example, the importance of coordination and communication is not addressed [21], and neither we do consider the potential costs that individual and collective adaptation might incur. Further extensions of the model include endowing agents with the capabilities of choosing more or less individual adaptation [7] or breaking up the formed team [8], and including alternative mechanisms of adaptation such as social learning [2]. The implementation of these and others aspects might help in extending our research.…”

Section: Discussionmentioning

confidence: 99%

“…Disaster relief rescue operations [18], or project development in consulting firms [3] are examples of tasks that can be characterized as complex tasks. These and many other complex tasks have two characteristics in common: (i) A single individual alone usually cannot find a solution to complex tasks, as the capabilities required to solve them are greater in scope than the ones a single individual possesses [5], and (ii) complex tasks are formed by various subtasks that are interdependent [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-level Adaptation of Distributed Decision-Making Agents in Complex Task Environments

Blanco-Fernández¹,

Leitner²,

Rausch³

2021

Preprint

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To solve complex tasks, individuals often autonomously organize in teams. Examples of complex tasks include disaster relief rescue operations or project development in consulting. The teams that work on such tasks are adaptive at multiple levels: First, by autonomously choosing the individuals that jointly perform a specific task, the team itself adapts to the complex task at hand, whereby the composition of teams might change over time. We refer to this process as selforganization. Second, the members of a team adapt to the complex task environment by learning. There is, however, a lack of extensive research on multi-level adaptation processes that consider self-organization and individual learning as simultaneous processes in the field of management science. We introduce an agentbased model based on the NK-framework to study the effects of simultaneous multi-level adaptation on a team's performance. We implement the multi-level adaptation process by a second-price auction mechanism for self-organization at the team level. Adaptation at the individual level follows an autonomous learning mechanism. Our preliminary results suggest that, depending on the task's complexity, different configurations of individual and collective adaptation can be associated with higher overall task performance. Low complex tasks favour high individual and collective adaptation, while moderate individual and collective adaptation is associated with better performance in case of moderately complex tasks. For highly complex tasks, the results suggest that collective adaptation is harmful to performance.

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“…learning via reinforcement that copying others for particular problems is beneficial) and the social learning of social learning (learning from others how to learn from others; Mesoudi et al, 2016) which may lead to cultural variation in the use of social learning strategies (Glowacki & Molleman 2017; Mesoudi et al 2015). Reward structure in a particular task can promote more or less social learning, with individual exploration being more pronounced at early stages of collective problem solving and with declining payoffs whereas exploitation of previous solutions is more likely later on and when payoffs are good (Billinger et al, 2014;Daniels et al, 2017;Giannoccaro et al, 2020;Sloman et al, 2019). In turn, this affects individual and group performance in fast changing environments, where individual exploration is more useful than copying past solutions (Lejarraga et al, 2014).…”

Section: Co-adaptation Of Cognitive Strategies and Problem Structuresmentioning

confidence: 99%

Beyond collective intelligence: Collective adaptation

Galešić¹,

Barkoczi²,

Berdahl³

et al. 2022

Preprint

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We develop a conceptual framework for studying collective adaptation: the process of iterative co-adaptation of cognitive strategies, social environments, and problem structures. Going beyond searching for “intelligent” collectives, we integrate research from different disciplines to show how collective adaptation perspective can help explain why similar collectives can follow very different and sometimes counter-intuitive trajectories. We further discuss how this perspective explains why successful collectives appear to have a general collective intelligence factor, why collectives rarely optimize their behaviour for a single problem, why their behaviours can appear myopic, and why playful exploration of alternative social systems can be useful. We describe different approaches for the study of collective adaptation, including computational models inspired by evolution and statistical physics. The framework of collective adaptation enables the integration and formalization of knowledge about human collective phenomena and opens doors to a rigorous transdisciplinary pursuit of important outstanding questions about human sociality.

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Search behavior of individuals working in teams: A behavioral study on complex landscapes

Cited by 20 publications

References 56 publications

Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation

Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation

Multi-level Adaptation of Distributed Decision-Making Agents in Complex Task Environments

Beyond collective intelligence: Collective adaptation

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