This paper addresses the lack of multidimensional research on emergent phenomena and interactions between mechanisms at different levels in organizations. The study takes a multifaceted perspective on individual learning and autonomous group formation and adaptation, exploring how these two levels interact to affect task performance. To analyze the interactions between the two levels, the authors introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. The study explores organizations that promote learning and group adaptation either simultaneously or sequentially, analyzing the interactions between these activities and their effects on performance. The results indicate that promoting learning and adaptation can increase task performance. However, pushing individual learning and group adaptation too far may backfire, leading to decreased performance. The study also shows that there are interaction effects between individual learning and group adaptation, which are moderated by task decomposability. If individuals are highly engaged in learning at the individual level, changing the composition of a group may decrease performance when tasks are interdependent. However, if individuals only learn to a minor extent, changing the composition of a group from time to time is beneficial for task performance. Overall, this study contributes to previous research by extending traditional unidimensional perspectives on either the individual or group level in organizations. It highlights the importance of considering multiple levels when analyzing organizational phenomena such as learning and adaptation for improved task performance. The availability of simulation data and code provides opportunities for further exploration into this area of research.
There is not enough research on how different parts of organizations work together. Learning and adapting as individuals and groups can help get things done better, but it's important to find the right balance. A computer model was made to study this. Looking at different levels of an organization can help us understand how to do things better.
Definitions
- Multidimensional: having many different aspects or features
- Emergent phenomena: unexpected or new behaviors or patterns that arise from interactions between different parts of a system
- Mechanisms: the processes or systems that make something work
- Autonomous group formation: when a group forms on its own, without being told to by someone else
- Adaptation: changing in response to new situations or information
- Agent-based model: a computer simulation that models the behavior of individual agents (like people) and their interactions with each other and their environment
- Task decomposability: how easy it is to break down a task into smaller parts
Exploring the Interactions between Individual Learning and Group Adaptation in Organizations
Organizations are complex systems composed of multiple levels, and understanding how these different levels interact is essential for improving task performance. This research paper takes a multifaceted perspective on individual learning and autonomous group formation and adaptation, exploring how these two levels interact to affect task performance. The authors introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. By analyzing organizations that promote learning and group adaptation either simultaneously or sequentially, the study reveals important insights into the interactions between individual learning and group adaptation as well as their effects on performance.
Background: Multidimensional Research on Emergent Phenomena
Previous research has often taken a unidimensional approach when studying organizational phenomena such as learning and adaptation. However, this study argues that it is necessary to consider multiple levels when analyzing these phenomena for improved task performance. To address the lack of multidimensional research on emergent phenomena in organizations, this paper explores how individual learning at one level interacts with group adaptation at another level to affect task performance.
Methodology: Agent-Based Modeling
To analyze the interactions between individual learning and group adaptation, the authors introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn from experience but are limited in their rationality due to bounded cognition. The model simulates organizations that promote learning and group adaptation either simultaneously or sequentially over time while measuring task performance outcomes such as efficiency, effectiveness, coordination costs, etc.
Results: Effects of Individual Learning & Group Adaptation on Performance
The results indicate that promoting both individual learning and group adaptation can increase task performance by allowing individuals to develop skills relevant for completing tasks more efficiently while also enabling groups to adapt quickly to changing conditions through collective problem solving capabilities. However, pushing individual learning or group adaptation too far may backfire leading to decreased performance due to cognitive overload or ineffective coordination within groups respectively.
Furthermore, there were interaction effects between individual learning and group adaptations which were moderated by task decomposability; if individuals were highly engaged in learning at the individual level then changing the composition of a group could decrease overall performance when tasks were interdependent whereas if individuals only learned minorly then changing up groups from time-to-time was beneficial for overall task completion success rates regardless of whether tasks were interdependent or not (i.e., decomposable).
Conclusion: Extending Unidimensional Perspectives
Overall this study contributes significantly towards extending traditional unidimensional perspectives on either just the individual or just the collective level within organizations by demonstrating how considering both together can lead towards improved outcomes regarding organizational goal achievement (i.e., increased productivity/task completion success rates). Additionally given its simulation data availability it provides opportunities for further exploration into this area of research which could potentially uncover even more useful insights about emergent phenomena occurring within multi-level systems like organizations!
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.
