Organizational adaptation to

Annals of Operations Research (Impact Factor: 1.1). 01/2011; abs/1110.4296. DOI: 10.1023/A:1018963630536
Source: DBLP

ABSTRACT A computational model of organizational adaptation in which change occurs at both the strategic and the operational level is presented. In this model, simulated annealing is used to alter the organization's structure even as the agents within the organization learn. Using this model a virtual experiment is run to generate hypotheses which can be tested in multiple venues. The results suggest that, although it may not be possible for organizations of complex adaptive agents to locate the optimal form, they can improve their performance by altering their structure. Moreover, organizations that most successfully adapt over time come to be larger, less dense, with fewer isolated agents, and fewer overlooked decision factors. These results have implications for organizations of both humans and non-humans. For example, they suggest that organizational learning resides not just in the minds of the personnel within the organization, but in the connections among personnel, and among personnel and tasks. These results suggest that collections of non-humans may come to seem more intelligent (i.e., show improved performance) even if the agents remain unchanged if the system simply develops duplicate copies of some of the artificial agents and if the connections among agents are dynamically altered.

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    • "Several organizational Structures for modeling MASs are introduced in literature (Deloach and Matson, 2004), (Horling and Lesser, 2005), (Kolp, Giorgini, Mylopolos, 2006). In addition, a variety of adaptation methods for different organizations have been proposed yet (Carley, 1997), (Dignum et al. ,2004), (Ghijsen, Jansweijer, and Wielinga, 2009), (Ghijsen, Jansweijer, and Wielinga, 2008), (Kirn and Gasser, 1998), (Kota, Gibbins, Jennings, 2009), (Martin and Barak, 2006), (Rosenfeld, Kaminka, Kraus, Shehory, 2008), (Zheng-guang and Xiao-hui, 2006). All of these methods attempt to enhance the system effectiveness using adaptation. "
    ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Volume 2 - Agents, Rome, Italy, January 28-30, 2011; 01/2011
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    • "While organizational structure changes are made to adapt to the task environment change, task accomplishment and schedule are also affected by the change of organizational structure. Organizational adaptation has two different levels: strategic and operational [15], and furthermore it should involve more dimensions besides accomplishment ratio. "
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    ABSTRACT: Agile enterprises with different organizational structures manifest different organizational behaviors when responding to environmental changes. In this paper, we use a computational model to examine organizational adaptation on four dimensions: Agility, Robustness, Resilience, and Survivability. We analyze the dynamics of organizational adaptation by a simulation study on the interaction between tasks and organization in a sales enterprise. The `what if' analyses in different scenarios show that more flexible communication between employees and less hierarchy level with the suitable centralization can improve organizational adaptation. The developed simulation model supports the exploration of the parametric space that defines alternative organizing processes for the optimal strategy given the specified environmental dynamics.
    SIMULATION: Transactions of The Society for Modeling and Simulation International 05/2009; 85:397-413. DOI:10.1177/0037549709105267 · 0.66 Impact Factor
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    • "Research in the field of organizational theory has shown that when an organization is able to adapt its structure, it will be able to operate more efficiently and effectively [2]. Two main types of reorganization can be distinguished; changing the coordination strategy in the organization and changing relations and interaction patterns between agents. "
    6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Honolulu, Hawaii, USA, May 14-18, 2007; 01/2007
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