Supporting the evolution and interoperability of organisational models with e-learning technologies

被引:0
|
作者
Weichhart, Georg [1 ,2 ]
机构
[1] Johannes Kepler Univ Linz, Commun Engn Business Informat, A-4040 Linz, Austria
[2] Metasonic GmbH, Pfaffenhofen, Germany
关键词
COMPLEXITY; SUSTAINABILITY; FRAMEWORK;
D O I
10.1016/j.arcontro1.2015.03.011
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Models are instruments that allow agents to gain understanding and plan future steps required for being sustainable. Unfortunately, social, economical and ecological systems are in constant flux. The modelling process in a dynamic environment is a never ending, ongoing concern. As a theoretical lens for the analysis of enterprise sustainability the theory of Complex Adaptive Systems (CAS) is used. This theory allows to capture dynamic aspects in models. Theoretical aspects of CASs are briefly introduced and used to analyse learning support for the active agents in the enterprise. A learning system is conceptualised. Support for enterprise models in general and the enterprises' active agents in particular is discussed to show how to support getting along with the dynamics of the overall system. Interoperability is derived as a key property of the overall system. Interoperability requires system-parts to be independent, while supporting the overall system's functionality. On the one hand multi-faced problems are independently analysed by active agents. On the other hand partial solutions realised by active agents need to be interoperable on the enterprise level. Taking a CAS point of view interoperability between system parts becomes a necessity, which, if not met, might bring the overall system to a halt. Requirements and properties for a support environment for organisations' agents to keep pace with permanent changes in their environments are described. Technologies are presented that fit to the used theoretical point of view and support both: the individual evolution and learning of agents who update local models and the interoperability between models. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:118 / 127
页数:10
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