Supporting the Complexity of Inquiring Organizations

2011 ◽  
pp. 109-132
Author(s):  
Dianne J. Hall ◽  
Yi Guo
Keyword(s):  
Complex Systems ◽  
Multiagent System ◽  
Support Systems ◽  
Systems Design ◽  
Agent Technology ◽  
Technological Support ◽  
Organization Support

This chapter examines the issue of technological support for inquiring organizations and suggests that the complexity of these organizations is best supported by a technology of equal complexity—that is, by agent technology. Agents and the complex systems in which they are active are ideal for supporting not only the activity of Churchman’s inquirers but also those components necessary to ensure an effective environment. Accordingly, a multiagent system to support inquiring organizations is introduced. By explaining agent technology in simple terms and by defining inquirers and other components as agents working within a multiagent system, this chapter demystifies agent technology, enables researchers to grasp the complexity of inquiring organization support systems, and provides the foundation for inquiring organization support systems design.

scholarly journals Modeling multidisciplinary design with multiagent learning

2018 ◽  
Vol 33 (1) ◽  
pp. 85-99 ◽  
Author(s):  
Daniel Hulse ◽  
Kagan Tumer ◽  
Christopher Hoyle ◽  
Irem Tumer
Keyword(s):  
Complex Systems ◽  
Multiagent System ◽  
Systems Design ◽  
Organizational Outcomes ◽  
Learning System ◽  
Multidisciplinary Teams ◽  
Design Teams ◽  
Multiagent Learning ◽  
Complex Engineered Systems ◽  
Design Variables

AbstractComplex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem in a coordinated way. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams – instead either representing designers as agents which control all variables, measuring organizational outcomes instead of design outcomes, or representing different aspects of distributed design, such as negotiation. This paper presents a new model which captures the distributed nature of complex systems design by decomposing the ability to control design variables to individual computational designers acting on a problem with shared constraints. These designers are represented as a multiagent learning system which is shown to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration in complex systems design.

Hierarchical Choice Modeling to Support Complex Systems Design

2012 ◽  
pp. 205-233 ◽  
Author(s):  
Wei Chen ◽  
Christopher Hoyle ◽  
Henk Jan Wassenaar
Keyword(s):  
Complex Systems ◽  
Systems Design ◽  
Choice Modeling

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

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