Simulating Collective Intelligence of the Communities of Practice Using Agent-Based Methods

Author(s):  
Emil Scarlat ◽  
Iulia Maries
2016 ◽  
Vol 27 (2) ◽  
pp. 218-241 ◽  
Author(s):  
Kristie A. McHugh ◽  
Francis J. Yammarino ◽  
Shelley D. Dionne ◽  
Andra Serban ◽  
Hiroki Sayama ◽  
...  

Author(s):  
Zaiyong Tang ◽  
Subramanian Sivaramakrishnan

Today’s enterprises must go beyond traditional goals of efficiency and effectiveness; they need to be intelligent in order to adapt and survive in a continuously changing environment (Liebowitz, 1999). An intelligent organization is a living organism, where all components and subsystems work coherently to enable the enterprise to maximize its potential in its goal-driven endeavors. Stonier (1991) suggested that intelligent organizations must have not only intelligent individuals, but also “collective intelligence” that is created through integration of intelligence from sub-units of the organization. Researchers have developed frameworks for building organizations around intelligence, as opposed to traditional approaches that focus on products, processes, or functions (e.g., McMaster, 1996; Liang, 2002). Analogous to intelligent biological life, an intelligent organization has a life of its own. An intelligent enterprise understands its internal structure and activities, as well as external forces such as market, competition, technology, and customers. It learns and adapts continuously to the changing environment. The learning and adaptation are achieved through real-time monitoring of operations, listening to customers, watching the markets, gathering and analyzing data, creating and disseminating knowledge, and making intelligent decisions.


Kybernetes ◽  
2015 ◽  
Vol 44 (6/7) ◽  
pp. 1122-1133 ◽  
Author(s):  
Erika Suárez Valencia ◽  
Víctor Bucheli ◽  
Roberto Zarama ◽  
Ángel Garcia

Purpose – The purpose of this paper is to focus on the underpinning dynamics that explain collective intelligence. Design/methodology/approach – Collective intelligence can be understood as the capacity of a collective system to evolve toward higher order complexity through networks of individual capacities. The authors observed two collective systems as examples of the dynamic processes of complex networks – the wiki course PeSO at the Universidad de Los Andes, Bogotá, Colombia, and an agent-based model inspired by wiki systems. Findings – The results of the wiki course PeSO and the model are contrasted with a random network baseline model. Both the wiki course and the model show dynamics of accumulation, in which statistical properties of non-equilibrium networks appear. Research limitations/implications – The work is based on network science. The authors analyzed data from two kinds of networks: the wiki course PeSO and an agent-based model. Limitations due to the number of computations and complexity appeared when there was a high order of magnitude of agents. Practical implications – Better understanding can allow for the measurement and design of systems based on collective intelligence. Originality/value – The results show how collective intelligence emerges from cumulative dynamics.


2019 ◽  
Vol 31 ◽  
pp. 320-331 ◽  
Author(s):  
Sandro M. Reia ◽  
André C. Amado ◽  
José F. Fontanari

2011 ◽  
pp. 216-232
Author(s):  
Maria Chiara Caschera ◽  
Arianna D’Ulizia ◽  
Fernando Ferri ◽  
Patrizia Grifoni

This chapter provides a classification of virtual communities of practice according to methods and tools offered to virtual community members for the knowledge management and the interaction process. It underlines how these methods and tools support users during the exchange of knowledge, enable learning, and increase the user ability to achieve individual and collective goals. In this chapter virtual communities are classified in virtual knowledge-sharing communities of practice and virtual learning communities of practice according to the collaboration strategy. A further classification defines three kinds of virtual communities according to the knowledge structure: ontology-based VCoP; digital library-based VCoP; and knowledge map-based VCoP. This chapter also describes strategies of interaction used to improve the knowledge sharing and learning in groups and organizations. It shows how agent-based method supports interaction among community members, improves the achievement of knowledge, and encourages the level of user participation. Finally, this chapter presents the system’s functionalities that support browsing and searching processes in collaborative knowledge environments.


Author(s):  
Demosthenes Akoumianakis

The chapter motivates and presents an approach for assembling innovative information-based products and services by virtual cross-organization communities of practice. Using a case study on assembling vacation packages, we describe the cross-organizational virtual partnership, the mechanics allowing it to operate as a virtual community of practice and how collective intelligence of the members is appropriated to ensemble innovative information-based products for tourists. The results provide useful insights into innovating through virtual networking as well as the ICT tools that may be used to foster value-creating networks of practice in boundary spanning domains.


2009 ◽  
Vol 7 (3) ◽  
pp. 479-499
Author(s):  
Yaniv (Junno) Ophir

Architectural programming is the research and decision-making process that identifies the scope of work to be designed. Programming is difficult because it involves identifying, collecting, analyzing and updating information from different sources such as engineers, clients, users, consultants, and others. In this paper I propose a computational model for programming and describe its implementation, a tool called PENA that allows a programming expert to represent different processes and people involved in a project using intelligent agents. By delegating responsibility to agents, a programming expert can better organize and manage project data as well as find creative solutions to conflicting issues through agent negotiation. As a proof-of-concept, I show how an agent, called the Arch-Learner, manages adjacencies of rooms in a simple program for a house by clustering them into public and private rooms. I conclude with a discussion of future work and development of PENA.


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