Revision as of 18:00, March 10, 2013 by Emijrp (Text replace - "<h2> Datasets </h2> There is no datasets for this keyword. <h2> Tools </h2> There is no tools for this keyword. <br clear="all" /> <h2> Publications </h2> There is no publications with this keyword." to "")
(Alternative names for this keyword)
|Related keyword(s)||wiki simulation|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of keywords|
simulation is included as keyword or extra keyword in 0 datasets, 0 tools and 4 publications.
There is no datasets for this keyword.
There is no tools for this keyword.
|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Open collaboration for innovation: Principles and performance||Levine S.S.
|Organization Science||English||2014||The principles of open collaboration for innovation (and production), once distinctive to open source software, are now found in many other ventures. Some of these ventures are Internet based: for example, Wikipedia and online communities. Others are off-line: they are found in medicine, science, and everyday life. Such ventures have been affecting traditional firms and may represent a new organizational form. Despite the impact of such ventures, their operating principles and performance are not well understood. Here we define open collaboration (OC), the underlying set of principles, and propose that it is a robust engine for innovation and production. First, we review multiple OC ventures and identify four defining principles. In all instances, participants create goods and services of economic value, they exchange and reuse each other's work, they labor purposefully with just loose coordination, and they permit anyone to contribute and consume. These principles distinguish OC from other organizational forms, such as firms or cooperatives. Next, we turn to performance. To understand the performance of OC, we develop a computational model, combining innovation theory with recent evidence on human cooperation. We identify and investigate three elements that affect performance: the cooperativeness of participants, the diversity of their needs, and the degree to which the goods are rival (subtractable). Through computational experiments, we find that OC performs well even in seemingly harsh environments: when cooperators are a minority, free riders are present, diversity is lacking, or goods are rival. We conclude that OC is viable and likely to expand into new domains. The findings also inform the discussion on new organizational forms, collaborative and communal.||0||0|
|Enhancing Critical Reflection on Simulation Through Wikis||Beyer D.A.||Clinical Simulation in Nursing||English||2012||This article discusses using wikis as a teaching strategy for follow-up debriefing after students participated in human patient simulator activities. Wiki tools assist students in collaborating, sharing, creating, and editing documents that reinforce learning developed during simulation. Students are actively engaged in the learning process by sharing information and experiences obtained in simulation scenarios. The use of a wiki enhances debriefing reflection. Wikis provide students with a written document to answer questions, discuss content, and develop notes and study guides after a classroom simulation activity.||0||0|
|Velo: Riding the knowledge management wave for simulation and modeling||Gorton I.
|Proceedings - International Conference on Software Engineering||English||2011||Modern scientific enterprises are inherently knowledge-intensive. In general, scientific studies in domains such as geosciences, climate, and biology require the acquisition and manipulation of large amounts of experimental and field data in order to create inputs for large-scale computational simulations. The results of these simulations must then be analyzed, leading to refinements of inputs and models and additional simulations. Further, these results must be managed and archived to provide justifications for regulatory decisions and publications that are based on these models. In this paper we introduce our Velo framework that is designed as a reusable, domain independent knowledge management infrastructure for modeling and simulation. Velo leverages, integrates, and extends open source collaborative and content management technologies to create a scalable and flexible core platform that can be tailored to specific scientific domains. We describe the architecture of Velo for managing and associating the various types of data that are used and created in modeling and simulation projects, as well as the framework for integrating domain-specific tools. To demonstrate a realization of Velo, we describe the Geologic Sequestration Software Suite (GS3) that has been developed to support geologic sequestration modeling. This provides a concrete example of the inherent extensibility and utility of our approach. Copyright 2011 ACM.||0||0|
|Wikipedia Usage Patterns: The Dynamics of Growth||Amitava Dutta
|International Conference on Information Systems (ICIS 2008)||2008||Wikis have attracted attention as a powerful technological platform on which to harness the potential benefits of collective knowledge. Current literature identifies different behavioral factors that modulate the interaction between contributors and wikis. Some inhibit growth while others enhance it. However, while these individual factors have been identified in the literature, their collective effects have not yet been identified. In this paper, we use the system dynamics methodology, and a survey of Wikipedia users, to propose a holistic model of the interaction among different factors and their collective impact on Wikipedia growth. The model is simulated to examine its ability to replicate observed growth patterns of Wikipedia metrics. Results indicate that the model is a reasonable starting point for understanding observed Wiki growth patterns. To the best of our knowledge, this is the first attempt in the literature to synthesize a holistic model of the forces underlying Wiki growth.||0||0|