scholarly journals Computational Experimentation

2019 ◽  
Author(s):  
Tabrez Ebrahim
Keyword(s):  
Computational Experimentation

Knowledge Management Research through Computational Experimentation

2010 ◽  
pp. 728-737
Author(s):  
Mark E. Nissen ◽  
Raymond E. Levitt
Keyword(s):  
Knowledge Management ◽  
Modern Science ◽  
Internal Validity ◽  
Computational Experiments ◽  
Research Approach ◽  
Field Methods ◽  
Complementary Method ◽  
Computational Experimentation ◽  
New Knowledge ◽  
Computational Modeling And Simulation

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management. This article combines and builds upon recent results to describe a research approach that bridges the chasm between laboratory and field methods in KM: computational experimentation. As implied by the name, computational experiments are conducted via computer simulation. But such experiments can go beyond most simulations (e.g., incorporating experimental controls, benefiting from external model validation). And they can offer simultaneously benefits of laboratory methods (e.g., internal validity, lack of confounding) and fieldwork (e.g., external validity, generalizability). Further, computational experiments can be conducted at a fraction of the cost and time associated with either laboratory experiments or field studies. And they provide a window to view the kinds of meta-knowledge that are important for understanding knowledge management. Thus, computational experimentation offers potential to mitigate many limitations of both laboratory and field methods and to enhance KM research. We discuss computational modeling and simulation as a complementary method to bridge the chasm between laboratory and field methods—not as a replacement for either of these methods.

A Musical Suite Composed by an Electronic Brain: Reexamining the Illiac Suite and the Legacy of Lejaren A. Hiller Jr.

2018 ◽  
Vol 28 ◽  
pp. 19-24 ◽  
Author(s):  
Tiffany Funk
Keyword(s):  
Visual Arts ◽  
Performance Art ◽  
Electronic Music ◽  
Digital Art ◽  
John Cage ◽  
Computational Experimentation ◽  
Art Practices ◽  
Conceptual Performance ◽  
The 1950S

In 1956, Lejaren A. Hiller, Jr., and Leonard Isaacson debuted the Illiac Suite, the first score composed with a computer. Its reception anticipated Hiller’s embattled career as an experimental composer. Though the Suite is an influential work of modern electronic music, Hiller’s accomplishment in computational experimentation is above all an impressive feat of postwar conceptual performance art. A reexamination of theoretical and methodological processes resulting in the Illiac Suite reveals a conceptual and performative emphasis reflecting larger trends in the experimental visual arts of the 1950s and 1960s, illuminating his eventual collaborations with John Cage and establishing his legacy in digital art practices.

Cooperating Services for Data-Driven Computational Experimentation

2005 ◽  
Vol 7 (5) ◽  
pp. 34-43 ◽  
Author(s):  
B. Plale ◽  
D. Gannon ◽  
Yi Huang ◽  
G. Kandaswamy ◽  
S. Lee Pallickara ◽  
...  
Keyword(s):  
Data Driven ◽  
Computational Experimentation

Computational Experimentation

2011 ◽  
pp. 412-420
Author(s):  
Mark E. Nissen ◽  
Raymond E. Levitt
Keyword(s):  
Knowledge Management ◽  
Social Science ◽  
Modern Science ◽  
Field Research ◽  
Knowledge Development ◽  
Trial And Error ◽  
Computational Experimentation ◽  
New Knowledge ◽  
Systematic Development

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management.

A PERCENTILE SEARCH HEURISTIC FOR GENERALIZED ASSIGNMENT PROBLEMS WITH A VERY LARGE NUMBER OF JOBS

2005 ◽  
Vol 22 (02) ◽  
pp. 171-188
Author(s):  
A. J. HIGGINS
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Fine Tuning ◽  
Threshold Accepting ◽  
Assignment Problems ◽  
Search Heuristic ◽  
Computational Experimentation ◽  
Generalized Assignment ◽  
Problem Instances ◽  
Probabilistic Acceptance

This article presents a new heuristic for generalized assignment problems with a very large number of jobs. The heuristic applies a probabilistic acceptance of a move, based on a percentile threshold, using information from recent moves. This percentile search heuristic (PSH) is compared to tabu search, simulated annealing, and threshold accepting using a rigorous computational experimentation with randomly generated problem instances of up to 50,000 jobs and 40 agents. The PSH did find the best solution among the heuristics for 45% of the instances, particularly larger size problems, versus 30% for tabu search, but required more fine-tuning of the heuristic parameters.

Computational Experimentation

2011 ◽  
pp. 51-57 ◽  
Author(s):  
Mark E. Nissen ◽  
Raymond E. Levitt
Keyword(s):  
Knowledge Management ◽  
Social Science ◽  
Modern Science ◽  
Field Research ◽  
Knowledge Development ◽  
Trial And Error ◽  
Computational Experimentation ◽  
New Knowledge ◽  
Systematic Development

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management.

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