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“In theory there is no difference between theory and practice. "Now I am no believer in the worth of any mere taste for art |
My research is a blend of thinking and doing, of the empirical and the constructive. It is in four principal areas. For almost two decades I have been working on a basic problem in business forecasting--no single forecasting method performs well across all kinds of data and situations. That has produced an extensive program of research related to forecasting that has included work on expert systems development, assessment methodologies, statistical pattern recognition, and applications of neural networks. More recently, I have been involved in an effort with Dick Boland to conceptualize Managing as Designing, an approach to management thinking that compliments the decision-oriented approach in general use. My interest in design extends to the design of visualization instruments, including MIDI-based instruments that allow painters and artists to play with images in the way that musicians play sounds. Finally, I have done work related to how people use information in setting objectives and on how they perceive time when using information systems. Following are some of my most important findings and accomplishments (the links on this page download full-text pdf versions of the papers).
In Forecasting
Developed “Rule-Based Forecasting,” an expert systems approach to improve the selection and combination of extrapolation forecasts.
Fred Collopy and J. Scott Armstrong (1992), “Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations,” Management Science, 38 (10), 1394-1414.
Adya, M., J. Armstrong, F. Collopy, & M. Kennedy (2000), “An Application of Rule-based Forecasting to a Situation Lacking Domain Knowledge,” International Journal of Forecasting, 16, 477-484.
Fred Collopy and J. Scott Armstrong (1989), “Toward Computer-Aided Forecasting Systems: Gathering, Coding, and Validating the Knowledge,” in George R. Widmeyer (ed.), DSS-899 Transactions: Ninth International Conference on Decision Support Systems, Institute of Management Science, pp. 103-119.
Armstrong, J. S., M. Adya, and F. Collopy, “Rule-Based Forecasting: Using Expert and Domain Knowledge in the Extrapolation of Time Series”, Principles of Forecasting: A Handbook for Researchers and Practitioners, J. Scott Armstrong (ed.): Norwell, MA: Kluwer Academic Publishers, (2001), 259-282.
Found that machine learning techniques could improve on the estimates of experts for coefficients used in expert systems for forecasting.
Monica Adya, Fred Collopy, J. Scott Armstrong, and Miles Kennedy (2001), “Automatic Identification of Time Series Features for Rule-Based Forecasting,” International Journal of Forecasting, 17, 143-157.
Reviewed the approaches proposed for integrating statistical and judgmental forecasts and the empirical support for each.
J. Scott Armstrong and Fred Collopy (1998), “Integration of Statistical Methods and Judgment for Time Series Forecasting: Principles from Empirical Research,” in G. Wright and P. Goodwin (eds.), Forecasting with Judgment. John Wiley & Sons Ltd., 269-293.
Found that of 48 studies that examined the use artificial neural networks to produce forecasts, only 22 were effectively validated and implemented. Of those, 18 supported the potential of neural nets for forecasting and prediction.
Monica Adya and Fred Collopy (1998), “How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation,” Journal of Forecasting, 17, 481-495.
Proposed and tested the use of causal forces for the selection and weighing of extrapolation methods.
J. Scott Armstrong and Fred Collopy (1993), “Causal Forces: Structuring Knowledge for Time-series Extrapolation,” Journal of Forecasting, 12, 103-115.
Found that the most widely used measures for assessing accuracy in forecasting studies are unreliable and unstable and proposed and evaluated the Relative Absolute Error (RAE) as an alternative.
J. Scott Armstrong and Fred Collopy (1992), “Error Measures for Generalizing about Forecasting Methods: Empirical Comparisons,” International Journal of Forecasting, 8, 69-80.
Found that prediction intervals are often asymmetric and proposed a method for modifying them.
J. Scott Armstrong and Fred Collopy (2001), “Identification of Asymmetric Prediction Intervals through Causal Forces,” Journal of Forecasting, 20, 273-283.
Determined that decomposition improves forecasts under a set of well-specified conditions and is risky in the absence of those conditions.
J. S. Armstrong, Fred Collopy, and J. Thomas Yokum, “Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series,” forthcoming in International Journal of Forecasting.
Determined that the use of diffusion models to forecast information systems spending has produced errors that are greatly in excess of those resulting from the application of simple extrapolation methods.
Collopy, F., M. Adya, and J. S. Armstrong, “Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts,” Information Systems Research, 5 (1994), 170-179.
In Instrument Design
Designed and programmed Imager, a performance instrument that provides for real-time control of color, form, and motion of abstract images.
Fred Collopy, “Color, form, and motion: Dimensions of a musical art of light,” Leonardo, Vol. 33, No. 5, 2000, 355-360.
Fred Collopy and Robert M. Fuhrer, “A visual programming language for expressing visual rhythms,” Journal of Visual Programming Languages, 12, 2001, 283-297.
Collopy, F., R. M. Fuhrer, and D. Jameson, “Visual Music in a Visual Programming Language,” IEEE Symposium on Visual Languages, (1999), 111-118.
Fred Collopy, “Improvisational Lumia: Playing Along with Musicians,” Leonardo, Vol. 34, No. 4, 2001, 353.
Related to Information and Information Systems Use
Found that competitor-oriented objectives and some uses of competitor-oriented information are likely to be detrimental to profits.
J. Scott Armstrong and Fred Collopy (1996), “Competitor Orientation: Effects of Objectives and Information on Managerial Decisions and Profitability,” Journal of Marketing Research, 33, 188-199.
J. Scott Armstrong and Fred Collopy (1994), “The Profitability of Winning,” Chief Executive, June, 1994, 61-63.
Identified systematic biases (regression to mean) in self-assessments of time by computer users.
Collopy, F., “Bias in Retrospective Self-Reports of Time Use: An Empirical Study of Computer Users,” Management Science, 42 (1996), 758-767.