Skip to main content

Research Summary: Abstraction Techniques, and Their Value

  • Conference paper
  • First Online:
Abstraction, Reformulation, and Approximation (SARA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2371))

  • 803 Accesses

Abstract

A decision support system (DSS) is often shared by multiple decision-makers or analysts. Different users may vary in their viewpoints, and therefore, in particular, they may place different demands on a DSS model-base. Users may benefit if the system offers each a model that suits his/her special requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Askira Gelman, I., Model Reduction: Extracting Simple and Adequate Mathematical Macro-econometric Model-Based Information. MSc Thesis, Department of Information Systems, Tel Aviv University, Tel Aviv, Israel (1998).

    Google Scholar 

  2. Askira Gelman, I., Model Abstractions that Address Time-Scale Differences among Decision-Makers.” Accepted for publication by DSS Internet Age 2002 Conference, Cork, Ireland (2002).

    Google Scholar 

  3. Askira Gelman, I., A theory of the Economic Value of Information Systems Integration: An Information Economics Perspective. (2001)

    Google Scholar 

  4. Simon, H.A., On the Definition of the Causal Relation. Journal of Philosophy, 49 (1952) 517–528.

    Article  Google Scholar 

  5. Simon, H.A. Ando, A., Aggregation of variables in Dynamic Systems. Econometrica, Vol. 29(1961)111–138.

    Article  MATH  Google Scholar 

  6. Iwasaki, Y., Model Based Reasoning of Device behavior with Causal Ordering, PhD Thesis, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA (1988).

    Google Scholar 

  7. Iwasaki, Y., and Simon, H.A., Causality and Model Abstraction. Artificial Intelligence, No. 67 (1994) 143–194.

    Google Scholar 

  8. Marschak, J., Economics of Information Systems. Journal of American Statistical Association, Vol. 66, No. 333 (1971) 192–219.

    Article  MATH  MathSciNet  Google Scholar 

  9. McGuire, C.B., Comparisons of Information Structures. In: C.B., McGuire and R. Radner (eds.), Decision and Organization, University of Minnesota Press, 2nd edition (1986) 101–130.

    Google Scholar 

  10. Ahituv, N., Ronen, B., Orthogonal Information Structures-A Model to Evaluate the Information Provided by a Second Opinion. Decision Sciences, Vol. 19, No. 2 (1988) 255–268.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gelman, I.A. (2002). Research Summary: Abstraction Techniques, and Their Value. In: Koenig, S., Holte, R.C. (eds) Abstraction, Reformulation, and Approximation. SARA 2002. Lecture Notes in Computer Science(), vol 2371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45622-8_31

Download citation

  • DOI: https://doi.org/10.1007/3-540-45622-8_31

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43941-7

  • Online ISBN: 978-3-540-45622-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics