Skip to main content
SpringerLink
Log in

Interactive preference elicitation incorporating a priori and a posteriori methods

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In a typical decision-making process, preference elicitation methods require a priori knowledge about the desired outcomes. It is expected that the decision maker (DM) has a rough idea of the alternatives or the production possibilities, and has a relatively clear idea of his/her preferences regarding the decision. For those DMs who do not have this general understanding, the determination of preferences (and/or targets for specific criteria) can be difficult, uncertain and may lead towards a suboptimal solution for the particular DM. In a typical planning process, a limited set of alternatives are generated by a professional. These are then evaluated using preference information. A more useful approach would be to use that preference information towards developing a more acceptable alternative. The method proposed in this article is an interactive system which first requires the DM to indicate their initial preference value for each criterion. Then a series of pre-determined goal programming functions generate a solution. The solutions are then compared simultaneously, so that the DM can understand how feasible the preferences are when used in the creation of alternative solutions. The DM is then asked to provide a minimum preference level guided by a comparison of the alternative solutions. By proceeding in an iterative fashion, the DM can adjust his/her preferences until the DM is satisfied with one of the generated solutions. A real forest planning situation is described as a case study of this method.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ananda, J., & Herath, G. (2009). A critical review of multi-criteria decision making methods with special reference to forest management and planning. Ecological Economics, 68, 2535–2548.

    Article  Google Scholar 

  • Avesani, P., Perini, A., & Ricci, F. (2000). Interactive case-based planning for forest fire management. Applied Intelligence, 13(1), 41–57.

    Article  Google Scholar 

  • Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. C. (2008). How are preferences revealed? Journal of Public Economics, 92, 1787–1794.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Ferguson, R. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1, 138–151.

    Article  Google Scholar 

  • Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. New York: Wiley.

    Google Scholar 

  • Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2006). Saving for retirement on the path of least resistance. In E. McCaffrey & J. Slemrod (Eds.), Behavioral public finance: toward a new agenda (pp. 304–351). New York: Russell Sage Foundation.

    Google Scholar 

  • Dillman, D., Smyth, J., & Christian, L. (2009). Internet, mail and mixed-mode surveys: the tailored design method (3rd ed.). Hoboken: Wiley.

    Google Scholar 

  • Diaz-Balteiro, L., & Romero, C. (2008). Making forestry decisions with multiple criteria: a review and an assessment. Forest Ecology and Management, 255, 3222–3241.

    Article  Google Scholar 

  • Diaz-Balteiro, L., González-Pachón, J., & Romero, C. (2012). Goal programming in forest management: customizing models for the decision-maker’s preferences. Scandinavian Journal of Forest Research. doi:10.1080/02827581.2012.712154.

    Google Scholar 

  • Dyer, J. S. (1972). Interactive goal programming. Management Science, Theory Series, 19(1), 62–72.

    Article  Google Scholar 

  • Eyvindson, K., Kangas, A., Kurttila, M., & Hujala, T. (2010). Using preference information in developing alternative forest plans. Canadian Journal of Forest Research, 40, 2398–2410.

    Article  Google Scholar 

  • Field, D. B. (1973). Goal programming for forest management. Forest Science, 19, 125–135.

    Google Scholar 

  • Franz, L. S., & Lee, S. M. (1981). A goal programming based interactive support system. In J. N. Morse (Ed.), Lecture notes on economic mathematical systems: Vol. 190. Organizations: multiple agents with multiple criteria (pp. 110–115). Berlin: Springer.

    Chapter  Google Scholar 

  • Geoffrion, A. M., Dyer, J. S., & Feinberg, A. (1972). An interactive approach for multicriterion optimization with an application to the operation of an academic department. Management Science, 19(4), 357–368.

    Article  Google Scholar 

  • Hiltunen, V., Kurttila, M., Leskinen, P., Pasanen, K., & Pykäläinen, J. (2009). MESTA: an Internet-based decision-support application for participatory strategic-level natural resources planning. Forest Policy and Economics, 11(1), 1–9.

    Article  Google Scholar 

  • Hwang, C.-L., & Masud, A. S. M. (1979). Multi objective decision making—methods and applications. Berlin: Springer.

    Book  Google Scholar 

  • Kangas, J., & Pukkala, T. (1992). A decision theoretic approach applied to goal programming of forest management. Silva Fennica, 26, 169–179.

    Article  Google Scholar 

  • Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley.

    Google Scholar 

  • Kharrat, A., Chabchoub, H., & Aouni, B. (2010). Decision-maker’s preferences modelling within the interactive imprecise goal programming model. International Journal of Innovative Computing and Applications, 2(3), 150–169.

    Article  Google Scholar 

  • Klamroth, K., & Miettinen, K. (2008). Integrating approximation and interactive decision making in multicriteria optimization. Operations Research, 56(1), 222–234.

    Article  Google Scholar 

  • Korhonen, P., Moskowitz, H., & Wallenius, J. (1990). Choice behaviour in interactive multiple criteria decision making. Annals of Operations Research, 23, 161–179.

    Article  Google Scholar 

  • Kurttila, M., Heinonen, T., Hujala, T., Kangas, A., Nuutinen, T., Pukkala, T., Pykäläinen, J., Rasinmäki, J., & Tikkanen, J. (2013, in press). The use of forest decision support systems in Finland. In J. G. Borges, E.-M. Nordström, J. Garcia-Gonzalo, T. Hujala, & A. Trasobares (Eds.), Computer-based tools for supporting forest management. The experience and the expertise world-wide. Brussels: OPOCE.

  • Lappi, J. (2004). J-users’ guide. Version 0. 9.4. Finnish Forest Research Institute, Suonenjoki Research Station.

  • Mendoza, G. A., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. Forest Ecology and Management, 230(1–3), 1–22.

    Article  Google Scholar 

  • Metsätalouden kehittämiskeskus Tapio (2006). Hyvän metsänhoidon suositukset [Guidelines for good silviculture]. Metsätalouden kehittämiskeskus Tapion julkaisuja 22/2006 (in Finnish).

  • Miettinen, K. (1999). Nonlinear multiobjective optimization. Dordrecht: Kluwer.

    Google Scholar 

  • Pasanen, K., Kurttila, M., Pykäläinen, J., Kangas, J., & Leskinen, P. (2005). Mesta—nonindustrial private forest owners’ decision-support environment for the evaluation of alternative forest plans over the Internet. International Journal of Information Technology & Decision Making, 4(4), 601–620.

    Article  Google Scholar 

  • Pykäläinen, J., & Kurttila, M. (2009). Interactive method for supporting forest owners in biodiversity protection decisions. Small Scale Forestry, 8(3), 337–348.

    Article  Google Scholar 

  • Pykäläinen, J. (2000). Interactive use of multi-criteria decision analysis in forest planning. Doctoral thesis, University of Joensuu, Faculty of Forestry.

  • Rabin, M. (1998). Psychology and economics. Journal of Economic Literature, 36, 11–46.

    Google Scholar 

  • Redsven, V., Hirvelä, H., Härkönen, K., Salminen, O., & Siitonen, M. (2011). MELA2009 reference manual (2nd ed.). The Finnish Forest Research Institute. ISBN 978-951-40-2283-3.

    Google Scholar 

  • Romero, C. (2004). A general structure of achievement function for a goal programming model. European Journal of Operational Research, 153(3), 675–686.

    Article  Google Scholar 

  • Samuelson, P. A. (1937). A note on measurement of utility. Review of Economic Studies, 4(2), 155–161.

    Article  Google Scholar 

  • Sheppard, S. R. J., & Meitner, M. J. (2005). Using multi-criteria analysis and visualisation for sustainable forest management planning with stakeholder groups. Forest Ecology and Management, 207(1–2), 171–187.

    Article  Google Scholar 

  • Steuer, R. (1986). Multiple criteria optimization: theory, computation, and application. New York: Wiley.

    Google Scholar 

  • Tamiz, M., & Jones, D. (1997). Interactive frameworks for investigation of goal programming models: theory and practices. Journal of Multi-Criteria Decision Analysis, 6, 52–60.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 1124–1131.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453–458.

    Article  Google Scholar 

  • Vitoriano, B., & Romero, C. (1999). Extended interval goal programming. Journal of the Operational Research Society, 50(12), 1280–1283.

    Article  Google Scholar 

  • Zionts, S., & Wallenius, J. (1976). An interactive programming method for solving the multiple criteria problem. Management Science, 22(6), 652–663.

    Article  Google Scholar 

  • Zionts, S., & Wallenius, J. (1983). An interactive multiple objective linear programming method for a class of underlying nonlinear utility functions. Management Science, 29(5), 519–529.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank three anonymous reviewers and the Editors of this Special Edition of ANOR for their comments. This study was supported by the Academy of Finland (decision number 127681).

Author information

Authors and Affiliations

  1. Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, 00014, Helsinki, Finland

    Kyle Eyvindson & Annika Kangas

  2. Vantaa Unit, Finnish Forest Research Institute (Metla), P.O. Box 18, 01301, Vantaa, Finland

    Teppo Hujala

  3. Joensuu Unit, Finnish Forest Research Institute (Metla), P.O. Box 68, 80101, Joensuu, Finland

    Mikko Kurttila

Authors
  1. Kyle Eyvindson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teppo Hujala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mikko Kurttila
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Annika Kangas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyle Eyvindson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eyvindson, K., Hujala, T., Kurttila, M. et al. Interactive preference elicitation incorporating a priori and a posteriori methods. Ann Oper Res 232, 99–113 (2015). https://doi.org/10.1007/s10479-013-1316-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-013-1316-5

Keywords

Search

Navigation