• Annika Kangas
  • Mikko Kurttila
  • Teppo Hujala
  • Kyle Eyvindson
  • Jyrki Kangas
Part of the Managing Forest Ecosystems book series (MAFE, volume 30)


In this chapter, decision, decision support, decision-making and planning are defined. We describe what planning means, why planning is needed and what are the aims of planning as a process. We describe the phases decision situations typically involve. We describe the different views for studying decision-making, i.e. the descriptive view, which studies decisions as people make them, and normative view, which studies the ways that may help in making better decisions. We present the different dimensions of decision situations (under certainty/under uncertainty, single goal/multiple goals, discrete/continuous, single decision-maker/multiple decision-makers or stakeholders). Finally, we briefly present classes of methods potentially useful for decision support for these situations such as mathematical optimisation, heuristics, multi-criteria decision-making and group decision-making.


Rational choice Alternatives Information Preferences Dimensions of decision problems Classes of decision support methods 


  1. Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: An integrated approach. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
  2. Biel, A., & Thøgersen, J. (2006). Activation of social norms in social dilemmas: A review of the evidence and reflections on the implications for environmental behaviour. Journal of Economic Psychology, 28(2007), 93–112.Google Scholar
  3. Birge, J. R., & Louveaux, F. (1997). Introduction to stochastic programming. New York: Springer. 421 p.Google Scholar
  4. Bouyssou, D., Jacquet-Lagrez, E., Perny, P., Slowinski, R., Vanderpooten, D., & Vincke, P. (Eds.). (2001). Aiding decisions with multiple criteria. Essays in honour of Bernard Roy. Dordrecht: Kluwer Academic Publishers. 274 p.Google Scholar
  5. Bradshaw, J. M., & Boose, J. H. (1990). Decision analysis techniques for knowledge acquisition: Combining information and preferences using Aquinas and Axotl. International Journal of Man-Machine Studies, 32, 121–186.CrossRefGoogle Scholar
  6. Dantzig, G. B. (1963). Linear programming and extensions. Princeton: Princeton University Press. 630 p.Google Scholar
  7. Dykstra, D. P. (1984). Mathematical programming for natural resource management. New York: McGraw-Hill Book Company. 318 p.Google Scholar
  8. Eriksson, L. O., & Borges, J. G. (2014). Computerized decision support tools to address forest management planning problems: History and approach for assessing the state of art world-wide 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 (pp. 3–15). Umeå: Swedish University of Agricultural Sciences. Arbetsrapport/Sveriges lantbruksuniversitet, Institutionen för skoglig resurshushållning och geomatik 1.Google Scholar
  9. Etzioni, A. (1986). The case for a multiple-utility conception. Economics and Philosophy, 2, 159–183.CrossRefGoogle Scholar
  10. French, S. (1989). Readings in decision analysis. London: Chapman and Hall. 210 p.CrossRefGoogle Scholar
  11. Glover, F. (1989). Tabu search – Part I. ORSA Journal of Computing, 1, 190–206.CrossRefGoogle Scholar
  12. Glover, F., Kelly, P., & Laguna, M. (1995). Genetic algorithms and tabu search: Hybrids for optimization. Computers in Operations Research, 22, 111–134.CrossRefGoogle Scholar
  13. Hillier, F. S., & Lieberman, G. J. (2001). Introduction to operations research (7th ed.). New York: McGraw Hill. 1214 p.Google Scholar
  14. Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis. Methods and software. Chichester: Wiley. 296 p.CrossRefGoogle Scholar
  15. Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux. 499 p.Google Scholar
  16. Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. The Journal of Economic Perspectives, 5(1), 193–206.CrossRefGoogle Scholar
  17. Kangas, J. (1992). Metsikön uudistamisketjun valinta – monitavoitteiseen hyöty teoriaan perus tuva päätösanalyysimalli. Summary: Choosing the regeneration chain in a forest stand, A decision-model based on multi-attribute utility theory. Joensuun yli opiston luonnontieteelli siä julkaisuja 24. 230 p.Google Scholar
  18. Keeney, R. L. (1982). Decision analysis: An overview. Operations Research, 30, 803–838.CrossRefPubMedGoogle Scholar
  19. Keeney, R. L. (1992). Value-focused thinking. A path to creative decision making. Cambridge, MA: Harvard University Press. 416 p.Google Scholar
  20. Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. New York: Wiley, 569 p.Google Scholar
  21. Kilgour, D. M., & Eden, C. (Eds.). (2010). Handbook of group decision and negotiation (Advances in group decision and negotiation, Vol. 4). Dordrecht: Springer. 477 p.Google Scholar
  22. King, A. J., & Wallace, S. W. (2012). Modelling with stochastic programming. New York: Springer. 173 p.CrossRefGoogle Scholar
  23. Kouchaki, M., Smith-Crowe, K., Brief, A. P., & Sousa, C. (2013). Seeing green: Mere exposure to money triggers a business decision frame and unethical outcomes. Organizational Behavior and Human Decision Processes, 121, 53–61.CrossRefGoogle Scholar
  24. Menzel, S. (2013). Are emotions to blame? — The impact of non-analytical information processing on decision-making and implications for fostering sustainability. Ecological Economics, 96, 71–78.CrossRefGoogle Scholar
  25. Miettinen, K. (1999). Nonlinear multiobjective optimization. Boston: Kluwer Academic Publishers.Google Scholar
  26. Mingers, J., & Brocklesby, J. (1997). Multimethodology: Towards a framework for mixing methodologies. Omega, International Journal of Management Science, 5, 489–509.CrossRefGoogle Scholar
  27. Ramsey, F. P. (1930). The foundations of maths and other logical essays. New York: Humanities Press. 280 p.Google Scholar
  28. Reeves, C. R. (Ed.). (1993). Modern heuristic techniques for combinatorial problems. Oxford: Blackwell Scientific Publications. 320 p.Google Scholar
  29. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  30. Simon, H. A. (1957). Models of man: Social and national. New York: Wiley. 247 p.Google Scholar
  31. Smith, J. Q. (2010). Bayesian decision analysis. Principles and practise (p. 338). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  32. Steuer, R. E. (1986). Multiple criteria optimization: Theory, computation, and application. New York: Wiley. 532 p.Google Scholar
  33. Taha, H. A. (1987). Operation research. New York: Macmillan Publishing Company. 876 p.Google Scholar
  34. Vincke, P. (1992). Multi-Criteria decision aid. New York: Wiley.Google Scholar
  35. von Neumann, J., & Morgestern, O. (1947). Theory of games and economic behaviour. New York: Wiley. 641 p.Google Scholar
  36. von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Annika Kangas
    • 1
  • Mikko Kurttila
    • 2
  • Teppo Hujala
    • 3
  • Kyle Eyvindson
    • 4
  • Jyrki Kangas
    • 5
  1. 1.Economics and SocietyNatural Resources Institute Finland (Luke)JoensuuFinland
  2. 2.Bio-based Business and IndustryNatural Resources Institute Finland (Luke)JoensuuFinland
  3. 3.Bio-based Business and IndustryNatural Resources Institute Finland (Luke)HelsinkiFinland
  4. 4.Department of Forest SciencesUniversity of HelsinkiHelsinkiFinland
  5. 5.School of Forest SciencesUniversity of Eastern FinlandJoensuuFinland

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