Papers of the Regional Science Association

, Volume 58, Issue 1, pp 7–20 | Cite as

Alternative theoretical frameworks for the interpretation of random utility models

  • E. Fabio Arcangeli
  • Giorgio Leonardi
  • Aura Reggiani
Recent Methodological Developments in Spatial Analysis


Orthodox demand theory has received greater flexibility and adaptability to different choice situations from the broad family of Additive Random Utility Models. But this development leaves the major open issues in demand analysis still unresolved. In this contribution special attention is given to alternative theoretical frameworks for the interpretation of Random Utility Models. The analysis conducted opens the way for further developments and, in conclusion, a first outline of a stochastic model, where choice probabilities can be interpreted as function of some “observed attractiveness” in the theoretical framework of bounded rationality, is presented.


Stochastic Model Open Issue Great Flexibility Utility Model Choice Probability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bain, A. D. 1964.The growth of television ownership in the U.K. since the war. Cambridge: Cambridge University Press.Google Scholar
  2. Becker, G. 1962. Irrational behaviour and economic theory.Journal of Political Economy 70: 1–13.Google Scholar
  3. Caldwell, B. 1982.Beyond positivism. Economic methodology in the twentieth century. London: Allen & Unwin.Google Scholar
  4. Chow, G. C. 1984.Econometrics. New York: McGraw-Hill.Google Scholar
  5. Cramer, J. S. 1969.Empirical econometrics. Amsterdam: North-Holland.Google Scholar
  6. Daganzo, C. F. 1979.Multinomial probit. The theory and its applications to demand forecasting. New York: Academic Press.Google Scholar
  7. Davies, S. 1979.The diffusion of process innovations. Cambridge: Cambridge University Press.Google Scholar
  8. Domencich, T. A. and McFadden, D. 1975.Urban travel demand. A behavioural analysis. Amsterdam: North-Holland.Google Scholar
  9. Golledge, R. G. 1970. Some equilibrium models of consumer behaviour.Economic Geography 46: 417–424.Google Scholar
  10. Kahneman, D., Slovik, P., and Tversky, A. 1982.Judgment under uncertainty: heuristics and biases. Cambridge: Cambridge University Press.Google Scholar
  11. Katona, G. 1975.Psychological economics. New York: Elsevier.Google Scholar
  12. Katona, G. and Mueller, E. 1954. A study of purchase decisions. InConsumer Behaviour, ed. L. Clark. New York University Press.Google Scholar
  13. Leonardi, G. 1984. Accessibility and agglomeration in the spatial distribution of populations. Unpublished paper.Google Scholar
  14. Leonardi, G. 1985. Equivalenza asintotica tra la teoria delle utilità casuali e la massimizzazione dell'entropia. InTerritorio e trasporti. Modelli matematici per l'analisi e la pianificazione, ed. A. Reggiani. Milano: F. Angeli.Google Scholar
  15. Leonardi, G., Arcangeli, E. F., and Reggiani, A. 1984. Aggregate revealed preferences and random utility theory. Paper delivered at the Second International Conference on Foundations of Utility and Risk Theory, Venice (revised version).Google Scholar
  16. Luce, R. D. 1959.Individual choice behaviour: a theoretical analysis. New York: Wiley.Google Scholar
  17. Luhmann, N. 1979.Potere e complessità sociale. Milano: II Saggiatore. (original edition: 1975.Macht. Stuttgart: F. E. Verlag).Google Scholar
  18. Machlup, F. 1978.Methodology of economics and other social sciences. New York: Academic Press.Google Scholar
  19. McFadden, D. 1974. Conditional logit analysis of quatitative choice behaviour. InFrontiers in econometrics, ed. P. Zarembka, pp. 105–142. New York: Academic Press.Google Scholar
  20. McFadden, D. 1981. Econometric models of probabilistic choice. In:Structural Analysis of Discrete Data with Econometric Applications, ed. C. F. Manski, and D. McFadden, pp. 198–272. Cambridge Mass: MIT Press.Google Scholar
  21. Nelson, R. N. and Winter, S. G. 1982.An evolutionary theory of economic change. Cambridge Mass.: The Belknap Press of Harvard University Press.Google Scholar
  22. Newman, J. W. and Staelin, R. 1972. Prepurchase information seeking for new cars and major household applicances.Journal of Marketing Resarch 9: 249–257.Google Scholar
  23. Olsson, G. 1980.Birds in eggs/eggs in birds. London: Pion.Google Scholar
  24. Pirie, G. H. 1976. Thoughts on revealed preference and spatial behaviour.Environment and Planning A 8: 947–955.Google Scholar
  25. Sen, A. 1984. Rationality and uncertainty. Paper delivered at the Second International Conference on Foundations of Utility and Risk Theory, Venice.Google Scholar
  26. Simon, H. 1981.The sciences of the artificial. Cambridge, Mass.: Harvard University Press.Google Scholar
  27. Simon, H. 1983.Reason in human affairs. Oxford: Basil Blackwell.Google Scholar
  28. Staddon, J. E. R. 1983.Adaptive behaviour and learning. Cambridge: Cambridge University Press.Google Scholar
  29. Tversky, A. and Kahneman, D. 1974. Judgement under uncertainty: heuristic and biases.Science 185: 1124–1131.Google Scholar
  30. Winter, S. 1964. Economic “natural selection” and the theory of the firm.Yale Economic Essays 4: 224–272.Google Scholar
  31. Wrigley, N. 1983. Quantative methods: developments in discrete choice modelling.Progress in Human Geography 6: 547–562.Google Scholar

Copyright information

© The Regional Science Association 1985

Authors and Affiliations

  • E. Fabio Arcangeli
    • 1
  • Giorgio Leonardi
    • 2
  • Aura Reggiani
    • 1
  1. 1.Department of Urban Economic and Social AnalysisUniversity of VeniceVeniceItaly
  2. 2.International Institute for Applied Systems AnalysisLaxenburgAustria

Personalised recommendations