Optimising Production Under Uncertainty

Part of the SpringerBriefs in Economics book series (BRIEFSECONOMICS)


This is the main chapter in which the concepts and tools developed in the previous chapters are used to derive criteria for optimal use and combination of inputs when producing under uncertainty. The chapter includes a formal definition of ‘good’ and ‘bad’ states of nature, and it derives criteria for optimal application of various types of input for risk-neutral and risk-averse decision makers. Although it is not possible to derive operational criteria for risk averse decision makers unless one knows the utility function, the analysis using the state-contingent approach provides interesting and operational results for risk neutral decision makers. It also reveals that even if one knows the form of the utility function, then it is not possible to determine uniquely whether risk-averse decision makers will use more or less input than risk-neutral decision makers.


Good state bad state risk neutral state-contingent income state-general input production technology variable input fixed input output-cubical technology state-contingent output set income curve cost function constraints risk averse 


  1. Chambers, R., & Quiggin, J. (2000). Uncertainty, Production, Choices, and Agency. The State-Contingent Approach. Cambridge: Cambridge University Press.Google Scholar
  2. Rasmussen, S. (2003). Criteria for Optimal Production under Uncertainty. The State-Contingent Approach. Aust. J. Agric. Res. Econ. p. 447–476.Google Scholar
  3. Rasmussen, S. (2006). Optimizing Production under Uncertainty. Generalization of the State-Contingent Approach and Comparison with the EV Model. FOI Working Papers no. 5/2006, Institute of Food and Resource Economics, The Royal Veterinary and Agricultural University, Copenhagen.Google Scholar
  4. Rasmussen, S. (2011). Production Economics. The Basic Theory of Production Optimisation. Berlin: Springer.Google Scholar

Copyright information

© Svend Rasmussen 2011

Authors and Affiliations

  1. 1.Institute of Food and Resource EconomicsUniversity of CopenhagenFrederiksberg C, CopenhagenDenmark

Personalised recommendations