Kybernetika 42 no. 6, 629-646, 2006

Decision-making under uncertainty processed by lattice-valued possibilistic measures

Ivan Kramosil


The notion and theory of statistical decision functions are re-considered and modified to the case when the uncertainties in question are quantified and processed using lattice-valued possibilistic measures, so emphasizing rather the qualitative than the quantitative properties of the resulting possibilistic decision functions. Possibilistic variants of both the minimax (the worst-case) and the Bayesian optimization principles are introduced and analyzed.