Applications of stochastic programming under incomplete information

Preference Programming – Multicriteria Weighting Models under Incomplete Information

Useful decision recommendations can often be provided even if the model parameters are not exactly specified. The recognition of this fact has spurred the development of multicriteria methods which are capable of admitting and synthesizing incomplete preference information in hierarchical weighting models. These methods share similarities in that they (i) accommodate incomplete preference infor...

Bargaining under Incomplete Information

Stochastic 0-1 linear programming under limited distributional information

We consider the problem minx∈{0,1}n{cx : ajx ≤ bj , j = 1, . . . , m}, where the aj are random vectors with unknown distributions. The only information we are given regarding the random vectors aj are their moments, up to order k. We give a robust formulation, as a function of k, for the 0-1 integer linear program under this limited distributional information.

Skill Formation under Incomplete Information

We consider an economic model of child development with multiple stages. Due to incomplete information, parents are not able to tailor their investments to their child's type when the child is young. We show that incomplete information weakens the importance of early investments in children when inter-stage investments are easily substitutable, but strengthens them when substitution is difficul...

Hedging Demands under Incomplete Information

I present a model of consumption and portfolio choice under market incompleteness and imperfect information regarding the investment opportunity set. I solve analytically the consumption and portfolio choice problem for an investor learning about the true state of the economy. When prices are the only observations, the previously unspanned state variables are spanned by the market securities un...