We consider optimal stopping problems in uncertain environments for an agent assessing utility by virtue of dynamic variational preferences or, equivalently, assessing risk by dynamic convex risk measures. The solution is achieved by generalizing the approach in terms of multiple priors...

The major challenge of the 21st century is to achieve food security under, roughly, a doubling in food demand by 2050 compared to present, and producing the additional food under marked shifts in climatic risks and with environmentally sound farming practices. Sustainable intensification of...

We analyse how progressive taxation and education subsidies affect schooling decisions when the returns to education are stochastic. We use the theory of real options to solve the problem of education choice in a dynamic, life-cycle consistent, stochastic model. We show that education attainment...

We analyse how progressive taxation and education subsidies affect schooling decisions when the returns to education are stochastic. We use the theory of real options to solve the problem of education choice in a dynamic stochastic model. We show that education attainment will be an increasing...

We develop a theory of optimal stopping problems under ambiguity in continuous time. Using results from (backward) stochastic calculus, we characterize the value function as the smallest (nonlinear) supermartingale dominating the payoff process. For Markovian models, we derive an adjusted...

We consider optimal stopping problems for ambiguity averse decision makers with multiple priors. In general, backward induction fails. If, however, the class of priors is time-consistent, we establish a generalization of the classical theory of optimal stopping. To this end, we develop first...