Decision tree analysis for a risk averse decision maker: CVaR Criterion

•We developed decision tree analysis for a risk averse decision maker under CVaR Criterion.•We proved there is an explicit non trivial linear representation of the problem.•We showed the geometric underpinnings of the model using disjunctive programming theory.

Risk aversion is a prevalent phenomenon when sufficiently large amounts are at risk. In this paper, we introduce a new prescriptive approach for coping with risk in sequential decision problems with discrete scenario space. We use Conditional Value-at-Risk (CVaR) risk measure as optimization criterion and prove that there is an explicit linear representation of the proposed model for the problem.