We consider dynamic pricing and demand learning in a duopoly, both from the perspective where the firms compete against each other and from the perspective where the firms aim to collude to increase their revenues. We adopt the widely studied multinomial logit demand model and construct a...

We consider dynamic assortment optimization with incomplete information under the uncapacitated multinomial logit choice model. We propose an anytime stochastic approximation policy and prove that the regret - the cumulative expected revenue loss caused by offering suboptimal assortments - after...