We examine recent claims that a particular Q-learning algorithm used by competitors 'autonomously' and systematically learns to collude, resulting in supracompetitive prices and extra profits for the firms sustained by collusive equilibria. A detailed analysis of the inner workings of this...

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...

This paper contributes to the ongoing debate on the plausibility of tacit collusion between sellers in algorithmic marketplaces, which can be detrimental to customers and social welfare. We study a broad class of assortment decisions routinely made by sellers on online platforms, including which...

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...

We consider optimal dynamic and static pricing of a single item during a finite timeperiod, and study the distribution of the time it takes to sell the item. We derive anexpression for this distribution in terms of the optimal demand rates and show that itconverges to a limit, as the time...