Understanding algorithmic collusion with experience replay

With the digitalization of the economy and the advances in data analytics, firms are increasingly handing key manual decisions such as product pricing over to computers (Fisher et al., 2018; Miklós-Thal and Tucker, 2019; Hansen et al., 2020). However, the sophistication and powerfulness of algorithms have also led to another prominent concern on the possibility of collusion. Pricing algorithms may be too advanced to learn that it is optimal to collude (Ezrachi and Stucke, 2016). Although many are skeptical that autonomous collusion is only science fiction, recent experimental research (Waltman and Kaymak, 2008; Klein, 2019; Calvano et al., 2020; Hansen et al., 2020) suggests that dynamic pricing algorithms can learn collusive strategies from scratch, even without human guidance or communication with each other.