Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets

The impact of artificial intelligence design on pricing

“…Aparicio et al (2021) provide related evidence in the context of supermarkets. Similarly, Normann and Sternberg (2021) provide related experimental evidence.…”

“…Aparicio et al (2021) provide related evidence in the context of supermarkets. Similarly,Normann and Sternberg (2021) provide related experimental evidence.15 In the Bertrand game case, this would converge (under some standard conditions) to the Nash equilibrium of the game.16 Another point of distinction in that, in our setting, players do not "learn" about the probability distribution of strategies chosen by other players. Rather, what they have in memory is only the value associated with every action.17 Examples of contributions directed at this policy debate includeMehra (2015),Ezrachi and Stucke (2017),Kühn and Tadelis (2017),Schwalbe (2018), de Corniere and Taylor (2020),Assad et al (2021), andVeljanovski (2022) Goldfarb et al (2019).…”

They Are Among Us: Pricing Behavior of Algorithms in the Field

“…Contributions in this field investigate algorithmic behavior in settings of simultaneous price-or quantity-competition except Klein (2021) who studies sequential actions. Examined algorithms are either of the family of learning algorithms (Waltman and Kaymak, 2008;Salcedo, 2015;Calvano et al, 2020;Klein, 2021;Werner, 2022;Schauer and Schnurr, 2022) , prediction algorithms (Miklós-Thal and Tucker, 2019;O'Connor and Wilson, 2021) or include a tit-for-tat rule (Normann and Sternberg, 2022). 2 Common to all is that they study variations of human and algorithmic interaction and their ability to tacitly collude in simulation or experimental settings.…”

“…The recent laboratory experiments of Normann and Sternberg (2022), Werner (2022) and Schauer and Schnurr (2022) all study algorithms' effect on prices in varying combinations of human and algorithmic interaction. Except for Normann and Sternberg (2022) 2 A tit-for-tat pricing rule is an algorithm in nature that takes a specified set of inputs (rival's and own price) and produces an output (next period's own price) according to a predefined process. This process is retaliating if the rival's price is lower and cooperating if it is equal or higher.…”

“…While studies based on computer simulations (Waltman and Kaymak, 2008;Calvano et al, 2020) and laboratory experiments (Normann and Sternberg, 2022;Schauer and Schnurr, 2022) provided a first evidence base in recent years, empirical approaches in this field are scarce with the notable exceptions being Assad et al (2020); Wieting and Sapi (2021) and Brown and MacKay (2021).…”