Algorithms, Artificial Intelligence and Simple Rule Based Pricing

Automated pricing comes in two forms - rule-based (e.g., targeting or undercutting the lowest price, etc) and artificial intelligence (AI) powered algorithms (e.g., reinforcement learning (RL) based). While rule-based pricing is the most widely used automated pricing strategy today, many retailers have increasingly adopting pricing algorithms powered by AI. Q-learning algorithm (a specific type of RL algorithm) is particularly appealing for pricing because it autonomously learns an optimal pricing policy and can adapt to any evolution in competitors' pricing strategy and market environment. It is commonly believed that the Q-learning algorithm has a significant advantage over simple rule-based pricing algorithms; therefore, in a competitive environment, most firms should adopt Q-learning based pricing algorithms if their competitors are using such algorithms. However, through extensive pricing experiments in a workhorse oligopoly model of repeated price competition, we show that a firm's best response to its competitor's Q-learning based algorithms is to use simple rule-based pricing algorithms. We find that when a Q-learning algorithm competes against a rule-based pricing algorithm, higher prices are sustained in the market in comparison to when multiple Q-learning algorithms compete against each other. The high prices are sustained because the rule-based algorithm introduces stationarity into the repeated price competition, which allows the Q-learning algorithm to more effectively search for the optimal policy benefiting both sellers. Further, the experimental phase where the Q-learning algorithm learns the optimal pricing policy is significantly shorter when it competes against a rule-based pricing algorithm in comparison to when it competes against another Q-learning algorithm. Our results are robust to alternative modeling assumptions on market structure, algorithm type, number of players, etc