Following to Lead: Dominant Followership in Sequential Price Competition
Abstract
I study how algorithmic price responses reshape competition when firms differ in demand advantage and response reliability. Using synchronized high-frequency cross-platform pricing data, I find that Amazon, despite being the dominant platform, stands out as an effective follower. I investigate this strategy, which I call dominant followership, as a mechanism through which demand asymmetry can raise prices.
A sequential Bertrand model modified to break ties in favor of the dominant firm explains why this mechanism can be incentive-compatible: with a majority of demand at price parity, the dominant firm may prefer to match rather than undercut because matching lets it monetize its existing customer base. Anticipating this response, the rival can profitably initiate price increases that would otherwise be difficult to sustain. Prices rise above the simultaneous-move Nash equilibrium level only when the two asymmetries coincide: the demand-advantaged firm must also be the reliable responder.
When pricing is delegated to adaptive algorithms, the same demand and timing asymmetries shape not only current pricing incentives, but also what rivals learn from market feedback over time. In Q-learning simulations, price increases emerge through learning in the rival's algorithm, yet most of the surplus accrues to the dominant firm. The main insight of this paper is that algorithmic price competition among asymmetric firms should be modeled as a sequential response process: when demand asymmetry changes response incentives, individually incentive-compatible pricing decisions can soften competition without explicit coordination.
Can Consumer AI Discipline Algorithmic Pricing?
Work in progress
This project studies whether consumer-side AI shopping agents can weaken dominant-platform pricing power in markets where firms use pricing algorithms. Many consumers begin product search inside a dominant platform and compare only a limited set of alternatives, allowing firms to benefit from search frictions and default position. AI shopping agents can change this environment by searching across platforms, tracking recent price histories, and making price and non-price tradeoffs easier to compare. However, lower search costs need not eliminate platform advantage, because consumers may still value the dominant platform's non-price advantages, such as delivery speed, return convenience, and trust.
I model consumer-side AI as lowering search frictions and expanding consumers' consideration sets, which reduces the dominant firm's demand advantage. Starting from a market in which the dominant platform captures a large share of demand, I ask how pricing incentives change as AI agents make consumers more willing to consider rival sellers. The main exercise is a threshold analysis: how much must the dominant firm's demand advantage fall before the dominant-followership mechanism breaks down? I then examine the implications for consumer welfare. The project speaks to a policy question in algorithmic pricing: whether consumer-side AI can discipline dominant platforms through better search, or whether dominant-platform advantages are too persistent for the market to self-correct.