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Revisiting Stackelberg in His Own Light: Conjecture Learning in Leader-Follower Games

Denis Claude & Mabel Tidball

International Game Theory Review2026https://doi.org/10.1142/s0219198926500039article
AJG 1ABDC B
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0.50

Abstract

This paper revisits Heinrich F. von Stackelberg’s original description of leader-follower games under incomplete information, exploring how learning dynamics shape strategic interaction. The leader iteratively updates its conjecture about the follower’s reaction function before choosing an activity level that maximizes its payoff. The follower, in turn, responds optimally to each activity level, revealing information that the leader uses to refine its conjecture. Assuming linear conjectures, a smooth updating process á la Jean-Marie and Tidball [2006], and quadratic payoff functions, we establish conditions under which the learning process converges asymptotically to a self-confirming steady-state. We characterize the resulting activity levels and payoffs in two canonical environments: a sequential partnership game and a sequential duopoly game with quantity competition. We then compare the learning outcomes to both the (complete information) Stackelberg and the cartel solution. In the process, we find conditions under which the lack of information and the resulting strategic ambiguity lead to higher joint payoffs, and under which usual intuitions about the first-mover advantage need qualifications.

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https://doi.org/https://doi.org/10.1142/s0219198926500039

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@article{denis2026,
  title        = {{Revisiting Stackelberg in His Own Light: Conjecture Learning in Leader-Follower Games}},
  author       = {Denis Claude & Mabel Tidball},
  journal      = {International Game Theory Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219198926500039},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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