arXiv:2602.21620v2 Announce Type: replace-cross Abstract: We study the discrete Bertrand pricing game with a non-increasing demand function. The game has $n \ge 2$ players who simultaneously choose prices from the set $\{1/k, 2/k, \ldots, 1\}$, where $k\in\mathbb{N}$. The player who sets the lowest price captures the entire demand; if multiple players tie for the lowest price, they split the demand equally. We study the Bertrand paradox, where classical theory predicts low prices, yet real markets often sustain high prices. To understand this gap, we analyze a repeated-game model in which firm
The paper leverages recent advancements in 'no-regret learners' within artificial intelligence, applying them to long-standing economic paradoxes, indicating a cross-pollination of disciplines.
It provides a new computational and game-theoretic framework for understanding market dynamics, potentially bridging the gap between theoretical economic models and observed market behavior.
This research suggests a more robust way to model competitive pricing in markets, moving beyond classical equilibrium concepts by incorporating adaptive learning agents.
- · AI researchers
- · Game theorists
- · Economic modelers
- · Firms with advanced AI pricing strategies
- · Traditional economic models relying solely on Nash equilibrium
Further research into AI-driven economic modeling and agent-based simulations for market analysis.
Development of more sophisticated AI pricing algorithms by companies to achieve sustainable high margins.
Potential for AI-driven pricing strategies to lead to less competitive markets if not properly regulated, challenging anti-trust paradigms.
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Read at arXiv cs.LG