身边的经济学·社会常识英语精读30篇（6）
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Batch-0008-006: When Algorithmic Price Optimization Crosses into Tacit Collusion
批次0008-006：算法定价优化何时滑向默契合谋
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Firms increasingly deploy AI to adjust prices in real time based on demand, inventory, and competitor signals.
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Regulators now question whether such systems—without explicit coordination—can still produce collusive outcomes.
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Empirical studies show pricing algorithms sometimes converge on similar markups even across rival platforms.
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This emergent coordination lacks intent but mimics the economic effects of traditional cartels.
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Antitrust frameworks struggle to assign liability when no human agreement or communication exists.
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Courts must distinguish between parallel conduct arising from rational self-interest and algorithmically reinforced interdependence.
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Transparency requirements for pricing logic are emerging in EU digital markets legislation.
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Yet enforcement remains hampered by proprietary black-box models and fragmented jurisdictional authority.
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The challenge lies not in banning automation but in designing accountability into its feedback loops.
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Public policy must evolve faster than optimization speed to preserve competitive market discipline.
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Such cases reveal how technical efficiency can unintentionally erode price competition’s foundational role.
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Ultimately, market integrity depends less on human restraint than on institutional guardrails for machine behavior.