身边的经济学·社会常识英语精读30篇（5）
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Algorithmic Governance and the Reconfiguration of Market Boundaries
算法治理与市场边界的重构
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Automated pricing engines, credit-scoring APIs, and real-time logistics optimizers now mediate transactions that once required human negotiation or institutional intermediation.
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These tools compress decision latency but also embed normative assumptions about risk, fairness, and efficiency into operational code.
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When platforms internalize externalities—like traffic congestion or carbon intensity—through dynamic fee structures, they effectively extend regulatory logic into private infrastructure.
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Yet algorithmic boundary-setting often lacks jurisdictional clarity: whose standards govern data usage across national cloud clusters or cross-border payment networks?
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Regulatory sandboxes attempt to reconcile innovation speed with systemic oversight, but their legitimacy depends on independent evaluation—not just industry self-reporting.
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The rise of 'embedded compliance'—where KYC and AML checks occur within checkout flows—shifts enforcement from post-hoc audits to pre-transaction gatekeeping.
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Such automation doesn’t eliminate discretion; it relocates it upstream—to model designers, data curators, and API governance councils.
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Market boundaries no longer align neatly with national borders or sectoral classifications when algorithms arbitrage regulatory asymmetries in real time.
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Transparency mandates for high-impact algorithms must distinguish between explainability for users and auditability for regulators—two distinct technical and legal requirements.
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Without interoperable governance protocols, algorithmic markets risk fracturing into incompatible technical sovereign zones rather than converging toward shared norms.