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This book develops a general theory of synthetic data as an emerging object of corporate and financial law. As artificially generated datasets increasingly inform credit modelling, stress testing, ESG disclosure, algorithmic governance, and supervisory experimentation, they are transforming the informational foundations of market life. In modern markets, knowledge is no longer simply observed; it is increasingly constructed.

It argues that, once informational premises are engineered rather than discovered, law can no longer be limited to regulating the use of data. It must also govern the conditions under which data are generated and rendered authoritative. Drawing together corporate governance, financial regulation, prudential supervision, data protection, and AI law, it develops a unifying framework-Bounded Simulation Governance-through which synthetic informational environments become legally intelligible.

At the core of the argument is the concept of simulation risk: a distinct form of systemic vulnerability arising when institutions rely on shared generative environments, with the result that hidden coordination, informational monoculture, and correlated fragility take shape across formally independent actors. Synthetic data thus emerge not merely as technical artefacts, but as legal infrastructures of judgment, discipline, and oversight.

Combining doctrinal analysis, institutional practice, and normative reconstruction, this book offers an original account of how law must adapt when artificial informational worlds become integral to corporate governance and financial regulation.