[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85986-en":3,"doc-seo-85986-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85986,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Sequential Compliance Decisions of Firms on Cross-Border Data Flows: An Institutionally Anchored Decision Support System","Data’s economic value depends on cross-organization and cross-border flows, but tightening governance rules make transfers a sequential, institutionally constrained decision for firms. The work develops an institutionally anchored decision support system that converts regulatory requirements into a computable minimal compliance mapping. Weekly firm choices are modeled as a finite-horizon Markov decision process with compliance enforced as a hard constraint. Masked deep reinforcement learning produces interpretable, auditable policies, highlighting localization boundary shifts, front-loaded credential acquisition, and an absorb-then-adjust response to increasing regulatory strictness.","arXiv :2607 . 10620v1 [ cs .CY] 12 Jul 2026  \nSequential Compliance Decisions of Firms on Cross-Border Data Flows: An Institutionally Anchored  \nDecision Support System  \n*  \nYuepeng Zhou, Dongchi Xing, Li Xiong  \nJuly 2026  \nThe economic value of data arises from its flow across organizations and national borders. Yet increasingly stringent data governance regimes are turning cross-border transfer into an institutionally constrained sequential decision, in which firms repeatedly weigh compliance costs against the value of data flows. From the perspective of a data-exporting firm, this paper develops an institutionally anchored decision support system. It converts regulatory rules into a computable minimal compliance mapping and models the firm’s weekly decisions as a finite-horizon Markov decision process (MDP), with compliance represented as a hard constraint rather than a penalty term. The resulting problem is solved using masked deep reinforcement learning, while counterfactual path advantages provide interpretable signals to support the firm’s cross-border data flow decisions. Experiments show that the policies learned within the system outperform the baselines considered and deliver interpretable, auditable decision support. Local processing concentrates in states where the business value of small lawful transfers does not cover their compliance costs, and the localization boundary shifts systematically as the regime tightens. Credential acquisition is front-loaded within the compliance year, and shallow decision trees reproduce the policy’s decisions with high fidelity. Treating the persistent-friction weight as a continuous representation of regulatory strictness further reveals an absorb-then-adjust pattern, in which expected rewards decline before observable behavior changes, implying  \nthat assessments based only on behavioral indicators may understate the burden already borne by firms. Moreover, the system is not tied to any specific regulation and can be transferred to other jurisdictions and rule-based compliance problems.  \nKeywords: Data governance; Cross-border data flows; Markov decision process (MDP); Deep reinforcement learning  \nYuepeng Zhou, Dongchi Xing, Li Xiong: School of Management, Shanghai University, Shanghai 200444, China.  \n*Corresponding author: Li Xiong ([xiongli7@126.com](xiongli7@126.com)). This work was supported by the National Social Science Fund Postdoctoral Research Funding Project of China (No. 24FGLB114).  \n1. Introduction  \nData has become a nonrival factor of production whose economic value is often realized through reuse and flows across teams, firms, and national borders (Goldfarb and Tucker 2019; Jones and Tonetti 2020) . Yet these value-creating flows of data have become a persistent focus of data governance regimes across jurisdictions. Organized around data sovereignty, data localization, and personal information protection, overlapping and differentiated regulatory regimes have transformed data transmission from a largely technical matter into an institutionally constrained firm decision. Cross-country regulatory differences, in turn, translate directly into firm-level compliance costs (Rong et al. 2025) . OECD/WTO estimates indicate that, if all economies fully restricted data flows and moved to a completely fragmented data regime, global GDP would fall by about 4.5% and exports by about 8.5%, whereas open data flows with safeguards would increase output (OECD/WTO 2025) . Cross-border data governance is therefore not an ancillary compliance issue but a central concern in the functioning of the digital economy. Facing such regimes, firms must balance the compliance costs of lawful transfer against the business value generated by cross-border data transfer. This trade-off also has an investment character: compliance outlays are incurred up front, whereas trust-based returns materialize only over the long term, creating a spend-first, benefit-later pattern (Chisam et al. 2026) .","cbCaiiCSp00FLHYy","https://ap.wps.com/l/cbCaiiCSp00FLHYy","pdf",1469007,3,1,43,"English","en",105,"# Introduction\n## Institutional compliance and dynamic decision making","[{\"question\":\"What problem does the paper address about cross-border data transfers?\",\"answer\":\"It addresses how firms repeatedly face compliance trade-offs under increasingly stringent governance regimes, turning transfers into a sequential decision problem rather than a one-time choice.\"},{\"question\":\"How is compliance represented in the proposed decision support system?\",\"answer\":\"Compliance is represented as a hard constraint instead of a penalty term, and regulatory rules are mapped into a computable minimal compliance mapping.\"},{\"question\":\"What modeling and learning methods are used to generate firm decision policies?\",\"answer\":\"The paper models weekly decisions as a finite-horizon Markov decision process and solves the resulting problem using masked deep reinforcement learning.\"}]",1784207574,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"sequential-compliance-decisions-of-firms-on-cross-border-data-flows-an-institutionally-anchored-decision-support-system","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/sequential-compliance-decisions-of-firms-on-cross-border-data-flows-an-institutionally-anchored-decision-support-system/85986/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What 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