9. Research Directions and Open Challenges

Based on use case analysis in Chapter 8 and survey of existing research, we organize research directions toward CADL realization and development. For each direction, we present related existing research and concrete research challenges that CADL should address.

9.1 Algebraic Composition Theory for Institutions

Establishment of an algebraic foundation for safely composing and decomposing multiple institutional descriptions is necessary. Benveniste et al. [Foundations and Trends in EDA, 2018] theorized parallel composition of contracts C₁ ⊗ C₂ in component design, but composition theory including three institutional layers (Institution, Protocol, Algorithm) remains unestablished. For example, when composing household chore-sharing institution with shared resource usage institution, a theory that algebraically guarantees absence of obligation conflicts and incentive interference is required.

ID	Challenge	Content
RD-1.1	Institutional Composition Algebra	Algebraic definitions and soundness proofs for parallel, sequential, and choice composition of institutions including three layers (Institution, Protocol, Algorithm).
RD-1.2	Institutional Refinement Relations	Formal proofs that overview level → design level → verification level refinements preserve institutional properties.
RD-1.3	Topological Structure of Parameter Space	Mathematical characterization of regime boundaries on institutional parameter space (α, β, λ). Continuity, convexity, monotonicity conditions.

9.2 Extraction and Institutionalization of Tacit Knowledge

Establishment of technology to explicitly formalize institutional knowledge implicitly shared in household rules and community customs as CADL descriptions is required. IndoorWorld [Wu+, Findings of EMNLP 2025] realizes behavior simulation of LLM agents in indoor environments, but the inverse problem of extracting institutional rules from observed behavior patterns (institutional reverse engineering) remains unaddressed.

ID	Challenge	Content
RD-2.1	Institutional Inference from Behavioral Observation	Algorithm to infer potential institutional rules (implicit authority structures, information sharing patterns, incentive structures) from IoT sensor data and behavior logs.
RD-2.2	LLM-based Institutional Description Generation	Technology to automatically generate CADL descriptions from natural language interview records and meeting minutes. Extends LLM-based Symboleo generation [Zitouni+, 2025] methods to general institutional descriptions.
RD-2.3	Tacit Knowledge Ontology for Institutions	Build ontology systematically classifying and structuring implicit institutional knowledge. Map Ostrom [1990] IAD framework [Ostrom, 2005] to CADL vocabulary.

9.3 Institutional Digital Twins

Developing the concept of Digital Twins of Organization [Lyytinen+, J. Organization Design, 2024], establishment of technology to build and simulate institutions themselves as digital twins is the next-generation research direction. vCity [BSC] and Social Digital Twin [Fujitsu] advance twinning of physical and social aspects, but twinning of institutions (governance structures, rules, incentives) is, to our knowledge, not their main target.

ID	Challenge	Content
RD-3.1	Institutional Simulator	Technology to automatically generate institutional simulation models from CADL descriptions and what-if analyze impacts of institutional parameter changes. MAPF simulator integration is initial target.
RD-3.2	Institutional Synchronization and Divergence Detection	Technology to detect divergence between real institutional operation and CADL descriptions (twins) via runtime monitoring and automatically update institutional descriptions. Integration with IEC 62853 [IEC 62853:2018] change management process.
RD-3.3	Institutional A/B Testing	Technology to operate different institutional designs in parallel and perform comparative experiments on performance. Integration with AI Economist [Zheng+, 2022] mechanism design automation methods.

9.4 Scalability and Computational Complexity

Scalability of formal verification and code generation of institutions is one of the most critical challenges toward practical deployment. To complete verification of SoS descriptions containing 100+ actors and 50+ contracts within practical time, theoretical analysis of computational complexity of verification methods and development of efficient algorithms are necessary.

ID	Challenge	Content
RD-4.1	Computational Complexity of Institutional Verification	Determine computational complexity classes for consistency verification, deadlock detection, and safety verification of CADL descriptions respectively. Identify decidability boundary conditions.
RD-4.2	Abstraction-based Scalable Verification	Methods to efficiently verify large-scale SoS through hierarchical abstraction of institutional descriptions. Application of counterexample-guided abstraction refinement (CEGAR) [Clarke+, 2000] to institutional verification.
RD-4.3	Incremental Verification	Technology to confirm consistency through differential verification rather than full re-verification when actors join/leave or institutions change. Implementation of compositional verification theory.

9.5 Multi-Cultural and Multi-Jurisdictional Support

For CADL to be applied to international SoS, support for different legal systems, cultural norms, and language environments is necessary. The EU Data Act (Regulation (EU) 2023/2854) sets essential requirements for smart contracts used for the automated execution of data sharing agreements, but institutional compatibility with other jurisdictions (Japan, US, China, etc.) remains an unresolved challenge.

ID	Challenge	Content
RD-5.1	Cross-jurisdictional Institutional Compatibility Verification	Technology to detect legal contradictions when composing CADL descriptions from different jurisdictions (e.g., differences between EU GDPR [Regulation (EU) 2016/679] and Japanese Personal Information Protection Act [Act No. 57 of 2003]).
RD-5.2	Parameterization of Cultural Norms	Technology to model cultural dimensions (collectivism/individualism, power distance, uncertainty avoidance, etc.) as institutional parameters and handle cross-cultural governance differences quantitatively.
RD-5.3	Multilingual Institutional Description	Enable CADL overview-level descriptions in multiple languages with AI verifying institutional equivalence across languages. This goes beyond NFR-7 (internationalization), which only requires that human-readable text can be written in any language.

9.6 Research Roadmap

We organize the above research directions along a time axis and present a short-, medium-, and long-term research roadmap:

Period	Key Challenges	Content
Short-term (2026-2027)	RD-1.1, RD-1.2, RD-2.2, RD-4.1, RD-4.3	Basic theory of institutional composition (RD-1.1) and of refinement between description levels (RD-1.2), initial prototype of CADL generation via LLM (RD-2.2), computational complexity analysis of verification (RD-4.1), incremental verification when actors join or leave (RD-4.3).
Medium-term (2027-2028)	RD-1.3, RD-2.1, RD-3.1, RD-4.2	Mathematical analysis of parameter space (RD-1.3), experiments in institutional inference from IoT sensors (RD-2.1), institutional simulator construction, starting with integrated experiments with a MAPF simulator (RD-3.1), implementation of scalable verification methods (RD-4.2).
Long-term (2028-2030)	RD-2.3, RD-3.2, RD-3.3, RD-5.1-5.3	Tacit knowledge ontology (RD-2.3), complete implementation of institutional digital twins including divergence detection (RD-3.2), institutional A/B testing platform (RD-3.3), multicultural and multi-jurisdictional support (RD-5.1 to RD-5.3).