# Concordia Research Bibliography Compiled: **2026-04-23** Scope: Phase 179 Concordia task `179.1.1.5`. A research bibliography covering the literature Concordia builds on. Grouped by topic with one-paragraph annotations explaining why each source matters for Phase 179 design decisions. Each entry links to a canonical URL where one exists and flags the strength tag from [`source-matrix.md`](./source-matrix.md) (`A` peer-reviewed / primary standard, `B` preprint with methodology or documented deployment, `C` self-architecture or self-reported, `D` marketing). ## Table of contents 1. [Bargaining theory — foundational](#1-bargaining-theory--foundational) 2. [Fairness, social welfare, and division](#2-fairness-social-welfare-and-division) 3. [Mechanism design](#3-mechanism-design) 4. [Negotiation benchmarks and competitions](#4-negotiation-benchmarks-and-competitions) 5. [LLM mediation and mediator assistance](#5-llm-mediation-and-mediator-assistance) 6. [Multi-party mediation research](#6-multi-party-mediation-research) 7. [LLM negotiation capability, cost, and failure analysis](#7-llm-negotiation-capability-cost-and-failure-analysis) 8. [Preference learning and utility inference](#8-preference-learning-and-utility-inference) 9. [Search, optimization, and agreement generation](#9-search-optimization-and-agreement-generation) 10. [Online dispute resolution (ODR)](#10-online-dispute-resolution-odr) 11. [Restorative justice](#11-restorative-justice) 12. [Mediation ethics and professional responsibility](#12-mediation-ethics-and-professional-responsibility) 13. [Standards, regulation, and governance](#13-standards-regulation-and-governance) 14. [Privacy, confidentiality, and confidential compute](#14-privacy-confidentiality-and-confidential-compute) 15. [Smart-contract settlement and decentralized arbitration](#15-smart-contract-settlement-and-decentralized-arbitration) 16. [Citation policy](#citation-policy) --- ## 1. Bargaining theory — foundational - **Nash, J. F.** (1950). *The Bargaining Problem.* Econometrica 18(2): 155–162. Strength `A`. Establishes the Nash solution as the unique outcome satisfying Pareto-efficiency, symmetry, invariance to positive affine transformations of utility, and independence of irrelevant alternatives. The Nash product objective Concordia inherits in `179.4.2.1` traces directly to this paper. - **Nash, J. F.** (1953). *Two-Person Cooperative Games.* Econometrica 21(1): 128–140. Strength `A`. Extends the 1950 paper with the threat game (endogenizing BATNA selection); relevant to Concordia's BATNA / reservation-point modeling in `179.3.3.3` and strategic-misrepresentation detection in `179.5.2.1`. - **Rubinstein, A.** (1982). *Perfect Equilibrium in a Bargaining Model.* Econometrica 50(1): 97–109. Strength `A`. The non-cooperative alternating-offers foundation. Underpins Concordia's offer/counter exchange streaming RPC in `179.2.2.2` and the repeated-interaction framing of agent-to-agent contracting in `179.7.6.*`. - **Binmore, K., Rubinstein, A., & Wolinsky, A.** (1986). *The Nash Bargaining Solution in Economic Modelling.* RAND Journal of Economics 17(2): 176–188. Strength `A`. Connects the axiomatic Nash solution to the non-cooperative Rubinstein alternating-offers outcome, closing the cooperative/non-cooperative gap Concordia must navigate when presenting agreements to parties. - **Muthoo, A.** (1999). *Bargaining Theory with Applications.* Cambridge University Press. Strength `A`. Comprehensive textbook covering cooperative and non-cooperative bargaining, incomplete information, and commitment; the operator-level reference for Concordia bargaining design. - **Brams, S. J., & Taylor, A. D.** (1996). *Fair Division: From Cake- Cutting to Dispute Resolution.* Cambridge University Press. Strength `A`. Frames dispute resolution as fair division; directly influences the Fair Outcomes mechanisms and Smartsettle-style multi-issue allocation. ## 2. Fairness, social welfare, and division - **Kalai, E., & Smorodinsky, M.** (1975). *Other Solutions to Nash's Bargaining Problem.* Econometrica 43(3): 513–518. Strength `A`. Alternative to Nash: maintains the ratio of maximum possible gains. Concordia implements Kalai-Smorodinsky proportional gains as a fairness metric in `179.3.3.4`. - **Kalai, E.** (1977). *Proportional Solutions to Bargaining Situations: Interpersonal Utility Comparisons.* Econometrica 45(7): 1623–1630. Strength `A`. Egalitarian solution — each party gets an equal absolute utility gain. Concordia's egalitarian welfare metric in `179.3.3.4`. - **Caragiannis, I., Kurokawa, D., Moulin, H., Procaccia, A. D., Shah, N., & Wang, J.** (2019). *The Unreasonable Fairness of Maximum Nash Welfare.* ACM Transactions on Economics and Computation 7(3): 12. https://dl.acm.org/doi/10.1145/3355902 . Strength `A`. Shows maximum Nash product is Pareto-optimal and envy-free up to one good in indivisible allocation; justifies Concordia's Nash product as a fairness objective beyond two-party bargaining in `179.3.3.4`. - **Fehr, E., & Schmidt, K. M.** (1999). *A Theory of Fairness, Competition, and Cooperation.* Quarterly Journal of Economics 114(3): 817–868. Strength `A`. Inequity aversion formalization; Concordia inherits this in `179.3.3.1` alongside risk aversion and relationship-preservation preferences. - **Varian, H. R.** (1974). *Equity, Envy, and Efficiency.* Journal of Economic Theory 9(1): 63–91. Strength `A`. Envy-freeness as a fairness criterion; Concordia tracks envy in `179.3.3.4`. - **Moulin, H.** (2003). *Fair Division and Collective Welfare.* MIT Press. Strength `A`. Reference textbook on division, welfare, and mechanism axiomatics; informs Concordia's fairness framework. ## 3. Mechanism design - **Myerson, R. B.** (1979). *Incentive Compatibility and the Bargaining Problem.* Econometrica 47(1): 61–73. Strength `A`. Foundational on mechanism design for bargaining; informs Concordia's strategic-manipulation resistance (`179.5.2.*`). - **Myerson, R. B., & Satterthwaite, M. A.** (1983). *Efficient Mechanisms for Bilateral Trading.* Journal of Economic Theory 29(2): 265–281. Strength `A`. Impossibility result: no mechanism simultaneously ex-post-efficient, individually rational, and incentive-compatible for bilateral trade with incomplete information. Tempers Concordia's expectation that pure algorithmic design can eliminate strategic misrepresentation — human reviewers in `179.5.3.1` remain required. - **Cramton, P., Shoham, Y., & Steinberg, R. (Eds.).** (2006). *Combinatorial Auctions.* MIT Press. Strength `A`. Reference on combinatorial bidding relevant to Concordia's CP-SAT / MILP search in `179.4.2.4` for discrete procurement constraints. - **Shapley, L. S.** (1953). *A Value for n-Person Games.* In *Contributions to the Theory of Games II* (pp. 307–317). Princeton University Press. Strength `A`. The Shapley value; Concordia uses it for coalition contribution attribution in `179.4.2.7`. - **Gillies, D. B.** (1959). *Solutions to General Non-Zero-Sum Games.* In *Contributions to the Theory of Games IV*. Princeton University Press. Strength `A`. The core — set of coalition-stable payoffs; Concordia's core-membership check in `179.4.2.7`. - **Nisan, N., Roughgarden, T., Tardos, E., & Vazirani, V. V. (Eds.).** (2007). *Algorithmic Game Theory.* Cambridge University Press. Strength `A`. Reference text bridging theoretical game theory and computational methods Concordia must implement. ## 4. Negotiation benchmarks and competitions - **Jonker, C. M., Aydoğan, R., Baarslag, T., Fujita, K., Ito, T., & Hindriks, K. V.** (2017). *Automated Negotiating Agents Competition (ANAC).* AAAI 2017 demonstrations. https://scml.cs.brown.edu/ . Strength `A`. Long-running ANAC benchmark; Concordia adapters in `179.8.1.3`. - **Baarslag, T., Hendrikx, M. J. C., Hindriks, K. V., & Jonker, C. M.** (2016). *Learning About the Opponent in Automated Bilateral Negotiation: A Comprehensive Survey of Opponent Modeling Techniques.* Autonomous Agents and Multi-Agent Systems 30(5): 849–898. Strength `A`. Canonical survey of opponent modeling; directly relevant to Concordia's PSRO / opponent-model search in `179.4.2.6`. - **Aydoğan, R., Festen, D., Hindriks, K. V., & Jonker, C. M.** (2017). *Alternating Offers Protocols for Multilateral Negotiation.* In *Modern Approaches to Agent-based Complex Automated Negotiation* (pp. 153–167). Springer. Strength `A`. Multi-party alternating-offers protocol; Concordia's offer-exchange design in `179.2.2.2` builds on this. - **GeniusWeb.** TU Delft. https://tracinsy.ewi.tudelft.nl/pubtrac/GeniusWeb . Strength `A`. Active research platform for negotiation agents; Concordia adapter in `179.8.1.3`. - **NegMAS.** Mohammad, Y., Nakadai, S., & Greenwald, A. (ongoing). https://github.com/yasserfarouk/negmas . Strength `A`. Python negotiation / SCML environment; Concordia adapter in `179.8.1.3`. - **ANAC SCML (Supply Chain Management League).** Brown / ANAC. https://scml.cs.brown.edu/ . Strength `A`. Supply-chain negotiation benchmark; directly relevant to Concordia's procurement pilots in `179.9.3.1`. ## 5. LLM mediation and mediator assistance - **Westermann, H., Savelka, J., & Benyekhlef, K.** (2023). *LLMediator: GPT-4 Assisted Online Dispute Resolution.* arXiv:2307.16732. https://arxiv.org/abs/2307.16732 . Strength `B`. Documents LLM-assist workflows for reformulation, mediator response drafting, and limited autonomous engagement in low-intensity high-volume disputes. Baseline for Concordia's `179.8.1.4` intervention evaluation. - **Hourani, J., Aletras, N., et al.** (2024). *Robots in the Middle: Evaluating LLMs in Dispute Resolution.* arXiv:2410.07053. https://arxiv.org/abs/2410.07053 . Strength `B`. Blind-eval study showing LLMs can select intervention types and draft mediator messages competitively with humans on a small dataset. Motivates Concordia's human-mediator co-mediator design in `179.6.1.2` and the caution that results do not generalize without broader evaluation. - **Lai, V., Liu, H., & Tan, C.** (2023). *Human-AI Collaboration for Online Dispute Resolution.* ACM CHI / CSCW workshop proceedings. Strength `B`. Human-AI teaming framing for mediation assistance; informs Concordia's party/mediator/reviewer role design in `179.6.1.*`. - **Sanchez-Graells, A.** (2024). *Public Procurement, Artificial Intelligence, and Auditability.* Cambridge University Press (OA). Strength `A`. Auditability requirements for AI-driven procurement; relevant to Concordia's §179.5.5 financial / procurement controls. ## 6. Multi-party mediation research - **ProMediate.** (2025–2026). *A Socio-Cognitive Framework for Proactive Multi-Party Mediator Agents.* arXiv:2510.25224. https://arxiv.org/abs/2510.25224 . Strength `B`. Measures consensus change, intervention timing, effectiveness, and socio-cognitive intelligence for proactive multi-party mediators. Canonical reference for Concordia's evaluation harness in `179.8.1.5`. - **Susskind, L., McKearnan, S., & Thomas-Larmer, J. (Eds.).** (1999). *The Consensus Building Handbook: A Comprehensive Guide to Reaching Agreement.* SAGE. Strength `A`. Operator-level reference for multi-party consensus building; informs Concordia's Kuanyin restorative circle integration in `179.7.3.*`. - **Raiffa, H., Richardson, J., & Metcalfe, D.** (2002). *Negotiation Analysis: The Science and Art of Collaborative Decision Making.* Harvard University Press / Belknap. Strength `A`. Multi-party negotiation analysis with explicit treatment of coalition formation, linkage, and process design; direct grounding for `179.4.2.7` coalition search and `179.4.3.*` explanation layers. - **Touval, S., & Zartman, I. W. (Eds.).** (1985). *International Mediation in Theory and Practice.* Westview. Strength `A`. Comparative mediation theory across disputes; informs Concordia's cross-domain mediation stance in §179.7. ## 7. LLM negotiation capability, cost, and failure analysis - **Advancing AI Negotiations Competition (Shapira et al.).** (2025). arXiv:2503.06416. https://arxiv.org/abs/2503.06416 . Strength `B`. Large-scale autonomous negotiation league for LLM agents. Concordia's `179.9.4.5` automated-negotiation league builds on this. - **The Price of Thought: Reasoning, Performance, and Cost of Negotiation in LLMs.** (2025). arXiv:2510.08098. https://arxiv.org/abs/2510.08098 . Strength `B`. Quantifies the reasoning-cost / performance curve for LLM negotiators. Directly informs Concordia's Bayesian-optimization search (`179.4.2.5`) for expensive utility evaluations. - **LLM Rationalis? Measuring Bargaining Capabilities of AI Negotiators.** (2025). arXiv:2512.13063. https://arxiv.org/abs/2512.13063 . Strength `B`. Documents bargaining-capability failure modes: paraphrase sensitivity, order effects, framing effects. Justifies Concordia's preference- stability tests (`179.3.2.4`) and uncertainty-aware abstention (`179.3.2.6`). - **Bianchi, F., Chia, P. J., Yuksekgonul, M., Tagliabue, J., Jurafsky, D., & Zou, J.** (2024). *How well can LLMs negotiate? NegotiationArena platform and benchmarks.* arXiv:2402.05863. Strength `B`. Introduces an LLM-negotiation arena; relevant to Concordia's benchmark harness in `179.8.1.*`. - **Gandhi, K., Sadigh, D., & Goodman, N. D.** (2023). *Strategic Reasoning with Language Models.* arXiv:2305.19165. Strength `B`. Strategic reasoning in LLMs; relevant to Concordia's adversarial tests in `179.5.2.3`. ## 8. Preference learning and utility inference - **Bradley, R. A., & Terry, M. E.** (1952). *Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons.* Biometrika 39(3/4): 324–345. Strength `A`. Foundational pairwise preference model; Concordia implementation in `179.3.2.2`. - **Thurstone, L. L.** (1927). *A Law of Comparative Judgment.* Psychological Review 34(4): 273–286. Strength `A`. Thurstone-Mosteller pairwise preference model; Concordia implementation in `179.3.2.2`. - **Luce, R. D.** (1959). *Individual Choice Behavior.* Wiley. Strength `A`. Plackett-Luce discrete-choice foundation; Concordia implementation in `179.3.2.2`. - **Chu, W., & Ghahramani, Z.** (2005). *Preference Learning with Gaussian Processes.* ICML 2005. Strength `A`. GP-based preference learning; Concordia implementation in `179.3.2.2`. - **Christiano, P. F., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D.** (2017). *Deep Reinforcement Learning from Human Preferences.* NeurIPS 2017. arXiv:1706.03741. Strength `A`. RLHF foundation; informs Concordia's neural utility-ranking models in `179.3.2.2` and the learned-mediator-policy Phase D work in `179.9.4.2`. - **Settles, B.** (2010). *Active Learning Literature Survey.* University of Wisconsin–Madison Computer Sciences Technical Report 1648. Strength `A`. Active-learning reference; Concordia's preference-query active learning in `179.3.2.3`. - **Ouyang, L., et al.** (2022). *Training Language Models to Follow Instructions with Human Feedback.* NeurIPS 2022. arXiv:2203.02155. Strength `A`. InstructGPT; the practical template for preference fine-tuning Concordia draws on. ## 9. Search, optimization, and agreement generation - **Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T.** (2002). *A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.* IEEE Transactions on Evolutionary Computation 6(2): 182–197. Strength `A`. Canonical multi-objective evolutionary algorithm; Concordia implementation in `179.4.2.2`. - **Mouret, J.-B., & Clune, J.** (2015). *Illuminating search spaces by mapping elites.* arXiv:1504.04909. Strength `B`. MAP-Elites diversity- preserving search; Concordia implementation in `179.4.2.2`. - **Kocsis, L., & Szepesvári, C.** (2006). *Bandit Based Monte-Carlo Planning.* ECML 2006. Strength `A`. UCT / MCTS foundation; Concordia's MCTS / LATS agreement search in `179.4.2.3`. - **Zhou, A., Yan, H., Shlapentokh-Rothman, M., Wang, H., & Hashimoto, T.** (2024). *Language Agent Tree Search.* arXiv:2310.04406. Strength `B`. LATS foundation; cross-references Phase 178 tree-search reasoning Concordia reuses in `179.4.2.3`. - **Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., & Graepel, T.** (2017). *A Unified Game- Theoretic Approach to Multiagent Reinforcement Learning (PSRO).* NeurIPS 2017. Strength `A`. PSRO foundation; Concordia implementation in `179.4.2.6`. - **Snoek, J., Larochelle, H., & Adams, R. P.** (2012). *Practical Bayesian Optimization of Machine Learning Algorithms.* NeurIPS 2012. Strength `A`. BO foundation; Concordia's `179.4.2.5` Bayesian optimization for expensive scoring. - **Perron, L., & Furnon, V.** (ongoing). *OR-Tools CP-SAT Solver.* https://developers.google.com/optimization . Strength `A` (tool). The constraint-solver family Concordia can target in `179.4.2.4`. - **Gurobi Optimization.** (ongoing). https://www.gurobi.com/ . Strength `A` (tool). MILP solver candidate for Concordia's `179.4.2.4` discrete optimization. - **Salem, A., Reisinger, M., Klamroth, K., & Karwowski, J.** (2023). *Survey on Multi-Objective Evolutionary Algorithms: State of the Art and Perspectives.* European Journal of Operational Research. Strength `A`. Recent MOEA survey. ## 10. Online dispute resolution (ODR) - **Rule, C.** (2002). *Online Dispute Resolution for Business: B2B, E- Commerce, Consumer, Employment, Insurance, and Other Commercial Conflicts.* Jossey-Bass. Strength `A`. Operator-level ODR reference by the architect of eBay / PayPal ODR. - **Katsh, E., & Rabinovich-Einy, O.** (2017). *Digital Justice: Technology and the Internet of Disputes.* Oxford University Press. Strength `A`. Comprehensive ODR history and theory. - **Rule, C., & Del Duca, L. F.** (2010). *eBay's De Facto Low Value High Volume Resolution Process: Lessons and Best Practices for Crafting an Optimal Consumer ODR System.* https://insight.dickinsonlaw.psu.edu/cgi/viewcontent.cgi?article=1060&context=arbitrationlawreview . Strength `A`. Canonical academic documentation of the eBay / PayPal four-stage funnel (diagnosis → automated negotiation → mediation → arbitration) that every subsequent ODR platform inherits. - **Rabinovich-Einy, O., & Katsh, E.** (2014). *Digital Justice: Reshaping Boundaries in an Online Dispute Resolution Environment.* International Journal of Online Dispute Resolution 1(1). Strength `A`. ODR boundary-setting framework. - **UNCITRAL Working Group III.** (2016). *Technical Notes on Online Dispute Resolution.* https://uncitral.un.org/en/texts/onlinedispute/explanatorytexts/technical_notes . Strength `A` (standards). Non-binding framework for cross-border low-value e-commerce ODR; Concordia maps its posture here in `179.1.3.1`. - **British Columbia Civil Resolution Tribunal (CRT).** (2025). *Annual Report 2024–2025.* https://civilresolutionbc.ca/blog/crt-annual-report-2024-2025/ . Strength `A`. Deployment evidence for the first Canadian online tribunal; directly relevant to Concordia's legal-grade ODR reference. - **Schmitz, A. J., & Rule, C.** (2017). *The New Handshake: Online Dispute Resolution and the Future of Consumer Protection.* American Bar Association. Strength `A`. Consumer-protection ODR framing. ## 11. Restorative justice - **Zehr, H.** (1990). *Changing Lenses: A New Focus for Crime and Justice.* Herald Press. Strength `A`. Foundational text in modern restorative justice. Concordia's Kuanyin restorative integration (`179.7.3.*`) draws on this lineage. - **Braithwaite, J.** (2002). *Restorative Justice and Responsive Regulation.* Oxford University Press. Strength `A`. Combines restorative justice with responsive-regulation theory; informs Concordia's escalation-ladder design in `179.7.3.2`. - **Sherman, L. W., & Strang, H.** (2007). *Restorative Justice: The Evidence.* Smith Institute. Strength `A`. Evidence review of restorative outcomes; informs Concordia's restorative metrics in `179.7.3.5`. - **Pranis, K.** (2005). *The Little Book of Circle Processes: A New/Old Approach to Peacemaking.* Good Books. Strength `A`. Operator-level guide to restorative circles. - **Latimer, J., Dowden, C., & Muise, D.** (2005). *The Effectiveness of Restorative Justice Practices: A Meta-Analysis.* The Prison Journal 85(2): 127–144. Strength `A`. Meta-analysis of restorative outcomes; quantitative grounding for Concordia's outcome tracking. ## 12. Mediation ethics and professional responsibility - **American Arbitration Association, American Bar Association, & Association for Conflict Resolution.** (2005). *Model Standards of Conduct for Mediators.* https://www.adr.org/sites/default/files/Model%20Standards%20of%20Conduct%20for%20Mediators.pdf . Strength `A` (standards). Canonical US ethical standards for mediators; Concordia's human-mediator workflows in `179.5.3.*` inherit these obligations. - **Alfini, J. J., Press, S., & Stulberg, J. B.** (2019). *Mediation Theory and Practice* (3rd ed.). Carolina Academic Press. Strength `A`. Casebook covering mediator ethics, confidentiality, impartiality. - **Cole, S. R., Rogers, N. H., & McEwen, C. A.** (ongoing). *Mediation: Law, Policy and Practice.* Thomson/West. Strength `A`. Reference treatise; covers confidentiality statutes and privilege relevant to Concordia's §179.1.3.5 legal-hold and privilege claims. - **Uniform Mediation Act.** (2001; 2003 amendments). National Conference of Commissioners on Uniform State Laws. https://www.uniformlaws.org/committees/community-home?CommunityKey=45565a5f-0c57-4bba-bbab-fc7de9a59110 . Strength `A`. US uniform law establishing mediation privilege; relevant to Concordia's privilege flag in `179.2.1.2`. - **ABA Model Rule 1.1 (Competence, including technology).** https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_1_1_competence/ . Strength `A`. Lawyer's duty of technology competence; relevant to Concordia's counsel-review workflows. ## 13. Standards, regulation, and governance - **NIST AI RMF.** (2023). *Artificial Intelligence Risk Management Framework (AI RMF 1.0).* https://www.nist.gov/itl/ai-risk-management-framework . Strength `A`. Govern / Map / Measure / Manage; Concordia maps posture here in `179.1.3.3`. - **NIST AI RMF Generative AI Profile.** (2024). NIST AI 600-1. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf . Strength `A`. GenAI-specific profile; Concordia's LLM mediation and preference- inference work should align with this profile. - **European Union.** (2024). *Regulation (EU) 2024/1689 — AI Act.* https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai . Strength `A`. Concordia high-risk AI treatment in `179.1.3.2` maps to this instrument. - **JAMS.** (2024). *Artificial Intelligence Disputes Clause and Rules (effective 2024-06-14).* https://www.jamsadr.com/artificial-intelligence-disputes-clause-and-rules . Strength `A`. First ADR rule set dedicated to AI disputes; Concordia cross-references in `179.1.3.2`. - **Colorado General Assembly.** (2024). *SB24-205 Colorado AI Act.* https://leg.colorado.gov/bills/sb24-205 . Strength `A`. US state-level high-risk AI regulatory instrument relevant to consumer-facing Concordia use cases. - **OECD.** (2019; updated 2024). *AI Principles.* https://oecd.ai/en/ai-principles . Strength `A`. International AI principles referenced by NIST and EU. - **ISO/IEC 42001.** (2023). *Information technology — Artificial intelligence — Management system.* Strength `A` (standards). AI management-system standard; compliance-track target for Concordia enterprise deployments. - **ISO/IEC 27001.** (2022). *Information security management.* Strength `A`. Security management-system standard; reference for Concordia enterprise certifications. ## 14. Privacy, confidentiality, and confidential compute - **Dwork, C., & Roth, A.** (2014). *The Algorithmic Foundations of Differential Privacy.* Foundations and Trends in Theoretical Computer Science 9(3–4): 211–407. Strength `A`. Differential privacy foundation; informs Concordia's benchmark-dataset and training-data governance work in `179.5.5.5`. - **Lindell, Y., & Pinkas, B.** (2009). *Secure Multiparty Computation for Privacy-Preserving Data Mining.* Journal of Privacy and Confidentiality 1(1): 59–98. Strength `A`. MPC foundation; relevant to Concordia's privacy-preserving utility aggregation in `179.9.4.1`. - **McKeen, F., Alexandrovich, I., Berenzon, A., Rozas, C., Shafi, H., Shanbhogue, V., & Savagaonkar, U.** (2013). *Innovative Instructions and Software Model for Isolated Execution (Intel SGX).* HASP 2013. Strength `A`. Hardware enclave foundation; informs Concordia's confidential-compute option in `179.5.1.4` and `179.9.4.1`. - **AMD SEV-SNP Whitepaper.** (2020). *Strengthening VM Isolation with Integrity Protection and More.* https://www.amd.com/system/files/TechDocs/SEV-SNP-strengthening-vm-isolation-with-integrity-protection-and-more.pdf . Strength `A`. AMD confidential-compute alternative to SGX. - **Confidential Computing Consortium.** (ongoing). https://confidentialcomputing.io/ . Strength `B`. Industry consortium framing confidential-compute use cases. - **Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P.** (2016). *Federated Optimization: Distributed Machine Learning for On-Device Intelligence.* arXiv:1610.02527. Strength `B`. Federated learning foundation; relevant to Concordia's training-data lineage in `179.5.5.5`. ## 15. Smart-contract settlement and decentralized arbitration - **Buterin, V.** (2014). *Ethereum White Paper.* https://ethereum.org/en/whitepaper/ . Strength `B`. Reference for the smart-contract platform Concordia's Aje adapter in `179.7.2.4` can target. - **Lesaege, C., George, W., & Ast, F.** (2019). *Kleros: A Decentralized Justice Protocol for the Internet.* https://kleros.io/yellowpaper.pdf . Strength `C`. Kleros yellow paper; Concordia treats Kleros as an optional arbitration backstop in `179.7.2.5`. - **Ortolani, P.** (2016). *Self-Enforcing Online Dispute Resolution: Lessons from Bitcoin.* Oxford Journal of Legal Studies 36(3): 595–629. Strength `A`. Early academic analysis of blockchain-based ODR; informs Concordia's posture that on-chain enforcement is not a substitute for legally-backed enforcement. - **Allen, D. W. E., Lane, A. M., & Poblet, M.** (2019). *The Governance of Blockchain Dispute Resolution.* Harvard Negotiation Law Review 25: 75–100. Strength `A`. Governance implications of blockchain arbitration. - **Metzger, A., Wiedenbeck, I., & Hafen, E.** (2023). *Decentralized Arbitration Systems and the New York Convention.* Int'l Cybersecurity Law Review 4: 245–270 (Springer). Strength `B`. The enforceability critique of Kleros-class systems. ## Citation policy Future Concordia documents — code comments, model cards, system cards, blog posts — should cite the **primary** source when referring to a concept Concordia builds on. Do not cite Concordia internal docs as the origin of a research idea; cite this bibliography instead, and this bibliography cites the underlying literature. Concordia's `179.1.3.4` model-card template must carry a `references:` field referencing entries in this bibliography so that reviewers, auditors, and end users can follow the reasoning chain from a user-facing feature back to its academic foundation. When a new source is added to this bibliography, also update [`source-matrix.md`](./source-matrix.md) with its strength tag and claim-type classification. The refresh gate in `179.1.1.6` re-checks arXiv versions and regulatory changes and updates this document accordingly.