Disciplines · Research

Concordia Research Bibliography

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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 (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
  2. Fairness, social welfare, and division
  3. Mechanism design
  4. Negotiation benchmarks and competitions
  5. LLM mediation and mediator assistance
  6. Multi-party mediation research
  7. LLM negotiation capability, cost, and failure analysis
  8. Preference learning and utility inference
  9. Search, optimization, and agreement generation
  10. Online dispute resolution (ODR)
  11. Restorative justice
  12. Mediation ethics and professional responsibility
  13. Standards, regulation, and governance
  14. Privacy, confidentiality, and confidential compute
  15. Smart-contract settlement and decentralized arbitration
  16. 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 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.