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Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System., and . AAMAS, page 233-241. International Foundation for Autonomous Agents and Multiagent Systems, (2019)Rethinking Blockchain Security: Position Paper., , , , , , , and . iThings/GreenCom/CPSCom/SmartData, page 1273-1280. IEEE, (2018)Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent., , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 391-407. PMLR, (2020)From Nash Equilibria to Chain Recurrent Sets: Solution Concepts and Topology., and . ITCS, page 227-235. ACM, (2016)Approximating Nash Equilibria in Tree Polymatrix Games., , and . SAGT, volume 9347 of Lecture Notes in Computer Science, page 285-296. Springer, (2015)Weighted voting on the blockchain: Improving consensus in proof of stake protocols., , and . Int. J. Netw. Manag., (2020)Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization., , and . CoRR, (2023)No-Regret Learning and Equilibrium Computation in Quantum Games., , , and . CoRR, (2023)Cycles in Adversarial Regularized Learning., , and . SODA, page 2703-2717. SIAM, (2018)Robust Self-organization in Games: Symmetries, Conservation Laws and Dimensionality Reduction., , and . AAMAS, page 1946-1948. International Foundation for Autonomous Agents and Multiagent Systems, (2020)