arXiv subjects

Nikolaos Al. Papadopoulos

Publications and source records attributed to Nikolaos Al. Papadopoulos.

3 recordsLinked to original sources

arXiv2026

Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies

Reporting guidance for information-theoretic measures is rarely tested against ground truth. We test one guardrail in two multi-agent reinforcement learning games, a social dilemma and a coordination race, where directed influence between selected agent pairs is zero by construction, over 100 seeds. Omitting one precondition, exclusion of the non-stationary training transient, gives false-positive rates of 100.00% and 99.95%: agents annealing exploration independently, in runs that never met, are flagged as influencing one another. Excluding the transient reaches 3.05% in the social dilemma but 11.8% in the coordination game, so the non-stationarity must be treated and exclusion is neither the only way nor sufficient. What we recommend instead changes the null model. Series, statistic and estimand are left untouched: permuting the source within blocks of training time reaches 5.25% and 5.50%, the only one of the three at or below the size of the test in both games. The construction is not new, being a restricted permutation local to blocks of training time; the measurement is. A third null control, four learners in the social dilemma, defeats all three, and there conditioning costs three times what ours does, 15.20 points against 4.55. Titrating injected links separates it from conditioning on identical pairs, 82.5% against 58.0%, while the ablated test reports a positive with or without a link, so its apparent sensitivity is uninformative. Five block counts spanning 16 to 256 land in the nominal region. Code and data are released.

cs.AI↗

arXiv2026

The Coordination Gap: Multi-Agent Alternation Metrics for Temporal Fairness in Repeated Games

Repeated multi-agent interactions require evaluation metrics that capture not only payoff distributions but also their temporal organization. Conventional outcome-based fairness measures can assign similar aggregate scores to temporally distinct coordination patterns, obscuring whether access to a shared resource is genuinely rotating or persistently monopolized. We study this problem in the Honey-Jar Game (HJG), a minimally dynamic repeated threshold-congestion Markov game in which n agents compete for exclusive access to a single high-reward resource. We introduce Perfect Alternation (PA), a reference turn-taking regime corresponding to the n-periodic round-robin picking sequence, together with six novel Alternation (ALT) metrics and a benchmarking methodology mapping ALT values to interpretable PA-equivalent performance. Using Q-learning agents as a minimal adaptive baseline against analytically derived random-policy baselines, we uncover a clear measurement failure: despite deceptively high traditional metrics (e.g., reward fairness often exceeding 0.9), learned policies perform worse than random on every ALT metric, by 34-74% on CALT and up to 92% on EALT, with PA-equivalent coordination falling to roughly one-fifth of the population at n=10. EALT further reveals two distinct failure patterns: without episodic memory (Type-A), the deficit grows from -20% at n=2 to -92% at n=10; with episodic memory (Type-B), it reaches a trough of -76% at n=5 before partially recovering to -10% at n=10. Conventional efficiency and fairness metrics do not reveal these differences. The ALT framework complements the temporal fair division and picking-sequence literature by diagnosing whether temporal fairness emerges spontaneously in decentralized adaptive systems rather than how to enforce it.

cs.MA↗

arXiv2026

Temporal Fair Division in Multi-Agent Systems: From Precise Alternation Metrics to Scalable Coordination Proxies

Many intelligent computing and autonomous systems rely on multiple independent, often learning, agents repeatedly sharing a limited resource. Examples include autonomous robots accessing a shared workstation, wireless devices competing for communication opportunities, and distributed AI agents coordinating access to shared computational resources. While conventional fairness measures assess whether resources are shared equally overall, they cannot distinguish orderly turn-taking from irregular access patterns that produce long and unpredictable waiting times despite similar cumulative outcomes. We introduce Rotational Periodicity (RP), a computationally efficient metric that evaluates both the regularity of waiting times between successful accesses and the balance of access frequencies across agents. We evaluate RP alongside a family of more detailed alternation metrics using a repeated threshold-congestion game in which two to ten reinforcement-learning agents compete for exclusive access to a shared resource. Our experiments reveal that independently trained agents often coordinate substantially worse than random-policy agents, even though conventional fairness metrics consistently report highly favourable outcomes. At the same time, RP closely reproduces the rankings of the more computationally expensive alternation metrics while computing twelve to twenty-five times faster as the number of agents increases. These findings show that evaluating multi-agent learning systems requires temporally aware measures of coordination, not only aggregate outcomes, and that efficient proxy metrics such as RP make this type of evaluation practical for larger intelligent computing systems.

cs.MA↗