Replicator Dynamics

Follow this topic Ask about this topic

Momentum

0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 9

All topics

Delayed Repression and Emergent Instability in Adaptive Multi-Agent Systems
May 28, 2026Igor ItkinDynamical Systems Replicator Dynamics
Independent Researcher, Tel Aviv, Israel
Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games
May 26, 2026Philipp WissgottReplicator Dynamics
danube.ai solutions gmbh, 1040 Vienna, Austria
The fitness landscape of social norms in social dilemmas
May 13, 2026Maximilian Puelma TouzelSocial Dilemmas Game Theory
Mila - Quebec AI Institute Montréal, Canada
A general classification of the replication dynamics with a unique fixed point in the interior of simplex $S_N$
May 11, 2026Hongju Daisy Chen, Bin Yi, Zhanshan Sam MaDynamical Systems Game Theory
School of Mathematics and Statistics, Guilin University of Technology, Guilin, Guangxi, China · Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin, Guangxi, China · Computational Biology and Medical Ecology Lab Biostatistics and Image Genetics Lab Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China +2
Graph Normalization: Fast Binarizing Dynamics for Differentiable MWIS
May 6, 2026Laurent GuiguesDynamical Systems Replicator Dynamics
Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics
Apr 28, 2026Arnau Vivet, Alex ArenasSoftmax Attention Dynamical Systems
1Departament d’Enginyeria Inform`atica i Matem`atiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain · 2Complexity Science Hub Vienna, Metternichgasse 8, 1030 Vienna, Austria
The Axiom of Consent: Authorization, Friction, and Multi-Agent Coordination
Jan 10, 2026Murad FarzullaMulti-Agent Coordination Replicator Dynamics
Dissensus, London, UK · King’s College London, London, UK
The Replicator-Optimization Mechanism: A Scale-Relative Formalism for Persistence-Conditioned Dynamics with a Conditional Consent-Friction Instantiation
Jan 10, 2026Murad FarzullaDynamical Systems Replicator Dynamics
Dissensus, London, UK · King’s College London, London, UK
Feature weighting for data analysis via evolutionary simulation
Nov 9, 2025Aris Daniilidis, Alberto Domínguez Corella, Philipp WissgottReplicator Dynamics Importance Weighting
Institut für Stochastik und Wirtschaftsmathematik, Variational Analysis, Dynamics and Operations Research Unit E105-04, Technische Universität Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria · Institut für Mathematik und Wissenschaftliches Rechnen, Universität Graz, Heinrichstraße 36, A-8010 Graz, Austria · danube.ai solutions gmbh, 1040 Vienna, Austria

May 28, 2026, cs.MAJ/K move · Enter open · S save

Delayed Repression and Emergent Instability in Adaptive Multi-Agent Systems

Igor Itkin

Independent Researcher, Tel Aviv, Israel

Dynamical Systems Replicator Dynamics Multi-Agent Systems Emergent Behavior in Multi-Agent Systems

Open paper AskPDF arXiv

Regulatory institutions (from content moderation platforms to financial supervisors) observe, deliberate, and intervene only after a characteristic delay. We ask whether this processing lag alone can destabilize a multi-agent system that would otherwise remain stable, without exogenous shocks, coordination among agents, or malicious actors. We study this in two stages. First, we analyze a delayed replicator equation in which autonomous agents benefit from radical behavior but face punishment based on a lagged institutional alarm signal. We derive a closed-form critical delay beyond which the unique interior equilibrium loses stability through a Hopf bifurcation, and prove via center manifold reduction that the bifurcation is supercritical (bounded oscillations, not explosive growth) for the entire sigmoid response family. Second, we embed N=240 agents on a network with reinforcement learning (tabular Q-learning) and cross institutional delay with three decision architectures: fixed-policy, reactive (a memoryless threshold heuristic), and Q-learning. The hierarchy is opposite to the naive expectation that learning amplifies instability. Reactive agents are perfectly stable without delay yet collapse once delay is introduced (96% runaway by delay >= 8); fixed-policy agents are immune (0% at all delays); Q-learning agents are only partially resilient (66% at delay 20). The destabilizing ingredient is reactivity to delayed signals, not learning: agents that immediately exploit low-alarm windows trigger oscillatory feedback loops, while learning buffers this through punishment memory encoded in value functions. Throughout, "runaway" denotes bounded large-amplitude oscillation crossing a radical-fraction threshold, consistent with the supercritical bifurcation, not unbounded growth.

Momentum

Related topics

Latest papers 9