Raoufi, MohsenHamann, HeikoRomanczuk, Pawel2025-11-042025-11-042025-10-012025-10-28http://edoc.hu-berlin.de/18452/35743Collective estimation is a variant of collective decision-making where agents reach consensus on a continuous quantity through social interactions. Achieving precise consensus is complex due to the co-evolution of opinions and the interaction network. While homophilic networks may facilitate estimation in well-connected systems, disproportionate interactions with like-minded neighbors lead to the emergence of echo chambers and prevent consensus. Our agent-based simulations confirm that, besides limited exposure to attitude-challenging opinions, seeking reaffirming information entrap agents in echo chambers. To overcome this, agents can adopt a stubborn state (Messengers) that carries data and connects clusters by physically transporting their opinion. We propose a generic approach based on a Dichotomous Markov Process, which governs probabilistic switching between behavioral states and generates diverse collective behaviors. We study a continuum between task specialization (no switching), to generalization (slow or rapid switching). Messengers help the collective escape local minima, break echo chambers, and promote consensus.eng(CC BY 4.0) Attribution 4.0 InternationalComputational modelsInformation technologyNetwork topologyarticleurn:nbn:de:kobv:11-110-18452/35743-310.18452/350962731-8753