Max-Olivier Hongler

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We construct a model of innovation diffusion that incorporates a spatial component into a classical imitation-innovation dynamics first introduced by F. Bass. Relevant for situations where the imitation process explicitly depends on the spatial proximity between agents, the resulting nonlinear field dynamics is exactly solvable. As expected for nonlinear(More)
We study, in the fluid flow framework, the cooperative dynamics of a buffered production line in which the production rate of each work-cell does depend on the content of its adjacent buffers. Such state dependent fluid queueing networks are typical for people based manufacturing systems where human operators adapt their working rates to the observed(More)
We consider the dynamics of a “one queue one server” feedback queueing system where the decision of an agent to use the feedback loop is based upon its waiting time in the system. We investigate the dynamics for very patient agents and quantify the emerging stable and almost deterministic oscillations of the queue length. The resulting delay dynamics are(More)
The mean-field dynamics of a collection of stochastic agents evolving under local and nonlocal interactions in one dimension is studied via analytically solvable models. The nonlocal interactions between agents result from (a) a finite extension of the agents interaction range and (b) a barycentric modulation of the interaction strength. Our modeling(More)
We consider the situation of a homogeneous swarm of agents following a partially observable leader agent. The resulting, slightly heterogeneous swarm of agents is softly controlled by the leader. We study the swarm dynamics using a recently established connection existing between multi-agents dynamics and nonlinear optimal state estimation. For a whole(More)
The inherent complexity characterizing the production and/or service networks strongly favors decentralized and self-organizing mechanisms to regulate the flows of matter and information in circulation. This basic observation motivates us to study the flow dynamics in queueing networks roamed by autonomous agents which, at a given time and at a given vertex(More)