Giacomo Como

Learn More
We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves a inhomogeneous stochastic gossip process of continuous opinion dynamics in a society consisting of two types of agents: regular agents, who update their beliefs according to information that they receive from their(More)
Motivated by distributed sensor networks scenarios, we consider a problem of state estimation under communication constraints, in which a real-valued random vector needs to be reliably transmitted through a digital noisy channel. Estimations are sequentially updated by the receiver, as more and more channel outputs are observed. Assuming that no channel(More)
This paper investigates the throughput behavior of single-commodity dynamical flow networks governed by monotone distributed routing policies. The networks are modeled as systems of ordinary differential equations based on mass conversation laws on directed graphs with limited flow capacities on the links and constant external inflows at certain origin(More)
A class of large-scale stochastic discrete-time continuous-opinion dynamical systems is analyzed. Agents have pairwise random interactions in which their vector-valued opinions are updated to a weighted average of their current values. The intensity of the interactions is allowed to depend on the agents' opinions themselves through an interaction kernel.(More)
We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves an inhomogeneous stochastic gossip process of continuous opinion dynamics in a society consisting of two types of agents: regular agents, who update their beliefs according to information that they receive from their(More)
Information-theoretic lower bounds on the estimation error are derived for problems of distributed computation. These bounds hold for a network attempting to compute a real-vector-valued function of the global information, when the nodes have access to partial information and can communicate through noisy transmission channels. The presented bounds are(More)
A lower bound bound is established on the error probability of fixed-length block-coding systems with finite memory feedback, which can be described in terms of a time dependent finite state machine. It is shown that the reliability function of such coding systems over discrete memoryless channels is upper-bounded by the sphere-packing exponent.
Robustness of distributed routing policies is studied for dynamical networks, with respect to adversarial disturbances that reduce the link flow capacities. A dynamical network is modeled as a system of ordinary differential equations derived from mass conservation laws on a directed acyclic graph with a single origin-destination pair and a constant total(More)
The error exponent of Markov channels with feedback is studied in the variable-length block-coding setting. Burnashev's classic result is extended to finite-state ergodic Markov channels. For these channels, a single-letter characterization of the reliability function is presented, under the assumption of full causal output feedback, and full causal(More)