Network-Induced Strategic Communication in Opinion Dynamics

What the math gives to ML

The paper provides a constructive mechanism by which network topology changes communication: each sender is reduced to a scalar strategic communication problem with a network-induced exaggeration factor, and weak audience influence produces stronger exaggeration. Two concrete signaling regimes are especially transferable: naive decoding yields a clipped amplified signal, while Bayesian decoding yields a piecewise-constant interval quantizer whose number of credible bins decreases as exaggeration grows. This suggests an adaptive graph-neural-network communication layer that allocates continuous messages to strongly influential edges and automatically collapses weakly influential channels to low-bit or binary signals, with measurable predictions for saturation, message entropy, and clustering.