In this talk, we consider the value of observation channels in decision making, a problem on which David Blackwell has many contributions. We briefly review our earlier results on some topological properties of experiments/observation channels in decision/control problems. We then consider the problem of stabilization of a linear system driven by unbounded noise, by a decision maker who has access to information from a noisy discrete observation channel. We prove that for sample path ergodicity and asymptotic mean stationarity, it is necessary and sufficient that the capacity of a channel is greater than the sum of the logarithms of the unstable eigenvalues in the system matrix. We provide conditions for finiteness of the limit of the average second moments as well. Stability results also apply to a class of channels with memory with feedback. Our proof utilizes recently studied random-time state-dependent stochastic drift criteria for stabilization of Markov chains.