This question adds some complexity to the models we've discussed, in order to help build intuition. (a) Suppose that we run the Chandrasekhar, Larreguy, and Xandri (2018) experiment, but we allow agents to give a continuous signal (between 0 and 1). In the simple example given in class, what must occur under both Bayesian and DeGroot learning? Contrast this with the experiment. (b) Suppose that you are fully Bayesian, and you wish to learn some continuous variable by repeated learning in a network. In a couple sentences, contrast your updating rule in a setting in which all of your neighbors are (1) DeGroot, vs. (2) Bayesian.