1. Suppose that we have the following updating matrix for a 4-person network. [0.3 0 0.4...

 

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1. Suppose that we have the following "updating" matrix for a 4-person network. [0.3 0 0.4 0.3 1 0 0 0 0 0.50 0.5 0 00 1 A= (a) Labeling nodes 1-4, represent this updating matrix with a drawing of a directed network. Note that you will need to include self-links. (b) Which agent puts the least amount of weight on her own prior beliefs? Which one puts the most? For the one with the most, what happens to her beliefs over time? (c) Assuming this network reaches consensus (which is not guaranteed), what will its con- sensus belief be as to? (d) Discuss your answer in (c) in terms of "naive" learning. What is "naive" about this process? How would fully-Bayesian learning update in a more sophisticated way? 1. Suppose that we have the following "updating" matrix for a 4-person network. [0.3 0 0.4 0.3 1 0 0 0 0 0.50 0.5 0 00 1 A= (a) Labeling nodes 1-4, represent this updating matrix with a drawing of a directed network. Note that you will need to include self-links. (b) Which agent puts the least amount of weight on her own prior beliefs? Which one puts the most? For the one with the most, what happens to her beliefs over time? (c) Assuming this network reaches consensus (which is not guaranteed), what will its con- sensus belief be as to? (d) Discuss your answer in (c) in terms of "naive" learning. What is "naive" about this process? How would fully-Bayesian learning update in a more sophisticated way?

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ANSWER a The updating mat... View the full answer