3. Consider a "star" network of K≥ 2 agents. Label Agent 1 as the "central" agent. (a) Suppose that all agents update according to a DeGroot process whereby they equally weight their own and their neighbors' information. Completely characterize the updating matrix A. Note that this is not the same as the adjacency matrix. (Hint: Your answer should state entries as a function of K.) (b) Agents 2,..., K are all similarly-situated in the network. What should be true of their social influence? (c) Using your answer to (b), derive the social influence for each player (Note that this will be a function of K). Comment on how the influence of the central agent changes as K increases.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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3. Consider a "star" network of K≥ 2 agents. Label Agent 1 as the "central" agent.
(a) Suppose that all agents update according to a DeGroot process whereby they equally
weight their own and their neighbors' information. Completely characterize the updating
matrix A. Note that this is not the same as the adjacency matrix. (Hint: Your answer
should state entries as a function of K.)
(b) Agents 2,..., K are all similarly-situated in the network. What should be true of their
social influence?
(c) Using your answer to (b), derive the social influence for each player (Note that this will
be a function of K). Comment on how the influence of the central agent changes as K
increases.
(d) What happens when K = 2? Relate this to the updating matrix A and in particular its
second eigenvalue. Comment on convergence of Bayesian vs. DeGroot processes in this
context.
Transcribed Image Text:3. Consider a "star" network of K≥ 2 agents. Label Agent 1 as the "central" agent. (a) Suppose that all agents update according to a DeGroot process whereby they equally weight their own and their neighbors' information. Completely characterize the updating matrix A. Note that this is not the same as the adjacency matrix. (Hint: Your answer should state entries as a function of K.) (b) Agents 2,..., K are all similarly-situated in the network. What should be true of their social influence? (c) Using your answer to (b), derive the social influence for each player (Note that this will be a function of K). Comment on how the influence of the central agent changes as K increases. (d) What happens when K = 2? Relate this to the updating matrix A and in particular its second eigenvalue. Comment on convergence of Bayesian vs. DeGroot processes in this context.
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