Efficient Bayesian Learning in Social Networks with Gaussian Estimators

Details Efficient Bayesian Learning in Social Networks with Gaussian Estimators Efficient Bayesian Learning in Social Networks with Gaussian Estimators

2010-02-03

arxiv.org/abs/1002.0747v2

Statistics

Applications

10 Pages

Scientific paper

We propose a Bayesian model of iterative learning on social networks that is computationally tractable; the agents of this model are fully rational, and their calculations can be performed with modest computational resources for large networks. Furthermore, learning is efficient, in the sense that the process results in an information-theoretically optimal belief. This result extends Condorcet's Jury Theorem to general social networks, preserving rationality, computational feasibility and efficient learning. The model consists of a group of agents who belong to a social network, so that a pair of agents can observe each other's actions only if they are neighbors. We assume that the network is connected and that the agents have full knowledge of the structure of the network, so that they know the members of the network and their social connections. The agents try to estimate some state of the world S (say, the price of oil a year from today). Each agent has a private measurement: an independently acquired piece of information regarding S. This is modeled, for agent v, by a number S_v picked from a Gaussian distribution with mean S and standard deviation one. Accordingly, agent v's prior belief regarding S is a normal distribution with mean S_v and standard deviation one. The agents start acting iteratively. At each iteration, each agent takes the optimal action given its current belief. This action reveals its mean estimate of S to its neighbors. Then, observing its neighbors' actions, each agent updates its belief, using Bayes' Law. We show that this process is efficient: all the agents converge to the belief that they would have, had they access to all the private measurements. Additionally, and in contrast to other iterative Bayesian models on networks, it is computationally efficient, so that each agent's calculation can be easily carried out.

Affiliated with

Also associated with

No associations

Rating

Efficient Bayesian Learning in Social Networks with Gaussian Estimators does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Efficient Bayesian Learning in Social Networks with Gaussian Estimators, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficient Bayesian Learning in Social Networks with Gaussian Estimators will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-602180