From Consensus to Social Learning Ali Jadbabaie Department of Electrical and Systems Engineering and...
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Transcript of From Consensus to Social Learning Ali Jadbabaie Department of Electrical and Systems Engineering and...
![Page 1: From Consensus to Social Learning Ali Jadbabaie Department of Electrical and Systems Engineering and GRASP Laboratory Alvaro Sandroni Penn Econ. and Kellogg.](https://reader035.fdocuments.in/reader035/viewer/2022062715/56649d7a5503460f94a5e44b/html5/thumbnails/1.jpg)
From Consensus to Social Learning
Ali Jadbabaie
Department of Electrical and Systems Engineeringand GRASP Laboratory
Alvaro Sandroni
Penn Econ. and Kellogg School of Management, Northwestern University
Block Island Workshop on Swarming, June 2009
With Alireza Tahbaz-Salehi and Victor Preciado
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Emergence of Consensus, Emergence of Consensus, synchronization, flockingsynchronization, flocking
Opinion dynamics, crowd control, synchronization and flocking
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Flocking and opinion dynamicsFlocking and opinion dynamics
Bounded confidence opinion model (Krause, 2000)Nodes update their opinions as a weighted average
of the opinion value of their friends
Friends are those whose opinion is already close
When will there be fragmentation and when will there be convergence of opinions?
Dynamics changes topology
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Conditions for reaching consensusConditions for reaching consensus
Theorem (Jadbabaie et al. 2003, Tsitsiklis’84): If there is a sequence of bounded, non-overlapping time intervals Tk, such that over any interval of length Tk, the network of agents is “jointly connected ”, then all agents will reach consensus on their velocity vectors.
Convergence time (Olshevsky, Tsitskilis) : T(eps)=O(n3log n/eps)
Similar result when network changes randomly.
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Random NetworksRandom Networks
The graphs could be correlated so long as they are stationary-ergodic.
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Variance of consensus value for ER graphsVariance of consensus value for ER graphsNew results for finite random graphs: Explicit expression for the variance of x*. The variance is a function of c, n and the initial conditions x(0) (although the explicit expression is messy)
Plots of Var(x*) for initial conditions uniformly distributed in [0,1]
The average weight matrix is symmetric!!
p
Var(x*)n=3 n=6 n=9 n=12 n=15
2
12
1
2
1
,)(
,
*)(
n
kk
jiji
n
kk
xnnpnnn
xxnpx
xVar
where r(p,n) is a non-trivial (although closed-form) function that goes to 1 as n goes to infinity
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Consensus and Naïve Social learning Consensus and Naïve Social learning
When is consensus a good thing?Need to make sure update converges to the correct value
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Naïve vs. Rational Decision MakingNaïve vs. Rational Decision Making
Just average!
Fuse info with Bayes Rule
Naïve learning
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Social learningSocial learning
There is a true state of the world, among countably many
We start from a prior distribution, would like to update the distribution (or belief on the true state) with more observations
Ideally we use Bayes rule to do the information aggregation
Works well when there is one agent (Blackwell, Dubin’1963), become impossible when more than 2!
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Locally Rational, Globally Naïve: Locally Rational, Globally Naïve: Bayesian learning under peer pressureBayesian learning under peer pressure
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Model DescriptionModel Description
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Model DescriptionModel Description
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Belief Update RuleBelief Update Rule
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Why this update?Why this update?
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Eventually correct forecastsEventually correct forecasts
Eventually-correct estimation of the output!
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Why strong connectivity?Why strong connectivity?
No convergence if different people interpret signals differently
N is misled by listening to the less informed agent B
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ExampleExample
One can actually learn from others
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Convergence of beliefs and Convergence of beliefs and consensus on correct value!consensus on correct value!
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Learning from othersLearning from others
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SummarySummary