Non-Bayesian social learning with observation reuse and soft switching
We propose a non-Bayesian social learning update rule for agents in a network, which minimizes the sum of the Kullback-Leibler divergence between the true distribution generating the agents’ local observations and the agents’ beliefs (parameterized by a hypothesis set), and a weighted varentropy-rel...
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格式: | Article |
語言: | English |
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2019
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在線閱讀: | https://hdl.handle.net/10356/102641 http://hdl.handle.net/10220/48151 |
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