Neural voting machines.

Neural Networks(2006)

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摘要
A "Winner-take-all" network is a computational mechanism for picking an alternative with the largest excitatory input. This choice is far from optimal when there is uncertainty in the strength of the inputs, and when information is available about how alternatives may be related. For some time, the Social Choice community has recognized that many other procedures will yield more robust winners. The Borda Count and the pair-wise Condorcet tally are among the most favored. If biological systems strive to optimize information aggregation, then it is of interest to examine the complexity of networks that implement these procedures. We offer two biologically feasible implementations that are relatively simple modifications of classical recurrent networks.
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关键词
information aggregation,computational mechanism,neuroeconomics,borda count,winner-take-all,social networks,decision-making,social choice community,neural voting machine,robust winner,pair-wise condorcet tally,classical recurrent network,biological system,biologically feasible implementation,condorcet voting,domain knowledge,largest excitatory input,recurrent neural network,maximum likelihood,winner take all,neural network,social network,ai
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