Kavli Affiliate: Feng Wang
| First 5 Authors: Yunxuan Ma, Yide Bian, Hao Xu, Weitao Yang, Jingshu Zhao
| Summary:
Market equilibrium is one of the most fundamental solution concepts in
economics and social optimization analysis. Existing works on market
equilibrium computation primarily focus on settings with relatively few buyers.
Motivated by this, our paper investigates the computation of market equilibrium
in scenarios with a large-scale buyer population, where buyers and goods are
represented by their contexts. Building on this realistic and generalized
contextual market model, we introduce MarketFCNet, a deep learning-based method
for approximating market equilibrium. We start by parameterizing the allocation
of each good to each buyer using a neural network, which depends solely on the
context of the buyer and the good. Next, we propose an efficient method to
unbiasedly estimate the loss function of the training algorithm, enabling us to
optimize the network parameters through gradient. To evaluate the approximated
solution, we propose a metric called Nash Gap, which quantifies the deviation
of the given allocation and price pair from the market equilibrium.
Experimental results indicate that MarketFCNet delivers competitive performance
and significantly lower running times compared to existing methods as the
market scale expands, demonstrating the potential of deep learning-based
methods to accelerate the approximation of large-scale contextual market
equilibrium.
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