Multiattribute Auctions Based on Generalized Additive Independence
This addresses the problem of inefficient auctions for traders with interdependent attribute preferences, though it appears incremental as it extends existing auction theory with more expressive preference modeling.
The authors tackled the problem of multiattribute auctions with non-additive trader preferences by developing an auction mechanism based on generalized additive independence (GAI) structures, which approximates optimal welfare with a small additive factor and shows significant efficiency improvements in simulations.
We develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. We propose an iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreases elicitation and computational burden, and creates an open competition among suppliers over a multidimensional domain. Most significantly, the auction is guaranteed to achieve surplus which approximates optimal welfare up to a small additive factor, under reasonable equilibrium strategies of traders. The main departure of GAI auctions from previous literature is to accommodate non-additive trader preferences, hence allowing traders to condition their evaluation of specific attributes on the value of other attributes. At the same time, the GAI structure supports a compact representation of prices, enabling a tractable auction process. We perform a simulation study, demonstrating and quantifying the significant efficiency advantage of more expressive preference modeling. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the two representations. We find that allowing traders to express existing dependencies among attributes improves the economic efficiency of multiattribute auctions.