Multiagent System Simulations of Signal Averaging in English Auctions with Two-Dimensional Value Signals

This study uses a multiagent system to investigate English auctions with two-dimensional value signals and agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal.