Since market scoring rules have become popular as a form of market maker, it seems worth reviewing just what such mechanisms are intended to do. The main function performed by most market makers is to serve as an intermediary between people who prefer to trade at different times. Traders who have the same favorite times to trade can show up together to an ordinary continuous double auction, and then make and accept offers to trade. But when traders have different favorite times, a market maker can help them by first making offers that some of them will accept, and then later making opposite offers which others will accept. By adjusting prices in his favor, a market maker can even profit from providing this service. By making offers, however, a market maker opens himself up to the risk of losing to informed traders who know more than he about asset values. It is a complex and difficult task to choose the price and duration of offers in order to profit the most from intermediary trades while suffering the least from informed trades. This task requires subtle judgments about the relative fraction of informed and intermediary trades at different times, prices, quantities, and trading histories. No simple algorithm could reasonably claim to do this task optimally. Very active markets have little need for market makers, as anyone can trade at anytime. In markets with large but sporadic trades, a human will likely find it profitable to apply their considerable intelligence to the complex task of market making. The question is what to do for smaller less-active markets, which cannot afford such human attention. Trading may simply not happen there if no intermediary can be found to make such markets. A computer program with less than human intelligence that attempts to make markets runs the risk of being out-smarted by human traders. Humans might even figure out how to turn that program into a money pump, giving up cash each time it is run through some cycle of trades..
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