This thesis describes and evaluates a market-making algorithm for setting prices in financial markets with asymmetric information, and analyzes the properties of artificial markets in which the algorithm is used. The core of our algorithm is a technique for maintaining an online probability density estimate of the underlying value of a stock. Previous theoretical work on market-making has led to price-setting equations for which solutions cannot be achieved in practice, whereas empirical work on algorithms for market-making has focused on sets of heuristics and rules that lack theoretical justification. The algorithm presented in this thesis is theoretically justified by results in finance, and at the same time flexible enough to be easily extended by incorporating modules for dealing with considerations like portfolio risk and competition from other market-makers. We analyze the performance of our algorithm experimentally in artificial markets with different parameter settings and find that many reasonable real-world properties emerge. For example, the spread increases in response to uncertainty about the true value of a stock, average spreads tend to be higher in more volatile markets, and market-makers with lower average spreads perform better in environments with multiple competitive market-makers. In addition, the time series data generated by simple markets populated with market-makers using our algorithm replicate properties of real-world financial time series, such as volatility clustering and the fat-tailed nature of return distributions, without the need to specify explicit models for opinion propagation and herd behavior in the trading crowd.