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  1. Market simulation is an increasingly important method for evaluating and training trading strategies and testing "what if" scenarios. The extent to which results from these simulations can be trusted depends on how realistic the environment is for the strategies being tested. As a step towards providing benchmarks for realistic simulated markets, we enumerate measurable stylized facts of limit order book (LOB) markets across multiple asset classes from the literature. We apply these metrics to data from real markets and compare the results to data originating from simulated markets. We illustrate their use in five different simulated market configurations: The first (market replay) is frequently used in practice to evaluate trading strategies; the other four are interactive agent based simulation (IABS) configurations which combine zero intelligence agents, and agents with limited strategic behavior. These simulated agents rely on an internal "oracle" that provides a fundamental value for the asset. In traditional IABS methods the fundamental originates from a mean reverting random walk. We show that markets exhibit more realistic behavior when the fundamental arises from historical market data. We further experimentally illustrate the effectiveness of IABS techniques as opposed to market replay. 
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  3. Autonomous robots frequently rely on models of their sensing and actions for intelligent decision making. Unfortunately, in complex environments, robots are bound to encounter situations in which their models do not accurately represent the world. Furthermore, these context-dependent model inaccuracies may be subtle, such that multiple observations may be necessary to distinguish them from noise. This paper formalizes the problem of detection and correction of such subtle contextual model inaccuracies in autonomous robots, and presents an algorithm to address this problem. The solution relies on reasoning about these contextual inaccuracies as parametric regions of inaccurate modeling (RIMs) in the robot’s planning space. Empirical results from various real robot domains demonstrate that, by explicitly searching for RIMs, robots are capable of efficiently detecting subtle contextual model inaccuracies, which in turn can lead to task performance improvement.

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