Please replace the paragraph immediately before equation (7) with: "Actions are drawn from a reduced strategy space (RSS) of dimension r = 2 m+1 . Each action is associated with a strategy k and a history µ(t). There are 2 m histories. Together, all actions by history and strategy (converted for convenience from {O,1}) can be represented in table form as a dim(RSS)×2 m binary matrix (i.e., a µt k ∈ {−1, 1}). In this case, the table reads:"

Abstract:In the Minority, Majority and Dollar Games (MG, MAJG, $G) synthetic agents compete for rewards, at each time-step acting in accord with the previously best-performing of their limited sets of strategies. Different components and/or aspects of real-world financial markets are modelled by these games. In the MG, agents compete for scarce resources; in the MAJG agents imitate the group in the hope of exploiting a trend; in the $G agents attempt to successfully predict and benefit from trends as well as changes in the direction of a market. It has been previously shown that in the MG for a reasonable number of preliminary time steps preceding equilibrium (Time Horizon MG, THMG), agents' attempt to optimize their gains by active strategy selection is "illusory": The calculated hypothetical gains of their individual strategies is greater on average than agents' actual average gains. Furthermore, if a small proportion of agents deliberately choose and act in accord with their seemingly worst performing strategy, these outperform all other agents on average, and even attain mean positive gain, otherwise rare for agents in the MG. This latter phenomenon raises the question as to how well the optimization procedure works in the MAJG and $G. We demonstrate that the illusion of control is absent in MAJG and $G. In other words, lowentropy (more informative) strategies under-perform high-entropy (or random) strategies in the MG but outperform them in the MAJG and $G. This provides further clarification of the kinds of situations subject to genuine control, and those not, in set-ups a priori defined to emphasize the importance of optimization.PACS. 89.75.-k Complex systems -89.65Gh Economics; econophysics, financial markets, business and management -02.50.Le Decision theory and game theory

The STAR-RICH detector extends the particle identification capabilities of the STAR experiment for charged hadrons at mid-rapidity. This detector represents the first use of a proximity-focusing CsI-based RICH detector in a collider experiment. It provides identification of pions and kaons up to 3 GeV/c and protons up to 5 GeV/c. The characteristics and performance of the device in the inaugural RHIC run are described.

Please replace the paragraph immediately before equation (7) with: "Actions are drawn from a reduced strategy space (RSS) of dimension r = 2 m+1 . Each action is associated with a strategy k and a history µ(t). There are 2 m histories. Together, all actions by history and strategy (converted for convenience from {O,1}) can be represented in table form as a dim(RSS)×2 m binary matrix (i.e., a µt k ∈ {−1, 1}). In this case, the table reads:"

Using virtual stock markets with artificial interacting software investors, aka agent-based models, we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the actual data and that generated by the reconstructed virtual stock market, we obtain parameters and strategies, which reveal some of the inner workings of the target stock market. We validate our approach by out-of-sample predictions of directional moves of the Nasdaq Composite Index.