STRATEGIC AND SOCIAL PREPLAY COMMUNICATION IN THE ULTIMATUM GAME

Pre-play face-to-face communication is known to facilitate cooperation. Various explanations exist for this effect, varying in their dependence on the strategic content of the communication. Previous studies have found similar communication effects regardless of whether strategic communication is available. These results were so far taken to support a social-preferences based explanation of the communication effects. The current experiment provides a replication and extension of previous results to show that different processes come into play, depending on the communication protocol. Specically, pre-play communication in an ultimatum game was either restricted to nongame- related content or unrestricted. The results show that strategic, but not social, communication affects responders' strategies. Thus, the existing results are cast in a new light. I conclude that pre-play communication effects may be mediated by qualitatively dierent processes, depending on the social context.

Arcs on Determinantal Varieties

We study arc spaces and jet schemes of generic determinantal varieties. Using the natural group action, we decompose the arc spaces into orbits, and analyze their structure. This allows us to compute the number of irreducible components of jet schemes, log canonical thresholds, and topological zeta functions.Comment: 27 pages. This is part of the author's PhD thesis at the University of Illinois at Chicago. v2: Minor changes. To appear in Transactions of the American Mathematical Societ

The Structured Weighted Violations Perceptron Algorithm

We present the Structured Weighted Violations Perceptron (SWVP) algorithm, a new structured prediction algorithm that generalizes the Collins Structured Perceptron (CSP). Unlike CSP, the update rule of SWVP explicitly exploits the internal structure of the predicted labels. We prove the convergence of SWVP for linearly separable training sets, provide mistake and generalization bounds, and show that in the general case these bounds are tighter than those of the CSP special case. In synthetic data experiments with data drawn from an HMM, various variants of SWVP substantially outperform its CSP special case. SWVP also provides encouraging initial dependency parsing results

Uniform Chernoff and Dvoretzky-Kiefer-Wolfowitz-type inequalities for Markov chains and related processes

We observe that the technique of Markov contraction can be used to establish measure concentration for a broad class of non-contracting chains. In particular, geometric ergodicity provides a simple and versatile framework. This leads to a short, elementary proof of a general concentration inequality for Markov and hidden Markov chains (HMM), which supercedes some of the known results and easily extends to other processes such as Markov trees. As applications, we give a Dvoretzky-Kiefer-Wolfowitz-type inequality and a uniform Chernoff bound. All of our bounds are dimension-free and hold for countably infinite state spaces