The challenge of non-zero-sum stochastic games

For a broad definition of time-discrete stochastic games, their zero-sum varieties have values. But the existence of ϵ -equilibrium for the corresponding non-zero-sum games has proven elusive. We present the problems associated with ϵ -equilibria in non-zero-sum stochastic games, from both the perspectives of proving existence and demonstrating a counter-example

Absorption paths and equilibria in quitting games

We study quitting games and introduce an alternative notion of strategy profiles—absorption paths. An absorption path is parametrized by the total probability of absorption in past play rather than by time, and it accommodates both discrete-time aspects and continuous-time aspects. We then define the concept of sequentially 0-perfect absorption paths, which are shown to be limits of ε-equilibrium strategy profiles as ε goes to 0. We establish that all quitting games that do not have simple equilibria (that is, an equilibrium where the game terminates in the first period or one where the game never terminates) have a sequentially 0-perfect absorption path. Finally, we prove the existence of sequentially 0-perfect absorption paths in a new class of quitting games

Modeling Attrition in Organizations from Email Communication

Abstract—Modeling people’s online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacy-preserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people’s online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65 % on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging. I