Refunctionalizing a Frayed American China-Taiwan Policy: Incrementalism or Paradigmatic Shift?

This paper is interested in exploring whether it is possible for the US. to pursue parallel relationships with Taiwan and China, that is, whether US.-Taiwan relations can be decoupled from the Washington-Beijing relationship? This paper uses a spatial model to review how US-Taiwan relations have evolved since 1949, when the reality of two Chinas set in with the founding of the PRC. It discusses the increasingly unbalanced dual track framework of current US. policy toward China and Taiwan and contrasts the changing contexts between the SCP\u27s time and the present post-Cold War era. It examines those most important new parameters that were absent or different in the SCP. Based on this contrast, the paper questions the policy\u27s continued validity and calls for a new paradigm to replace the SCP. Based on these new parameters, the final section sketches out a new paradigm for US. policy toward Taiwan in the post-Cold War era and weighs the pros and cons of three distinct policy choices - disengagement, decoupling, and improved status quo-for the shape and direction of future US.-Taiwan relations. By bringing developments up to date (President Bush\u27s December 9, 2003 comments regarding Taiwan\u27s referendum), this paper argues that although the Bush Administration seems content to refunctionalize a frayed framework, it has abandoned strategic ambiguity and has added preference for the status quo

Online Regularization for High-Dimensional Dynamic Pricing Algorithms

We propose a novel \textit{online regularization} scheme for revenue-maximization in high-dimensional dynamic pricing algorithms. The online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (\texttt{OORMLP}) algorithm with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity over all decision points; envelop prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design safer and more robust dynamic pricing algorithms in practice. In theory, the proposed \texttt{OORMLP} algorithm exploits the sparsity structure of high-dimensional models and obtains a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online LASSO procedure that resolves dynamic pricing problems at the \textit{process} level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate \texttt{OORMLP} in different synthetic pricing problem settings and observe that \texttt{OORMLP} performs better than \texttt{RMLP} proposed in \cite{javanmard2019dynamic}