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The paper by Alexander Frankel and Emir Kamenica, featured in the American Economic Review in 2019, is exceptionally remarkable. Their work, titled Quantifying Information and Uncertainty, delves into the intricate dynamics of information and belief systems. Reference: Quantifying Information and Uncertainty (2019) by Alexander Frankel and Emir Kamenica. Published in the American Economic Review, Volume 109, Issue 10, pages 3650–3680. DOI: 10.1257/aer.20181897. Suppose we observe some pieces of news. How might we quantify the amount of information contained in it? Another related question, how might we quantify the uncertainty of a belief? One desideratum might be that the measure of information/uncertainty should correspond to the instrumental value/loss associated with some decision problem. ...

A review of Waldfogel’s 1993 AER paper “The Deadweight Loss of Christmas”.

the unexpected value in gift-giving, beyong deadweight loss

Aggarwal (2019)’s WINE paper proposed a LP-based framework for modelling autobidder for value maximizers. But it does not capture value maximizing bidders with budget and ROI constraints.

Regarding ADIP, a first-order method for solving LP, the following is an excerpt from Deng et al.’s arxiv page: The ADMM-based interior point method (ABIP, Lin et al. 2021) is a hybrid algorithm which effectively combines the iterior point method and the first-order method to achieve performance boost in large-scale linear programming. Different from the standard interior point method which relies on a costly Newton step, ABIP applies the alternating direction method of multipliers (ADMM) to approximately solve the barrier penalized problem. In this paper, we provide a new version of ABIP with several improvements. First, we develop some new implementation strategies to accelerate ABIP’s performance for linear programming. Next, we extend ABIP to solving the more general linear conic programming and establish the associated iteration complexity of the algorithm. Finally, we conduct extensive numerical experiments in both synthetic and real-world datasets to demonstrate the empirical advantage of our developments. In particular, the enhanced ABIP achieves a 5.8x reduction in the geometric mean of run time on 105 LP instances from Netlib and it compares favorably against state-of-the-art open-source solvers in a wide range of large-scale problems. Moreover, it is even comparable to the commercial solvers in some particular datasets. ...