In Press, 2013
Wang, Mengxue, et al.Quantitative Modeling of Operational Risk”. Society of Actuaries Risk Management Newsletter 28 (In Press, 2013). Print.
III, Raymond T. Perkins, and Warren B. Powell. “Stochastic Optimization with Parametric Cost Function Approximations”. (Forthcoming). Web. Publisher's VersionAbstract

A widely used heuristic for solving stochastic optimization problems is to use a deterministic rolling horizon procedure, which has been modified to handle uncertainty (e.g. buffer stocks, schedule slack). This approach has been criticized for its use of a deterministic approximation of a stochastic problem, which is the major motivation for stochastic programming. We recast this debate by identifying both deterministic and stochastic approaches as policies for solving a stochastic base model, which may be a simulator or the real world. Stochastic lookahead models (stochastic programming) require a range of approximations to keep the problem tractable. By contrast, so-called deterministic models are actually parametrically modified cost function approximations which use parametric adjustments to the objective function and/or the constraints. These parameters are then optimized in a stochastic base model which does not require making any of the types of simplifications required by stochastic programming. We formalize this strategy and describe a gradient-based stochastic search strategy to optimize the parameters.

PDF icon 1703.04644.pdf
III, Raymond T. Perkins, and Michael Li. “Polling and big data in the age of Trump, Brexit, and the Colombian Referendum”. Data Driven Journalism 2017. Web. Publisher's VersionAbstract

Given the abundance of personal data, it is natural to expect clairvoyant-like accuracy from modern election models. However, recent elections predictions, regard the British Referendum of the EU, the 2016 US Presidential Election, and the 2016 Colombian Referendum, have tragically failed expectations. This article addresses the cause of these failures.

Perkins, Raymond, and Michael Li. “Better Questions to ask your Data Scientists”. The Harvard Business Review 2016. Web. Publisher's VersionAbstract

One particular challenge that many of these individuals face is how to request new data or analytics from data scientists. While it’s impossible to give an exhaustive account, here are some important factors to think about when communicating with data scientists, particularly as you begin a data search.

III, Raymond T. Perkins, and Michael Li. “Interacting with Data Scientist”. HBR Guide to Data Analytics Basics for Managers. 1st ed. Boston: The Harvard Business Review, 2016. 39-46. Print.Abstract

The intersection between big data and business is growing daily. Business managers often leverage analytics to understand the effectiveness of new products, get a holistic view of customer interactions, identify sources of operational efficiencies, target marketing campaigns, and answer other critical business questions. Although enterprises have been doing analytics for decades, data science is a relatively new capability. Sometimes, conflicts emerge when reconciling traditional business culture and and the new data science culture.  While business is driven by the bottom line, data scientists, analysts, and engineers are more concerned about statistical soundness and distributed computing. Non-technical business managers face particular challenges when requesting new data or analytics from data scientists. While it’s impossible to give an exhaustive account, here are some important factors to think about when communicating with data scientists.

III, Raymond T. Perkins, and Michael Li. “The perils of polling in a Brexit and Donald Trump world”. 2016. Web. Publisher's VersionAbstract

In a digital era of Big Data -- polling is ironically more fraught.  The reasons offer a microcosm of both the promise and perils of Big Data.