Dr. Gurrea has more than 20 years of experience in economic consulting and specializes in industrial organization, financial economics, and econometrics.
San Francisco
Stuart Gurrea has more than 20 years of experience in economic consulting. He specializes in industrial organization, financial economics, and econometrics. Dr. Gurrea has conducted economic analyses in the context of legal disputes, policy making, and business strategy, and has served as both a consulting and testifying expert in arbitration and in state and federal courts.
Dr. Gurrea’s work combines theoretical modeling with the application of quantitative techniques to address economic questions, including those arising from breach of contract, asset valuation, securities fraud, labor market discrimination, antitrust, and intellectual property. Many of his assignments involve identifying and quantifying economic damages on an individual or class-wide basis, and evaluating damages claims in light of the principles of economics and finance. Dr. Gurrea has addressed economic issues in various industries, including financial services, energy, airlines, telecommunications, and pharmaceuticals.
While attending Northwestern University, Dr. Gurrea worked as an investment research analyst. He also collaborated in numerous research projects involving the construction of data sets and the estimation of econometric models.
Dr. Stuart Gurrea provided trial testimony that contributed to the United States Naval Academy’s victory in Baltimore Federal District Court upholding the Academy’s consideration of race and ethnicity in its admissions process.
Challenges to Quantifying Economic Harm from Data Breaches
Quantifying economic harm resulting from data breaches is challenging. It requires sound economic analysis to address a range of issues, including issues related to personal data exposure and the valuation of privacy, the use of dark-web data for benchmarking, and the difficulties in assessing mitigation costs for class-action lawsuits.
Big-data analysis has become increasingly important in detecting and litigating major cases of systemic misconduct. Quantitatively analyzing these events is challenging because of the volume of data in addition to the context of litigations, investigations, and regulatory proceedings requiring speed and accuracy but without complete information about the data. In their article “A Primer on […]