NYU LogoRiskEcon® Lab for Decision Metrics

Courant Institute for Mathematical Sciences

 

   Tel: (212) 998-3264

   Email: riskeconlab at cims.nyu.edu

   Address: 60 Fifth Avenue, 3rd Floor, New York, N.Y. 10011


In order to facilitate the development of software as a service, analytics tools, and semantic libraries that employ high-dimensional datasets to integrate conventional data with web-enabled demographic, biometric, psychometric and socio-metric data from innovative sources, Risk Economics has established RiskEcon® Lab for Decision Metrics at New York University’s Courant Institute, an independent division of NYU, widely considered to be one of the world’s leading mathematics educational and scientific research centers, and ranked first in applied mathematical research.

RiskEcon® Lab applies a range of computational methods to analyze commercial, consumer and population-related societal trends. Recent events demonstrate that many large-scale geopolitical and socioeconomic questions are particularly related to the implications and effects of interrelated changes in demographics, technology adoption, and lifestyle choices on the economy. Understanding these patterns is crucial for decision-making in both industry and government. The most critical are the emerging effects of changes in technology and consumer behavior on finance, labor, and housing, and on trends in income and wealth distribution, immigration, aging, health and the environment.

RiskEcon® Lab primarily focuses upon research and development by employing applied computational statistics in the context of robust and scalable data analytic solutions, integrating web-enabled crowdsourcing with machine learning, data-mining, and text-mining, in order to provide solutions and answer large-scale, real world questions with three fundamental objectives:

• Foster, promote, and coordinate public-private-academic research partnerships
• Sponsor, fund, organize and manage “big data” libraries
• Advance NYU’s competency within applied computational statistics

RiskEcon® Lab’s primary role is to enable, facilitate and coordinate academic research in the development of commercially-viable, analytic applications employing computational statistical tools in conjunction with innovative and non-traditional data structures. In addition, other activities of RiskEcon® Lab involve the advancement of the fields of applied mathematical statistics and computational economics, via interdisciplinary post-doctoral, postgraduate, graduate research and education in data sciences and social computing.

RiskEcon® Lab is the cornerstone of the Computational Economics and Algorithmic Data Analytics (CEcADA) cooperative at New York University, established concurrently in 2011.

Press releases on the establishment of RiskEcon® Lab:

NYU Press Release: August 18th, 2011

Courant Newsletter Article, Fall 2011 Issue

 

Selected Research Themes and Workstreams:  

  • Adaptive learning processes, activity-recognition and social computing applications of statistical inference and stochastic control mechanisms to agent-based cyber-physical networks.
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  • Applications of generative agent-based modeling, spatio-temporal mapping and social computing to forensic, geopolitical, socioeconomic, psychometric, sociometric, demographic, syndromic, and environmental surveillance, inference and analytics.
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  • Market-consistent enterprise risk and liability management applications of scalable, robust cyber-physical adaptive learning and pervasive, embedded computing systems. 
  • Institutional and industry configuration, market microstructure, and commercial process engineering applications of computational linguistics, law and economics.
  • General methodological interests and experience include: applications of high-dimensional computational and graphical statistics to Bayesian experimental design, simulation and statistical inference; applied principal components and dimension reduction methods; applied information geometry, graphical statistical modeling and network analysis; applied functional data analysis, nonparametric hierarchical mixture and kernel models; latent variable analysis; applied generalized linear and logistic regression models and discrete choice methods, algorithmic natural and social computing frameworks; robust market-based predictive estimation, pricing and valuation applications of auctions and parimutuel exchange mechanisms.  
  • The socioeconomic and socio-demographic determinants of communicable diseases and non-communicable diseases (NCDs), and their impact on health capital in the labor market via the dependency ratio; structural changes in labor force growth relating to emerging health trends; time-series properties of socioeconomic and socio-demographic risk factors relating to obesity; the relationship of cross-country differentials in the dependency ratio and relative growth.
  • Income elasticity of consumption spending on healthcare goods and service; co-evolution of healthcare spending and selected socioeconomic and socio-demographic indicators; regional and socio-demographic variation of income elasticity in healthcare spending.
  • Effects of population aging on aggregate consumer demand; structural changes in the composition of consumption spending patterns across generations.
  • Methodological interests: time series and longitudinal applications to event history and duration analysis; surveillance and monitoring of NCDs; discrete choice, latent variable and social network analysis of behavioral (sociometric and psychometric) influences on discretionary consumption decisions related to health.