Vision and Influence in Econometrics: John Denis Sargan

Denis Sargan's intellectual influence in econometrics is discussed and some of his visions for the future of econometrics are considered in this memorial article. One of Sargan's favorite topics in econometric theory was finite sample theory, including both exact theory and various types of asymptotic expansions. We provide some summary discussion of asymptotic expansions of the type that Sargan developed in this field and give explicit representations of Sargan's formula for the Edgeworth expansion in the case of an econometric estimator that can be written as a smooth function of sample moments whose distributions themselves have Edgeworth expansions.Academic bodhisattva, asymptotic expansion, bodhicitta, Edgeworth, finite sample theory, intellectual influence, vision

METHODOLOGY FOR MODELING COST AND SCHEDULE RISK ASSOCIATED WITH RESOURCE DECISIONS INVOLVING THE U.S. ARMY'S MODERNIZATION EFFORTS FOR 2035

Prioritization decisions using the Army Modernization and Analysis (AMA)-developed Trade-Space Decision Exploration System (TRADES) does not address programmatic variance related to cost and schedule growth. This study offers an improved methodology for modeling cost risk by employing sound cost estimation principles, distribution fitting, Monte Carlo simulations, and cost/benefit analysis to assist strategic decision makers and the acquisitions community. To that end, this approach follows a five-step methodology that (1) collects and screens cost data from the Cost Assessment Database Enterprise (CADE), (2) determines normalized cost growth factors, (3) identifies and constructs the appropriate distributions for modeling, (4) simulates cost variance among the entire program portfolio, and (5) recommends the necessary contingency cash reserve quantity associated with a decision maker’s confidence level. The result is a credible, repeatable, and effectual cost estimating methodology that promotes commodity-based models for predicting cost growth and measuring risk.Major, United States ArmyApproved for public release. Distribution is unlimited

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

The automated analysis of digital human communication data often focuses on specific aspects such as content or network structure in isolation. This can provide limited perspectives while making cross-methodological analyses, occurring in domains like investigative journalism, difficult. Communication research in psychology and the digital humanities instead stresses the importance of a holistic approach to overcome these limiting factors. In this work, we conduct an extensive survey on the properties of over forty semi-automated communication analysis systems and investigate how they cover concepts described in theoretical communication research. From these investigations, we derive a design space and contribute a conceptual framework based on communication research, technical considerations, and the surveyed approaches. The framework describes the systems' properties, capabilities, and composition through a wide range of criteria organized in the dimensions (1) Data, (2) Processing and Models, (3) Visual Interface, and (4) Knowledge Generation. These criteria enable a formalization of digital communication analysis through visual analytics, which, we argue, is uniquely suited for this task by tackling automation complexity while leveraging domain knowledge. With our framework, we identify shortcomings and research challenges, such as group communication dynamics, trust and privacy considerations, and holistic approaches. Simultaneously, our framework supports the evaluation of systems and promotes the mutual exchange between researchers through a structured common language, laying the foundations for future research on communication analysis.Comment: 11 pages, 2 tables, 1 figur

A decision support system for disaster prevention in Urban Areas

This paper presents the use of Human Behavior Modeling for Disaster Relief and Emergency Management. The authors propose an innovative MS2G (Modeling, Interoperable Simulation and Serious Game) using Intelligent Agents to reproduce a complex scenario used for Verification, Validation and Accreditation of the approach. The case study is inspired to South Sudan situation and to the necessity to provide accommodations, food, health care services, security and administrative support to a large number of IDPs (Internally Displaced Persons) over a wide area. The simulator includes camp preparation and installation, air dr ops, logistics network creation while the model includes populations, entities and units as well as different equipment (e.g. cargo planes, helicopters, ground units, etc.

Understanding fire behavior mechanics in a complex landscape: Simulating wildfires in northeastern Minnesota

Forest species composition and structure in northeastern Minnesota is tightly coupled with the size, frequency, and intensity of historic wildfires on this landscape as evidenced by the abundance fire-dependent forest species (e.g. Pinus banksiana Lamb.). Hence, fire is a salient disturbance agent on this landscape, in that it strongly influences nutrient cycling, carbon stores, and energy pathways between vegetation and the soil. While there are many positive forest ecosystem services associated with fire, there are also risks for an increasing population inhabiting the region. Because of this increase in the wildland-urban interface and shifts in fire frequency and severity induced by climate change, it is increasingly important to accurately model fire behavior and predict fire risk given the current state of fuels on the landscape. Such refinements in fire risk prediction will enable the development and implementation of efficient management strategies to maximize public safety. Managers of the Superior National Forest (SNF) have faced lingering challenges in replicating historical wildfires without doctoring simulation inputs (e.g. increasing wind speeds). Crown fires were not propagating in simulations to the extent that they were in the field. As such, a pilot study was conducted over the course of a two-year period (2015-2017) to investigate fire behavior modeling issues faced by SNF forest managers. The research presented here investigates the sensitivity of the fire area simulator FARSITE to these regionally-calibrated, spatially-explicit, landscape-scale fuel inputs. Initial tests focused on four new canopy bulk density (CBD) models derived from the 2015 pilot study. Additionally, we evaluated a canopy base height (CBH) model using the ground data from the 2015 pilot study as well as low-density LiDAR. Finally, we tested a crosswalked surface fuel model (FBFM) image based on a ruleset provided by managers of the Superior National Forest (SNF) Two historical (2006) fires, Redeye and Famine, were used as proxies for simulation scenarios. A pairwise comparison of spatial correspondence metrics was used to analyze the recently calibrated forest fuels estimates to preexisting raster images provided by LANDFIRE. Results of this study provided evidence that the locally-calibrated images represented historical fire perimeters more accurately than LANDFIRE estimates for CBD and FBFM. However, the comparison between CBH estimates proved unsubstantial. These data products will allow a range of potential fire behavior options for the managers of the SNF to use in risk assessment and fuel management