Scaling Productivity and Innovation on the Path to Exascale with a ?Team of Teams? Approach.
Abstract not provided.
Raybourn, Elaine M.↗
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Enabling coordinated, distributed development of scientific software: A research agenda for adapting a team of teams approach
The “team of teams” concept is a strategy for coordinating activities within and across organizations, developed by General Stanley McChrystal and his colleagues based on their experiences leading the Joint Special Operations Task Force in Iraq from 2003-2008. These experiences, and the lessons learned from them, are recounted in the books Team of Teams (McChrystal 2015) and One Mission (Fussell 2017) (the authors and contributors to these books will hereafter be jointly referred to as McChrystal et al.). Essentially, their story is one of rapidly adapting a rigid, siloed organization to defeat the flexible, resilient, and fast-moving network of Al Qaeda in Iraq. As described in the books, when McChrystal took over the Task Force, he found a collection of highly competent, efficient teams of analysts and warfighters which nonetheless had difficulty operating in a unified manner due to close internal ties, distrust of outsiders, bureaucratic stove pipes, and cultural differences stemming from the diverse organizations Task Force teams were affiliated with, including Army Special Forces, CIA, and NSA.
97 MATHEMATICS AND COMPUTING↗
“Shoulda, Coulda, Woulda”: Conceptualizing the Differences in Trust Between Human-Human Teaming and Human-Machine Teaming
Intelligent decision support systems (IDSSs) are machine teammates designed to facilitate better human decision-making in high-consequence domains such as health care, power grid operations, and fraud detection. IDSSs identify patterns in datasets and provide intelligent decision-making recommendations to human teammates. However, previous research indicates that humans often trust IDSS recommendations less than the recommendations from their human teammates, even when the machine teammate is more accurate. To conceptualize why trust differs, we review the literature surrounding trust, error, and predictability. Then, we compile and compare participant trust ratings and decision-making in an abridged systematic review of previous studies manipulating teammate type, error rate, and error type. Finally, we conduct a content analysis of participants’ qualitative responses to trust queries from a survey on generative language models. Results suggest that humans may trust IDSS teammates less than other human teammates because of differences in (1) interaction complexity, (2) blame attribution, and (3) swift trust. We conclude that human factors practitioners should collaborate with data scientists and domain experts to build and maintain trust in IDSSs by anthropomorphizing algorithms, matching mental models, and considering individual differences.
97 MATHEMATICS AND COMPUTING↗
A Data-driven Approach to Rethinking Open Source Software Organizations as a Team of Teams.
Abstract not provided.
Raybourn, Elaine M.↗
Team Dynamics and Development in Chemical Emergency Response
This presentation is focused on Team Dynamics and Development in Chemical Emergency Response. While regulatory requirements focus on defining the performance requirements associated with this category of response team, there is very little information and guidance provided to team leaders on the criticality of team dynamics of development in performance. These elements are essential to achieving successfully integrated team operations. NREL has worked to expand chemical response team participation to include operational personnel from research, support operations, and various technical areas. Since this team expansion has encompassed integration from numerous levels and areas within the organization, a systematic approach was adopted to better understand how organization culture, dynamics, and diversity can be used to be more effective. Benchmarking other organizations and reviewing relevant literature assisted in laying the foundation to develop a strategy that incorporates team development principles, dynamics, and leadership practices to increase the effectiveness of emergency response operations. This presentation focuses on the valuable lessons learned and the team-based approaches that can be used to develop a successful response team.
emergency response↗
ESGF project plan for ramping down development activities and transition the project and data to a new team
This document summarizes the current software development activities carried by the ESGF team with planned delivery dates before the transition of the project to a new team. The work we started on the publishing, search and retrieval, and the backend for indexing and data management that can be deployed using Kubernetes are planned to be delivered within the next three months; however, this assumes that the current team members will continue to work on the project. The activities are listed in Table 1 by priority and in case we lose team members, development of lower priority tasks can be paused and passed to the new team with its current status. Team members can be re-assigned based on their availability and transition work needed. Table 2 below describe activities necessary to help project transition to the new team. Deadlines for these activities are before October 1 st of 2022 or the completion of a successful transition. Support during the transition period assumes that we will have 3 key ESGF members and the ESGF PI supported at the 50% level or less for the duration of the transition. The level of support is an estimate, and it depends on several factors that cannot be decided at this time. The total cost of the proposed work and support activities for fiscal year 2021 is $1,270K with $230K carry over to fiscal year 2022. The cost for the support work listed in Table 2 is the sum of the two rows “HW support” and “project support, maintenance, and transition efforts”, colored blue, in Table1 which adds up to $720K. This cost is included in the $1,270K cost in table 1.
97 MATHEMATICS AND COMPUTING↗
Collaborative Management for Complex Closure Teams - 19246
The closure documentation for government waste management sites requires a multidisciplinary approach to achieve success. This approach is necessary and similar to approaches utilized across remediation projects in the private and public sectors [1]. Closure documentation for government projects can range from tens of pages to upwards of thousands of pages. They normally take more than six months to put together - although, in many cases, it takes years - with multiple revisions. These documents, while all unique, share common themes with regards to communication and conflict with the other waste management projects. This paper explores the challenges in management and leadership approaches for generating technical documents, such as those found in the closure missions at government hazardous waste management sites. These matrixed teams are composed of many different members with varying expertise and experience, each with different stakeholder backgrounds. These teams experience challenges not only from using a matrixed approach - where the majority of the members are only partially engaged in the project - but also from integrating each of their respective stakeholder group's cultural individualities [2]. The Project Leads, who head up these highly matrixed projects, operate in environments comprised of virtual project teams- or more specifically in this context, intensive virtual project teams. Most of the participants are within the same city, but all of the document project teams operate with some level of organizational dispersion. Moreover, the larger a project becomes the more dispersed the team becomes. The frequent addition of sub-contracting resources by Project Leads further disperses the group and dilutes communication. The difficulties in communication derive from complications in the goal setting stages of a project and can manifest across the project team in the form of motivational issues, disengagement, quality expectations, and a loss of understanding. This puts a considerable amount of importance on a Project Lead to establish goals through the appropriate tools and methodology. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Characterizing Interaction Uncertainty in Human-Machine Teams
With the increasing use and adoption of artificial intelligence (AI), the reliability of modern data systems will be driven by a tighter teaming between human experts and intelligent machine teammates. As in the case of human-human teams, the success of human-machine teams will also rely on clear communication about mutual goals and actions. In this paper, we combine related literature from cognitive psychology, human-machine teaming, uncertainty in data analysis, and multi-agent systems to propose a new form of uncertainty: interaction uncertainty for characterizing bidirectional communication in human-machine teams. We map the causes and effects of interaction uncertainty and outline potential ways to mitigate uncertainty for mutual trust in a high-consequence real-world scenario.
uncertainty, data analytics, interaction, trust, h↗
Using AI Simulations to Dynamically Model Multi-agent Multi-team Energy Systems
The complexity of energy systems is well known as they are complex and intricate systems. As a result, many extant studies have used many simplifications or generalizations that do not accurately reflect the nature of this complex system. In particular, most HVAC systems are modeled as a single unit, or several large units, rather than as a hierarchical composite (e.g., as a floor rather than as a collection of disparate rooms). The net result of this is that the simulations are too generic to perform meaningful analysis, machine learning, or integrated simulation. We propose using a multi-agent multi-team strategic simulations framework called SiMAMT to better define, model, simulate, and learn the HVAC environment. SiMAMT allows us to create distinct models for each type of room, hierarchically aggregate them into units (like floors, or sections), and then into larger sets (like buildings or a campus), and then perform a simulation that interacts with each sub-element individually, the teams of sub-elements collectively, and the entire set in aggregation. Further, and most importantly, we additionally model another ‘team’ within the simulation framework - the users of the systems. Again, each individual is modeled distinctly, aggregated into sub-sets, then collected into large sets. Each user, or agent, is performing on their own but with respect to the larger team goals. This provides a simulation that has a much higher model fidelity and more applicable results that match the real-world.
Franklin, D. Michael↗
Automating Software Productivity Planning: Lightweight Tools for Upgrading Team Practices
A lightweight iterative workflow, called Productivity and Sustainability Improvement Planning (PSIP) has been developed to assist software teams with employing good software engineering practices. Three PSIP tools automate practice summarization, self-assessment, and progress tracking to enhance team engagement around software process improvement: Reposcanner, RateYourProject (RYP) and Progress Tracking Cards (PTCs) respectively. Reposcanner is a data analysis tool. RYP is an interactive self-assessment tool that provides an easy-to-use entry point to the PSIP process. Once process improvements are identified, PTCs are created by teams to measure outcomes and progression toward their goals. We present an exemplifying case study with a software team where these tools have been put to use, along with a description of the tools, and future plans for further improvements.
Raybourn, Elaine↗
Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking
"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.
Sensemaking, human-machine teaming, agency, artifi↗
SaS4D Home Team UI (SaS4D-HT-UI) v1.0
The SaS4D Home Team UI (python) is a software to view and interact with different layers of 3D geometries and generate usable MCNP-style input file. It is used by the remote Home Team in providing guidance and building models of environments they have never seen in order to investigate threat object discovered at the Working Point. The UI visualizes a colorized mesh, a semantic labelled mesh, and a semantic labelled probability mesh of the scanned environment as well as individual water-tight material-labeled objects. It allows for manipulation and re-processing of these objects. The UI also contains measurement tools to facilitate better MCNP input file generation in the manipulation workflow. The software is a key component in ensuring the Home Team has prompt awareness of the Working Point.
Chen, Xin↗
Winning Asset Management Improvement Team: Maintenance Planning and Scheduling in a Highly Regulated Environment
The Y-12 National Complex (Y-12) site has numerous aging facilities that are crucial to the Department of Energy and the national security strategy for the nation. Y-12’s commitment to safety and regulatory compliance is of the highest importance. The commitment to meet the national security mission also creates additional rigor and complexity to the everyday maintenance and planning process. Y-12 is a collection of many facilities, both old and new, nestled between two ridges in Oak Ridge, TN. Y-12 was made with the short-term focus of ending “The Great War” through the creation of the worlds’ first atomic bomb. Almost eighty (80) years have passed since the groundbreaking, with the mission of the site changing from decade to decade. While the mission has changed, the way Y-12 employees continuously meet the challenge has not. The site was created to react and overcome; Y-12 still takes pride in the ability to react and overcome. The difference is the site is no longer ignorant to the need for a better way to manage the aging facilities and infrastructure. Shear willpower and determination was once the way to reach the objectives, but as a wise man once said, “Work smarter, not harder.” The business case for change started within the senior leadership at Y-12. A team of managers sat down and dictated objectives to provide a clear scope for the maintenance planning and scheduling optimization team, to include our Eruditio integrated blended learning coaches. In addition to providing the direction, they also made themselves available for escalation of issues in the event the team ran in to road blocks.
99 GENERAL AND MISCELLANEOUS↗
TEAM Project Review, Year 2
This report summarizes our research activities within the TEAM project between December 2020 and December 2021, funded by the ASCR Advanced Research in Quantum Computing program. During the reporting period the LLNL-MSU team has made progress on several fronts. An overarching goal of the team is to provide a comprehensive suite of software tools that can be used for the Characterize-Optimize-Compute loop needed to implement and execute algorithms on quantum devices. We are concurrently developing lightweight solvers that can be used on desktop computers to find optimal control pulses and to characterize small quantum systems (consisting of a few transmons and cavities). However, desktop computers are insufficient for simulating and characterizing larger quantum systems. We have therefore also developed parallel, distributed memory, simulators and optimization solvers, both for open and closed quantum systems. These parallel solvers have, for example, been used to study quantum optimal control for pure-state preparation, utilizing 1000’s of cores on a modern high-performance computing (HPC) platform.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Bob Geyer: Coaching wrestling or clearing waste, he puts his team first
When Bob’s team started the project of clearing drums out of the PF-4 basement, there were 1,800 drums inside. Now, there are less than 500. It’s no coincidence that Bob Geyer leads a team that goes by the acronym of MVP — move, validate and prepare — more commonly recognized as “most valuable player.” Bob and the MVP team manage waste drums after they’ve been packaged. Their job is to constantly work a puzzle of drums and locations based on the contents of the drums, where they came from and where they’re headed. All the while, they’re ensuring there’s enough floor space in the Plutonium Facility for the waste materials that come out of daily production operations.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Our teams design high-resolution MeV x-ray radiography systems for National security missions [Poster]
The NNSS has been at the forefront of custom high-fidelity x-ray/gamma radiographic imaging solutions that serve our national security for over five decades. Our radiography team utilizes expertise in physics modeling and analysis, along with optical, mechanical, and electrical design, in close collaboration with our customers, to develop imaging methods and capabilities that go beyond their needs. Conceptual designs are developed through R&D efforts to provide solutions for the specific problem at hand. Field systems are designed and built in-house, then qualified utilizing a range of facilities across the National Security Enterprise. Radiographic imaging systems are deployed by our team in the most challenging environments. The NNSS has a strong math and programming team that provides novel on-site image analysis methods that extract crucial information from data returned in field tests.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗