Posters

The Society of Decision Professionals (SDP) is pleased to announce our Call for Posters for Students + Early Career Professionals with 10 years or less of experience in the field of decision analysis or related disciplines. This poster session takes place at the Annual SDP Conference & Workshops, www.sdpevents.com, which is celebrating 30 years of conferencing! The conference will take place in the vibrant city of Washington DC at the Hilton Arlington hotel in Arlington, Virginia, from April 15-18, 2024.

Mission and Opportunity:

Aligned with one of our key mission pillars, SDP is dedicated to the education and engagement of young professionals in the science of decision-making. This session offers a unique opportunity to showcase your research, engage in multidisciplinary collaborative discussions, and address unsolved issues in the realm of Decision Science theory and applications.

Conference Theme:

The theme for this milestone conference is "Decision Making in a Dynamic World." We will focus on five key industries: Pharma, Energy, Government, Natural Resources/Sustainability. Our Early Career Poster Session aims to contribute fresh perspectives to these dynamic fields.

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  • Presented by: Thomas Palley

  • Co-authored by: Victor Richmond R Jose (Georgetown University - McDonough School of Business), Asa Palley (Indiana University - Kelley School of Business - Department of Operation & Decision Technologies), Ralph Keeney (Duke University - Fuqua School of Business), Mario Juric (University of Washington - Department of Astronomy)

  • Abstract:

  • Asteroids and other near-earth objects (NEOs) pose a significant ongoing threat to our planet, with the potential to catastrophically disrupt life on Earth. Advance detection is essential to be able...

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  • Authors: Erik Schneider & Diana Del Bel Belluz (Claridec)

  • Abstract:

  • The context of decision-making is ever-changing. The post-COVID reset, culture wars and the advent of generative AI have all impacted how organizations make decisions. Where does organizational decision-making stand today? Our Decision Capabilities Benchmarking Model, inspired by foundational work developed at Stanford University, introduces a novel framework for evaluating and enhancing decision-making processes within contemporary organizations. This...

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  • Authors: Diana Del Bel Belluz & Erik Schneider (Claridec)

  • Abstract:

  • The failure of corporate strategies has been linked to common weaknesses in organizational decision-making. Survey data shows that the following decision-making weaknesses are implicated in 20-60% of failed corporate strategies: underestimating risks, overestimating rewards, overestimating management’s ability to predict and control future events, and leaders taking too much or too little risk given the risk and rewards...

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  • Author: Dr. Zachry Engel (Lone Star Analysis)

  • Abstract:

  • The current Risk Reporting Matrix, or simple risk matrix, is a tool used by many decision-makers, including those in the Department of Defense. The risk matrix is often used due to its simplicity to understand and create. However, it is a tool that is not well suited for proper decision making and risk analysis. The risk matrix is built using two 5-point Likert scales, one to represent the likelihood of...

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    • Author: Raul Rios (Lone Star Analysis)

    • Abstract:

    • To achieve high prediction accuracy, common Artificial Intelligence (AI) methods require vast amounts of training datapoints. Such methods, therefore, have diminished usefulness when data is scarce. One option to alleviating data scarcity is to generate new synthetic datapoints that are similar to the original dataset. A potential way to do this is to create a probability distribution fit to the original data and then randomly sample from that distribution to generate synthetic – but realistic – datapoints. A plethora of distributions are available for smooth, continuous data (e.g., normal, Student’s t,...

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    • Authors: Walter Kosi, Ralph Hinsch, Jost Püttmann, Kent Burkholder, Hanns Peter Schmid (OMV) (Decision Frameworks)

    • Abstract:

    • OMV is a Vienna based energy company with an Upstream division and Exploration & Production activities in 13 countries. In November 2023 OMV faced a complex challenge when they commenced drilling a 5500m deep well, Strasshof T17, in the Vienna Basin, Austria. The project aimed to explore a complex subsurface structure, poorly imaged on seismic data. The initial plan involved drilling an expensive pilot hole before the final sidetrack to mitigate subsurface uncertainties.

      To address this...

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    • Author: Joseph Rilling (Temple University)

    • Abstract:

    • The recently introduced metalog distributions (Keelin 2016) present an alternative to traditional probability distributions. Metalog distributions are extremely flexible and can be easily fit via ordinary least squares estimation. Once fit, a metalog distribution immediately yields a CDF, PDF, and quantile function, making decision analysis and monte carlo methods trivial.

      The metalog procedure needs to estimate the percentile of each observation. All current methods calculate the percentiles by implicitly assuming observations are independently and identically distributed...

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    • Author: Simon Brooks (Villanova University Sustainable Engineering)

    • Abstract:

    • Across all large or small companies, federal or local governments, communities, families, and individuals, every action we take is fundamentally answering the decision-making question of how we should spend our limited time on Earth. The answer to this penultimate question will decide for how long humanity will survive, and to what extent society will operate on the optimal pathway—one that sustains life on Earth equitably and justly for all people and all species of Earth’s biodiversity, today, and for all time. We are at a critical time in history that demands all decisions to...

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    • Author: Stephanie McLaney (Decision Education Foundation)

    • Co-Author: Chris Spetzler (Decision Education Foundation)

    • Abstract:

    • Decision Education Foundation has tools for assisting youth, educators, and school administrators to learn and benefit from Decision Quality. The Decision Island activity is a creative way to introduce Decision Skills to students (7th –12th grade) and explore what it means to have decision power. The Decision Workspace worksheet helps students work through decisions on their own with an accessible one-page layout. Educators like teachers, counselors, and parents can benefit from the...

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  • Authors: Jost Püttmann, Walter Kosi, Martin Vögele, Donia Wamani (OMV)

  • Abstract:

OMV is a Vienna based energy company with an Upstream division and Exploration & Production

activities in 13 countries. In 2020 an ODQ implementation initiative was started. The implementation journey will be sketched from the initial connection to a digitization program, to developing a DQ flag ship. It will shed light on the challenges of a bottom-up initiative and will highlight important implementation factors that were rolled-out or falsely missed out.

The implementation initiative...

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  • Authors: Sam Vardy (Decision Frameworks)

  • Co-Author: Sri Vaidyanathan (Shell)

  • Abstract:

In an increasingly unpredictable world, scenario thinking is a crucial tool for developing robust supply chain strategies. The global landscape is characterized by uncertainty, with factors such as geopolitical tensions, technological disruptions, climate change, and pandemics, all of which can significantly impact supply chains. Scenario thinking allows us to anticipate and prepare for a range of possible futures, rather than betting on...

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  • Author: Bryce Brosig

  • Co-author: Randy Allen

  • Abstract:

  • This paper demonstrates an optimization technique, which builds on R. Allen’s Adaptive Nonconvex Optimization technique (I/ITSEC in 2019), where one can find optimal control variables for a system that contains uncertainty, that is, one or more of the state variables are stochastic. This optimization technique, in addition to being gradient free and effective on discontinuous and/or non-convex problems, builds a topology of the system robust enough that an optimal solution can be extracted from a noisy objective function. This allows one to accurately model systems that are, in fact, uncertain and...

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