Dear Colleagues,
We are delighted to invite you for the finalist presentations of the DAS - SDP Practice Award.
The Decision Analysis Practice Award is sponsored jointly by the Decision Analysis Society and the Society of Decision Professionals. It is given annually to the best decision analysis application, as judged by a panel of members of both Societies. The winner(s) will be announced during the 2023 INFORMS conference, October 15 – 18 in Phoenix, Arizona. This year we are running this session via an open webinar to reflect the dual-society award, and to further promulgate this prestigious prize with our Decision Analysis Society (DAS) and Society of Decision Professionals (SDP) communities.
Please join us on Wednesday, October 11th from 8:00am to 9:30am Pacific Time to learn more about the three innovative projects that are finalists this year. Please register for the Webinar below.
Welcome! You are invited to join a webinar: DAS-SDP Practice Award Finalist Presentation 2023. After registering, you will receive a confirmation email about joining the webinar.
Details about the award are given below.
Fostering participation and knowledge construction processes in real settings through decision analysis and collaborative value modelling
Presenter: Mónica Duarte Oliveira (and Team Members Carlos António Bana e Costa and Ana Vieira)
To address the challenge of effectively engaging many stakeholders and experts in real-world decision analysis processes, both for knowledge construction and for stakeholder engagement, we have developed the Collaborative Value Modeling (CVM) framework. The CVM combines large-scale participatory Web-Delphi processes with smaller-scale decision conferencing or workshop processes to promote agreement in different modeling stages of multicriteria decision analysis (MCDA).
A Decision Support for Portfolio Capital Allocation for Internal Capital Market at Dow Chemicals
Presenter: Saurabh Bansal
This project involved a decision analysis application of portfolio management for the $400 annual Internal Capital Allocation Program at Dow Chemicals. The total amount requested across all projects exceeded $600 million, which led Dow to make the project allocation decisions based on cost estimates provided by managers for individual projects. However, this motivated managers to systematically provide low estimates for costs. There was also no incentive for managers to return surplus amount if projects finished with a surplus. We developed a data-driven near optimal mechanism that addressed these issues. It (i) provided a quantification for the variability in actual project costs, (ii) This variability provided a basis to determine a strategic reserve for potential cost overages, (iii) encouraged managers to truthfully report cost estimates. The mechanism was implemented with success at the firm.
A Multiattribute Decision Model to Evaluate Potential Investments in Near-Earth Object Detection Technologies
Presenter: Ralph Keeney (and Team Members Thomas S. Palley, Victor Richmond R. Jose, Asa Palley and Mario Juric)
Asteroids and other near-earth objects (NEOs) pose a significant threat to our planet. Advance detection is essential to respond to any object on a collision course, but detection and tracking technologies require substantial investments. We provide a multiattribute utility framework to analyze which NEO detection technologies offer the best decision alternatives using a stylized model of the uncertainties, objectives, and tradeoffs inherent to decisions involving low-probability, high-consequence events.
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Onesun Yoo
Associate Professor
UCL School Of Management, University College London
London
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