ISyE Seminar- Elisabeth Paulson

Event Details
  • Date/Time:
    • Monday January 11, 2021
      11:00 am - 12:00 pm
  • Location: Virtual
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    N/A
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Summaries

Summary Sentence: Optimizing group-level food policy interventions

Full Summary: The federal government currently spends over $100 billion per year on interventions aimed at increasing fruit and vegetable consumption among low income households. These include interventions related to price, nutrition education, and access. Currently, funds are allocated to each type of intervention in a siloed fashion, in some cases resulting in surprisingly disappointing outcomes. The goal of this work is to increase the efficacy of food policy interventions through optimization and increased personalization. This work introduces a novel consumer behavioral model for grocery shopping dynamics, which is nested into a bi-level model for optimizing the government's investments. In this model, the government’s goal is to increase fruit and vegetable (FV) consumption among low income households by utilizing strategic portfolios of interventions (referred to as intervention bundles). However, complete personalization may be undesirable or infeasible. Therefore, group-level personalization—where individuals are assigned to groups that receive unique intervention bundles—is considered. This work develops a new framework that allows us to quantify the level of personalization (i.e., the number of groups) needed to achieve a certain outcome level. We also show how this framework is generalizable to many settings beyond food policy.

Title

Optimizing group-level food policy interventions

Abstract:

The federal government currently spends over $100 billion per year on interventions aimed at increasing fruit and vegetable consumption among low income households. These include interventions related to price, nutrition education, and access. Currently, funds are allocated to each type of intervention in a siloed fashion, in some cases resulting in surprisingly disappointing outcomes. The goal of this work is to increase the efficacy of food policy interventions through optimization and increased personalization. This work introduces a novel consumer behavioral model for grocery shopping dynamics, which is nested into a bi-level model for optimizing the government's investments. In this model, the government’s goal is to increase fruit and vegetable (FV) consumption among low income households by utilizing strategic portfolios of interventions (referred to as intervention bundles). However, complete personalization may be undesirable or infeasible. Therefore, group-level personalization—where individuals are assigned to groups that receive unique intervention bundles—is considered. This work develops a new framework that allows us to quantify the level of personalization (i.e., the number of groups) needed to achieve a certain outcome level. We also show how this framework is generalizable to many settings beyond food policy.

Bio:

Elisabeth Paulson is a fifth-year Ph.D. candidate at the MIT Operations Research Center, where she is co-advised by Prof. Retsef Levi and Prof. Georgia Perakis. She is broadly interested in data-driven policy making and the design of public interventions for social good. Her current research focuses on supply chain and public policy interventions for creating better access to, and consumption of, fresh food. Before coming to MIT, she received a B.S. in Mathematics, B.S. in Statistics, and M.A. in Mathematics from the Pennsylvania State University. Elisabeth also spent a year working as a data scientist for Booz Allen Hamilton, and spent the summer of 2019 as an intern with IBM Food Trust. Elisabeth’s research is supported by the NSF Graduate Research Fellowship.

Additional Information

In Campus Calendar
Yes
Groups

School of Industrial and Systems Engineering (ISYE)

Invited Audience
Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students
Categories
Seminar/Lecture/Colloquium
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Status
  • Created By: sbryantturner3
  • Workflow Status: Published
  • Created On: Nov 30, 2020 - 8:47am
  • Last Updated: Dec 17, 2020 - 9:37am