Scaling decision analysis
Abstract/Contents
- Abstract
- The problem that motivates this dissertation is why some decision problems have automated support tools, while others remain manual. Economic theory suggests that for any good or service where demand increases, an entrepreneur will appear to help fill the gap between supply and demand. The framework of decision analysis has shown a normative approach to the decision-making process, and since its inception, many have tried to explain the technical requirements needed to achieve intelligent decision systems or automated decision support. But the market has thus far failed to leverage this body of knowledge to create automated decision support business at scale. This dissertation presents an analytic framework for the creation of automated decision businesses and allows entrepreneurs who wish to enter this market to have a quantitative approach to understand which barriers have the most significant effect on the lag of automated decision support. This theory provides us with the ability to measure the impact of implementing new ideas and how future research could affect those barriers for any particular decision problem. One of the main barriers to automation is the need for personalization that is common in most decision problems. This barrier stems from the difficulty of eliciting preferences from decision-makers and encoding the elicitation process in a way that lends itself to automation. This dissertation provides a mathematical approach to preference elicitation (Preference Thresholds) that significantly improves the ability of decision-makers to interact with automated decision systems as well as reducing the implementation costs of automation, and lowering the cognitive burden on untrained decision-makers. Preference Thresholds allow for the piecewise discretization of continuous value functions around points of interest (preference) to each decision-maker. PTs also represent a way to reduce the size of uncertainties around preferences and to frame problems to eliminate alternatives that do not fit in with the decision maker's values.\ Another critical barrier to automation is the difficulty of implementing these models at scale. This dissertation provides an example of how broad model templates can be used to reduce the difficulty by providing willing entrepreneurs with a starting point for a particular type of decision. In the application chapter, this dissertation introduces a broad model template for patient-centric medical decision making and proposes a way to customize that template to many conditions as well as a way to automate the customization process. The template, together with preference thresholds (PT), is used to create, develop, and deploy an automated decision support tool to aid with the treatment selection decision of prostate cancer patients. The tool is implemented at Stanford Cancer Center for patients with clinically localized prostate cancer who come for a consult with a radiation oncologist.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Martinez Machado, Alejandro Jose |
|---|---|
| Degree supervisor | Howard, Ronald A. (Ronald Arthur), 1934- |
| Thesis advisor | Howard, Ronald A. (Ronald Arthur), 1934- |
| Thesis advisor | Scheinker, David |
| Thesis advisor | Shachter, Ross D |
| Degree committee member | Scheinker, David |
| Degree committee member | Shachter, Ross D |
| Associated with | Stanford University, Department of Management Science and Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Alejandro Martinez. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Alejandro Jose Martinez Machado
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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