Optimizing technology R&D and adoption with adaptive adversaries : a Markov game approach with cyber and biosecurity applications
Abstract/Contents
- Abstract
- Decision makers in government, industry, and academia need to consider adversaries with harmful intentions when planning the development and adoption of some new technologies. Typical decision models for technology focus on commercial applications where patent law or market economics strongly influence competition. Existing models do not address all cases where adversaries can co-opt or misuse even a tightly regulated technology. This dissertation extends the technology adoption literature to consider technology decisions with adversaries in conflict scenarios where technology determines outcomes for the decision makers. We develop a general framework and model that is intended to capture strategic issues in technology decision making using a Markov game. Then we use the model to answer contemporary risk management questions in two diverse fields: cyber operations (a high-obsolescence technology field) and synthetic biology (a case study on dual-use influenza viral research). Technology R& D, conflict events, and external random events are modeled as stochastic processes for each player. These cases seem unrelated—but they share mathematical features that benefit from analysis with our model.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Keller, Philip John | |
|---|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. | |
| Primary advisor | Paté-Cornell, M. Elisabeth (Marie Elisabeth) | |
| Thesis advisor | Paté-Cornell, M. Elisabeth (Marie Elisabeth) | |
| Thesis advisor | Relman, David A | |
| Thesis advisor | Weyant, John P. (John Peter) | |
| Advisor | Relman, David A | |
| Advisor | Weyant, John P. (John Peter) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Philip John Keller. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Philip John Keller
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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