Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation
Nuclear Proliferation is a complex problem that has plagued national security strategists since the advent of the first nuclear weapons. As the cost to produce nuclear weapons has continued to decline and the availability of nuclear material has become more widespread, the threat of proliferation...
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| Format: | Technical Report |
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Massachusetts Institute of Technology. Center for Advanced Nuclear Energy Systems. Nuclear Fuel Cycle Program
2012
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| Online Access: | http://hdl.handle.net/1721.1/75263 |
| _version_ | 1811072903626620928 |
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| author | Holcombe, Robert Golay, Michael W. |
| author2 | Massachusetts Institute of Technology. Nuclear Fuel Cycle Program |
| author_facet | Massachusetts Institute of Technology. Nuclear Fuel Cycle Program Holcombe, Robert Golay, Michael W. |
| author_sort | Holcombe, Robert |
| collection | MIT |
| description | Nuclear Proliferation is a complex problem that has plagued national security strategists
since the advent of the first nuclear weapons. As the cost to produce nuclear weapons has
continued to decline and the availability of nuclear material has become more widespread,
the threat of proliferation has increased. The spread of technology and the globalization of
the information age has made the threat not only more likely, but also more difficult to
detect. Proliferation experts do not agree on the universal factors which cause nations to
want to proliferate or the methods to prevent countries from successfully developing nuclear
weapons. Historical evidence also indicates that the current nuclear powers pursued their
nuclear programs for different reasons and under different conditions. This disparity
presents a problem to decision makers who are tasked with preventing further nuclear
proliferation.
Bayesian Inference is a tool of quantitative analysis that is rapidly gaining interest in
numerous fields of scientific study that have previously been limited to purely statistical
methods. The Bayesian approach removes the statistical limitations of large-n data sets and
strictly numerical types of data. It allows researchers to include sparse and rich data as well
as qualitative data based on the opinions of subject matter experts. Bayesian inference
allows the inclusion of both the quantitative data and subjective judgments in the
determination of predictions about a theory of interest. This means that contrary to classic
statistical methods, we can now make accurate predictions with reduced information and
apply this probabilistic method to problems in social science.
The problem of nuclear proliferation is one that lends itself to a Bayesian analysis. The data
set is relatively small and the data is far from consistent from country to country. There is
however, a wide body of literature that seeks to explain proliferation factors and capabilities
through both quantitative and qualitative means. This varied field can be brought together in
a coherent method using Bayesian inference and specifically Bayesian Networks which
graphically represent the various causal linkages. This work presents the development of a
Bayesian Network describing the various causes, factors, and capabilities leading to
proliferation. This network is constructed with conditional probabilities using theoretical
insights and expert opinion. Bayesian inference using historical and real time events within
the structure of the network is then used to give a decision maker an informed prediction of
the proliferation danger of a specific country and inferences about which factors are causing
it. |
| first_indexed | 2024-09-23T09:20:46Z |
| format | Technical Report |
| id | mit-1721.1/75263 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:20:46Z |
| publishDate | 2012 |
| publisher | Massachusetts Institute of Technology. Center for Advanced Nuclear Energy Systems. Nuclear Fuel Cycle Program |
| record_format | dspace |
| spelling | mit-1721.1/752632019-04-12T21:17:25Z Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation Holcombe, Robert Golay, Michael W. Massachusetts Institute of Technology. Nuclear Fuel Cycle Program Holcombe, Robert Golay, Michael W. Nuclear Proliferation is a complex problem that has plagued national security strategists since the advent of the first nuclear weapons. As the cost to produce nuclear weapons has continued to decline and the availability of nuclear material has become more widespread, the threat of proliferation has increased. The spread of technology and the globalization of the information age has made the threat not only more likely, but also more difficult to detect. Proliferation experts do not agree on the universal factors which cause nations to want to proliferate or the methods to prevent countries from successfully developing nuclear weapons. Historical evidence also indicates that the current nuclear powers pursued their nuclear programs for different reasons and under different conditions. This disparity presents a problem to decision makers who are tasked with preventing further nuclear proliferation. Bayesian Inference is a tool of quantitative analysis that is rapidly gaining interest in numerous fields of scientific study that have previously been limited to purely statistical methods. The Bayesian approach removes the statistical limitations of large-n data sets and strictly numerical types of data. It allows researchers to include sparse and rich data as well as qualitative data based on the opinions of subject matter experts. Bayesian inference allows the inclusion of both the quantitative data and subjective judgments in the determination of predictions about a theory of interest. This means that contrary to classic statistical methods, we can now make accurate predictions with reduced information and apply this probabilistic method to problems in social science. The problem of nuclear proliferation is one that lends itself to a Bayesian analysis. The data set is relatively small and the data is far from consistent from country to country. There is however, a wide body of literature that seeks to explain proliferation factors and capabilities through both quantitative and qualitative means. This varied field can be brought together in a coherent method using Bayesian inference and specifically Bayesian Networks which graphically represent the various causal linkages. This work presents the development of a Bayesian Network describing the various causes, factors, and capabilities leading to proliferation. This network is constructed with conditional probabilities using theoretical insights and expert opinion. Bayesian inference using historical and real time events within the structure of the network is then used to give a decision maker an informed prediction of the proliferation danger of a specific country and inferences about which factors are causing it. Idaho National Laboratory Massachusetts Institute of Technology. Dept. of Nuclear Science and Engineering Massachusetts Institute of Technology. Center for Advanced Nuclear Energy Systems 2012-12-05T21:08:34Z 2012-12-05T21:08:34Z 2010-04 Technical Report http://hdl.handle.net/1721.1/75263 MIT-NFC;TR-117 application/pdf Massachusetts Institute of Technology. Center for Advanced Nuclear Energy Systems. Nuclear Fuel Cycle Program |
| spellingShingle | Holcombe, Robert Golay, Michael W. Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation |
| title | Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation |
| title_full | Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation |
| title_fullStr | Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation |
| title_full_unstemmed | Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation |
| title_short | Development of a Bayesian Network to Monitor the Probability of Nuclear Proliferation |
| title_sort | development of a bayesian network to monitor the probability of nuclear proliferation |
| url | http://hdl.handle.net/1721.1/75263 |
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