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41
Chapter Seven: Statistical Analysis
In order to carry out the statistical analysis over the data set, a robust method is needed. This is because of the non-normal distribution of the data. I chose to run Yuen’s test for comparing trimmed means [28]. For my tests, the standard 20% trimmed mean was used. A trimmed mean refers to a situation where a certain proportion of the largest and smallest sample points are removed and the remaining sample points are averaged. This is typically done to help reduce variance in data collections that may have extreme outliers that can skew data sets [23, 24]. This method has been considered due to its use in some of the previous work [18].
Cluster 1
The statistical probability parameters (p-value) for various game models against each other in cluster 1 are shown in Tables 26 to 30.
PT
PT-Attract
COBRA (α = 0.15)
COBRA (α = 0.5)
DOBSS
QRE (λ = 0.45)
QRE (λ = 0.76)
PT
1
0.6225
0.0962
0.1654
0.4407
0.0016
0.0215
PT-Attract
0.6225
1
0.0268
0.0489
0.1572
0.0004
0.0054
COBRA (α = 0.15)
0.0962
0.0268
1
0.0004
0
0
0.0423
COBRA (α = 0.5)
0.1654
0.0489
0.0004
1
0
0
0.0104
DOBSS
0.4407
0.1572
0
0
1
0
0.0005
QRE (λ = 0.45)
0.0016
0.0004
0
0
0
1
0.0094
QRE (λ = 0.76)
0.0215
0.0054
0.0423
0.0104
0.0005
0.0094
1
Table 26: p-values for 3 Gate settings in Cluster 1
Object Description
| Title | Computational model of human behavior in security games with varying number of targets |
| Author | Goenka, Mohit |
| Author email | mgoenka@usc.edu; mohitgoenka@gmail.com |
| Degree | Master of Science |
| Document type | Thesis |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-03-30 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-04-19 |
| Advisor (committee chair) | Tambe, Milind |
| Advisor (committee member) |
John, Richard S. Maheswaran, Rajiv T. |
| Abstract | Security is one of the biggest concerns all around the world. There are only a limited number of resources that can be allocated in security coverage. Terrorists can exploit any pattern of monitoring deployed by the security personnel. It becomes important to make the security pattern unpredictable and randomized. In such a scenario, the security forces can be randomized using algorithms based on Stackelberg games.; Stackelberg games have recently gained significant importance in deployment for real world security. Game-theoretic techniques make a standard assumption that adversaries' actions are perfectly rational. It is a challenging task to account for human behavior in such circumstances.; What becomes more challenging in applying game-theoretic techniques to real-world security problems is the standard assumption that the adversary is perfectly rational in responding to security forces' strategy, which can be unrealistic for human adversaries. Different models in the form of PT, PT-Attract, COBRA, DOBSS and QRE have already been proposed to address the scenario in settings with fixed number of targets. My work focuses on the evaluation of these models when the number of targets is varied, giving rise to an entirely new problem set. |
| Keyword | artificial intelligence; behavioral sciences; game theory; human behavior; COBRA; DOBSS; PT; PT-Attract; QRE; Stackelberg |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3757 |
| Contributing entity | University of Southern California |
| Rights | Goenka, Mohit |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Goenka-4204 |
| Archival file | uscthesesreloadpub_Volume62/etd-Goenka-4204.pdf |
Description
| Title | Page 56 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 41 Chapter Seven: Statistical Analysis In order to carry out the statistical analysis over the data set, a robust method is needed. This is because of the non-normal distribution of the data. I chose to run Yuen’s test for comparing trimmed means [28]. For my tests, the standard 20% trimmed mean was used. A trimmed mean refers to a situation where a certain proportion of the largest and smallest sample points are removed and the remaining sample points are averaged. This is typically done to help reduce variance in data collections that may have extreme outliers that can skew data sets [23, 24]. This method has been considered due to its use in some of the previous work [18]. Cluster 1 The statistical probability parameters (p-value) for various game models against each other in cluster 1 are shown in Tables 26 to 30. PT PT-Attract COBRA (α = 0.15) COBRA (α = 0.5) DOBSS QRE (λ = 0.45) QRE (λ = 0.76) PT 1 0.6225 0.0962 0.1654 0.4407 0.0016 0.0215 PT-Attract 0.6225 1 0.0268 0.0489 0.1572 0.0004 0.0054 COBRA (α = 0.15) 0.0962 0.0268 1 0.0004 0 0 0.0423 COBRA (α = 0.5) 0.1654 0.0489 0.0004 1 0 0 0.0104 DOBSS 0.4407 0.1572 0 0 1 0 0.0005 QRE (λ = 0.45) 0.0016 0.0004 0 0 0 1 0.0094 QRE (λ = 0.76) 0.0215 0.0054 0.0423 0.0104 0.0005 0.0094 1 Table 26: p-values for 3 Gate settings in Cluster 1 |
