The authors present a methodology and an example of preparing an order of merit list to rank terrorist based upon decision maker weights. This research used an old terrorist data set as our base data to keep the information unclassified. This data is used to demonstrate this methodology. The authors perform numerical iterative criteria weight sensitivity analysis to show the effects on the model’s outputs in changes in the weights. Through their analysis the most critical criterion is identified.
The United States of America is fighting a war against terrorism. The National Strategy for Combating Terror (NSCT) [
According to Department of Defense (DoD) doctrine in Army FM 34-8-2 [
Targeting is the process of selecting targets and matching the appropriate response to them, including operational requirements and capabilities. The purpose of targeting is to disrupt, delay, or limit threat interference with friendly Course of Action (COAs).
Human-targeting, the process of selecting a human target exists as a subset of this more general targeting doctrine. This human targeting research is being applied to terrorists.
A common misconception is that human-targeting denotes either a hard-power and soft-power strategy that involves either kinetic or non-kinetic power. Human-targeting is instead intent or objective neutral. It does not specify the type of action taken nor the counterterrorism (CT) objective desired. Human-targeting, rather, re- presents an analytical process that assigns a heuristic value to a target. This assignment of value allows for the prioritization of multiple targets and this prioritization permits CT organizations to direct efforts and allocate resources. Consequently, every government agency, unit, or official whose function serves to counter terrorism remains dependent on the human-targeting process [
To mitigate this risk of terrorist, we propose the development of a systematic method for the conduct of human targeting. We test the proposition using mathematical modeling and multi-attribute decision making tools. These methods are extensively tested and used for finding key network nodes, [
The current targeting process involves numerous complex and dynamic interactions filled with ambiguities. Minor variations in the process dramatically affect human-targeting decisions producing essentially unpredictable results. In other words, CT organizations may be targeting the wrong (or a less valuable) terrorist. This inefficiency is not only a misuse of intelligence, but wastes limited national resources, which inevitably places lives unnecessarily at risk. Left unaddressed, this critical USG decision-making process with systemic problems could result in a catastrophic intelligence failure [
In previous work by Twedell and Edmonds [
TOPSIS was the result of research and work done by Yoon and Hwang [
Napier [
In manufacturing analysis, Wang [
In a business setting it has been applied to a large number of application cases in advanced manufacturing processes [
We describe the TOPSIS process is carried out through the following steps.
Step 1
Create an evaluation matrix consisting of
native and criteria given as
Step 2
The matrix shown as
using the normalization method
Step 3
Calculate the weighted normalized decision matrix. First we need the weights. Weights can come from either the decision maker or by computation.
Step 3(a)
Use either the decision maker’s weights for the attributes
use Saaty’s (1980) AHP’s decision maker weights method to obtain the weights as the eigenvector to the attributes versus attribute pair-wise comparison matrix.
The sum of the weights over all attributes must equal 1 regardless of the method used.
Step 3(b)
Multiply the weights to each of the column entries in the matrix from Step 2 to obtain the matrix,
Step 4
Determine the worst alternative
where,
We suggest that if possible make all entry values in terms of positive impacts.
Step 5
Calculate the L2-distance between the target alternative
and the distance between the alternative
where
Step 6
Calculate the similarity to the worst condition:
Step 7
Rank the alternatives according to their value from
Since AHP, at least in the pairwise comparisons, is based upon subjective inputs using the 9 point scale then sensitivity analysis is extremely important. Leonelli [
Chen [
The decision weights are subject to sensitivity analysis to determine how the affect the final ranking. Sensitivity analysis is essential to good analysis. Additionally, Alinezhad [
where
value of the selected weight,
A CT analyst produced both target lists (blue and green) between 2004-2005 [
Based on a review of relevant literature as well as our combined experience of personnel in defense analysis department, we identify 96 critical attributes of terrorists to initially use in the modeling process. We organize these 96 critical attributes to test as predictive variables. Many of these variables were categorical (binary) variables, so we tried to consolidate and refine the number of variables to consider. We felt that initially concentrating on the decision criteria might provide useful information. To maintain organization, we subdivided the criteria into four main categories: Cell Membership/Experience Variables; Other Individual Variables; Worldliness Variables; and SNA/Graph Measures Variables that we refer to as Level 1 criteria. We then broke each of these into sub-criteria with their own respective data that we refer to as Level 2 criteria. The Level 2 criteria were used in the OML process. This is highlighted in
We further propose a hierarchy for our analysis.
Objective: Find the Most Dangerous Terrorist
Alternatives: List of terrorists active in 2008
Criteria: Level 1: Level 2 breakdown
Step 1. Obtaining the decision maker weights by level.
Level 1: Priorities: Social Network Analysis, Individual Variables, Cell membership/experience, Worldliness. A begin the pairwise comparisons using our Excel template.
The decision matrix is
The consistency ratio, CR = 0.0372, which is less than 0.1 implies the decision matrix is consistent. The decision weights for Level 1 are:
Next, we proceed to do similar analyses for Level 2. We will take each set of Level 2 variables and obtain their respective weights. In show how we did this in more detail for only one of the Level 1 criteria, Social Networks.
Criteria | ||||
---|---|---|---|---|
Level 1 | Cell Membership/Experience | Individual Variables | Worldliness Variables | Social Network Analysis |
Level 2 | State Sponsorship | Versatility | languages | Degree Centrality |
Safe Havens | References | Countries | Eigenvector Centrality | |
Unity | Age | Speaks English | Closeness | |
Funds | Months as a Terrorist | Propagation Fit | ||
Criminal Activity | Number of Aliases | Bunker Score | ||
Organ. Structure |
For example, we start with the breakdown of Level 1 social network into specific Level 2 criteria shown to be valid variables and follow the same methods to obtain our decision weights.
The decision maker matrix for these sub-criteria based upon pairwise comparisons is
The resulting weights were found and above matrix is consistent (CR = 0.00318).
We multiply these by the Level 1 weight of 0.55728387 to obtain the weights to be used in our TOPSIS model of
We followed this technique this for all Level 2 variables. We present the results only by criteria main level.
Individual Variables (CR = 0.011)
Cell Membership/Experience (CR = 0.02753)
Worldliness (CR = 0.003)
We apply the TOPSIS seven steps as described in section 2 with the data collected for our terrorists. We present our top 25 terrorist ranking in
We apply sensitivity analysis. The sensitivity analysis should be applied to the decision maker weights because they result from subjective pairwise comparison using Saaty’s 9 point process.
We used the suggested sensitivity approach suggested by Alinezhad [
where
A complete sensitivity analysis would concern each decision weight being incrementally changed and finding the range over which changes in ranking did or did not occur.
We present a side by side comparison showing the top 25 are still about the same with order adjustments. The top5 are identical and the top 10 are still the top 10 with only terrorist #42, #55, #25 having slight ranking changes as shown in
TOPSIS | Terrorist | Subjetive | Model | |
---|---|---|---|---|
Alternative | Value | # Code | Tier Rank | Rank |
22 | 0.675218 | 54 | 1 | 1 |
1 | 0.675216 | 12 | 1 | 2 |
26 | 0.54184 | 24 | 7 | 3 |
3 | 0.47225 | 53 | 4 | 4 |
24 | 0.47225 | 52 | 3 | 5 |
53 | 0.465736 | 40 | 7 | 6 |
65 | 0.388934 | 5 | 4 | 7 |
23 | 0.348206 | 3 | 3 | 8 |
42 | 0.331119 | 33 | 46 | 9 |
45 | 0.326806 | 90 | 65 | 10 |
2 | 0.318377 | 91 | 3 | 11 |
25 | 0.305574 | 50 | 6 | 12 |
55 | 0.288408 | 97 | 47 | 13 |
49 | 0.255626 | 23 | 40 | 14 |
63 | 0.1955147 | 16 | 62 | 15 |
40 | 0.192414 | 25 | 23 | 16 |
60 | 0.185771 | 30 | 52 | 17 |
34 | 0.180796 | 19 | 15 | 18 |
30 | 0.154171 | 6 | 11 | 19 |
18 | 0.137166 | 58 | 26 | 20 |
41 | 0.132053 | 27 | 25 | 21 |
10 | 0.10009 | 7 | 17 | 22 |
59 | 0.097761 | 15 | 51 | 23 |
21 | 0.088592 | 56 | 31 | 24 |
33 | 0.087089 | 103 | 14 | 25 |
Terrorist | Sensitivity Analysis | ||
---|---|---|---|
# Code | Rank | TOPSIS | Rank |
54 | 1 | 54 | 1 |
12 | 2 | 12 | 2 |
24 | 3 | 24 | 3 |
53 | 4 | 53 | 4 |
52 | 5 | 52 | 5 |
40 | 6 | 3 | 6 |
5 | 7 | 90 | 7 |
3 | 8 | 91 | 8 |
33 | 9 | 40 | 9 |
90 | 10 | 5 | 10 |
91 | 11 | 33 | 11 |
50 | 12 | 50 | 12 |
97 | 13 | 97 | 13 |
23 | 14 | 23 | 14 |
16 | 15 | 25 | 15 |
25 | 16 | 16 | 16 |
30 | 17 | 30 | 17 |
19 | 18 | 19 | 18 |
6 | 19 | 15 | 19 |
58 | 20 | 58 | 20 |
27 | 21 | 6 | 21 |
7 | 22 | 99 | 22 |
15 | 23 | 98 | 23 |
56 | 24 | 27 | 24 |
103 | 25 | 77 | 25 |
This does indicate the model results are sensitive to the decision maker’s pairwise comparisons that are used to find the decision maker weights.
Based on our analysis, we see substantial benefits of applying our methodology to ordering the targeting of terrorist. However, since our MADM research was primarily focused on explaining and demonstrating this methodology, we first recommend that additional research be conducted in the form of applying this methodology to an active target set that can serve as a further proof of concept. Once our methodology can be verified and validated, we recommend integration into the targeting process of both counter-terrorist focused units and the larger force. We provide a conceptual framework for developing decision support tools for all types of decision problems beyond just the target prioritization problem. We envision an eventual suite of decision support tools and larger decision support systems to assist decision makers with a wide range of problems.
This process provides leadership at all levels with a methodology to produce a key target list among terrorist and terrorist organizations based upon quantitative analysis. We feel that having a quantitative process is better than either a totally subjective approach or a linear regression modeling approach offered by Twedell and Edmond’s research.