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The final project is intended to complete a risk analysis on a reasonably realistic case study. The case study will be in regard to transnational drug trafficking, more specifically highlighting it in the United States.
Tips for a high-quality risk report:
Provide a thorough description of the risk. Consider what you learned in Module 1 that risk is uncertainty of damage. A good report will be begin by describing what could go wrong, how frequent the triggering events might happen, what is uncertain, what damage could be caused, what decisions/alternatives are available to manage risk, what are the tradeoffs (or expected tradeoffs) between alternatives, and why is the decision difficult to make in light of the uncertainty. Try to be specific.
Try to focus on the decision. Risk analysis is for the purpose of supporting the process of making decisions despite uncertainty. The analytic objective for a risk paper should be to evaluation alternatives or support a decision. For example, the risk problem “should we start using copper beds in hospitals to reduce infections?” is probably a better risk problem for a report than “what is the risk of secondary infections in hospitals?”. Another example, the risk problem “should we spend limited border protection resources on surveillance or inspections to reduce drug trafficking” is better than “what is the risk of drugs crossing US borders.”
Provide a map of your overall methodological approach. You don’t need to reuse or republish all of your Ongoing Projects, but you should use what makes sense. For large-scale problems, thoughtful application of RFRM with a focus on risk management can be useful. For almost all problems, some version of decision trees are useful. Provide a map towards the beginning of the paper of how the different methods you use contribute to informing the problem and how are the methods connected to yield insight into some decision.
Use a common mental model to connect the different parts of your paper. Each system has some underlying processes, states, connections, or dependencies that help you reason through what the tradeoffs between various decision alternatives. Try to map out the mental model with a graphic or a table. For example, if you are trying to decide between methods to protect federal builds from unmanned aircraft your mental model for the cost of the alternative might include training, equipment, scale of application across buildings, etc. your mental model for the effectiveness of the approach might to protect against reconnaissance, wifi spoofing, payload delivery (explosive or chem/bio/rad), kinetic attack, and general operational distraction/disruption. This mental model provides a unified way then to use different methods to explore different aspects of the problem, while still helping to communicate how the different aspects of the problem are connected to the decision. For example, this mental model could be used to compare netguns, lasers, EM monitoring, high-res radar, or other alternatives for protecting federal buildings from unmanned aircraft.
Use evidence. Each quotation, sentence that contains numbers, proposition, etc. should be backed up with a reference to a source. You can introduce artificialities for this class project (feel free to be explicit about those artificialities), but make sure that they are anchored in something that you can reference. Use only public / open-source information. Feel free to search on scholar.google.com for rigorous/academic papers that provide background, details, and evidence. Try to find multiple sources for every major proposition. Discuss and critically analyze the evidence for bias, uncertainty, and applicability. Contradictory evidence is great for risk analysts because it helps to clearly show uncertainty and communicate why uncertainty is confounding the ability to make easy decisions. Each report should cite more than 20 sources, which could include materials used in this module, academic papers, news publications/investigations, reputable blogs, data sources, and other trustworthy sources.
Lots of discussion and analysis. Don’t stop with the application of the methods and the computational results. Explain the results, elaborate on what they mean, elaborate on the limitations/assumptions, connect your results to results published in other studies, explore reasons for agreement/disagreement, explore tradeoffs, explain the values of decision makers and how those values might make them prefer certain tradeoffs over others, make/defend recommendations, etc. When you use outside data, sources, analyses, be sure to explain the results, analyze their quality, compare them against each other. Be critical and verbose in the analysis.
Suggested Outline for Final Project:
Cover Page that includes: project title, course title and semester, name of students and instructors, and perhaps a relevant image.
Executive Summary that includes: description of the problem, a summary of the approach(es), a summary of key results and findings, and a summary of conclusions and recommendations (should be approx. 1 page of summary for every 20 pages of text in the main document, images/tables/etc that help summarize are encouraged for the summary) Ideally, your project will only be 20 pages (with details relegated to the appendices), so hopefully, your executive summary will only need to be one or two pages.
Problem Definition that describes why the study is needed and potential impacts of insight into the problem
Project Objectives that describes both the student’s objectives and study objectives
Methodology and Approach that describes the methodologies and approaches employed in the study
Risk Model Description that describes a high-level risk model of the system under study. Perhaps this will use some sort of block diagram to break down what are the pieces, how will the data be collected or elicited, how are those features combined for an output. This section may also use HHM and RFRM that describes multiple perspectives and scales of the problem and defends specific focus areas and model components.
Data Used that summarizes key data sources, elicitation method, calibration results, and the effort devoted to data collection or elicitation
Project Tasks and Contributions that describes how the methodology and the approach was executed and the details about why the approach was tailored and executed in a specific way. This might might multiple subsections for the various methodologies, tools, and techniques that were applied.
Computational Results that describes the outcomes from project tasks and the application of various methodologies.
Analysis of Results that interprets the results in the context of the problem, the data sources, the elicitation techniques and the assumptions/shortcomings of the methodologies. This section should also describe recommendations and provide some discussion of those recommendations.
Key Findings, Conclusions, and Recommendations that summarizes key findings, draws conclusions from those findings as it relates to the problem and summarizes recommendations.
References (recommend 20 or more references to help ground your paper in evidence)
Appendices (as appropriate to provide details, charts, code, equations, calculations, etc… that distract from the readability of the text, but may be important for defensibility of the results and analyses to an interested reader).
You will receive a grade of 0-100 which will reflect the quality of your paper. See the detailed rubric based on quality of your risk description, clarity of the analysis objective, description and adequacy of your methodological approach, your description and consistent use of a mental or computational model of your system, the range and correct application of risk methods, the adequacy of evidence or data used, the thoroughness and specificity of discussion and analysis of your results, the clarity of your conclusions, and the overall writing quality.
Page 2 of 3 in Module 10
Final Project Rubric
Final Project Rubric
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeProblem Definition (12 points)on of criterion
This criterion is linked to a Learning OutcomeObjectives (study objectives) (8 points)
This criterion is linked to a Learning OutcomeMethodological Approach (12 points)
This criterion is linked to a Learning OutcomeModeling Quality/Effort (10 points)
This criterion is linked to a Learning OutcomeRange/Diversity of Methods or Tools Applied (10 points)
This criterion is linked to a Learning OutcomeCorrect Application of Methodologies (10 points)
This criterion is linked to a Learning OutcomeEffort of data collection (8 points)
This criterion is linked to a Learning OutcomeAnalysis of Results (12 points)
This criterion is linked to a Learning OutcomeConclusion (8 points)
This criterion is linked to a Learning OutcomeGeneral Quality of Writing and Completeness of Articulation (10 points)
Total Points: 100
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