Complete the following assignment in one MS word document: Chapter 8 –discussion question #1-4 & exercise 4 When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week. All work must be original (not copied from any source). 1. How does prescriptive analytics relate to descriptive and predictive analytics? 2. Explain the differences between static and dynamic models. How can one evolve into the other? 3. What is the difference between an optimistic approach and a pessimistic approach to decision-making under assumed uncertainty? 4. Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved  under conditions of risk exercise 4: Investigate via a Web search how models and their  solutions are used by the U.S. Department of Homeland  Security in the “war against terrorism.” Also, investigate how other governments or government agencies are using models in their missions.

Chapter 8 of our textbook delves into the topic of prescriptive analytics, and in this assignment, we will explore the relationship between prescriptive analytics, descriptive analytics, and predictive analytics. Additionally, we will discuss the differences between static and dynamic models and how they can evolve into one another. We will also examine the distinctions between an optimistic and pessimistic approach to decision-making under assumed uncertainty. Lastly, we will investigate the use of models and their solutions by the U.S. Department of Homeland Security in the “war against terrorism,” as well as how other governments or government agencies utilize models in their missions.

Prescriptive analytics is a branch of analytics that focuses on recommending the best course of action to achieve a particular outcome. It leverages both descriptive analytics, which involves analyzing historical data to understand past events, and predictive analytics, which uses statistical models to forecast future events. By combining the insights derived from descriptive and predictive analytics, prescriptive analytics provides actionable recommendations for decision-making.

Static models, as the name suggests, remain unchanged over time and do not consider the influence of time or other dynamic factors. They represent a snapshot of a system at a specific point in time. On the other hand, dynamic models take into account the dynamic nature of a system and incorporate time as a variable. Dynamic models capture the changes and interactions within a system as time progresses. Static models can be transformed into dynamic models by introducing time-dependent variables and incorporating the concept of change over time.

In decision-making under assumed uncertainty, an optimistic approach and a pessimistic approach represent two contrasting mindsets. An optimistic approach tends to assume favorable outcomes and focuses on the best possible scenario, even when facing uncertainty. This approach is characterized by choosing actions that maximize potential gains. Conversely, a pessimistic approach assumes unfavorable outcomes and focuses on the worst-case scenario. It aims to minimize potential losses and regrets. Both approaches have their merits and implications, and the choice between them depends on the decision-maker’s risk appetite and attitude towards uncertainty.

Solving problems under uncertainty often involves assuming that the problem is to be solved under conditions of risk. Risk refers to situations where the appropriate probabilities can be assigned to possible outcomes. By assuming risk, decision-makers can employ various techniques such as expected monetary value analysis and decision trees to quantify and evaluate different options. This enables them to make informed decisions based on expected values and probabilities, even in the absence of complete information.

Turning our attention to the application of models in addressing the challenges of national security, the U.S. Department of Homeland Security extensively utilizes models and their solutions in the “war against terrorism.” Through advanced analytics techniques, models are employed to predict and prevent potential threats, optimize resource allocation, and enhance situational awareness. These models analyze vast amounts of data from various sources, such as intelligence reports, social media, and surveillance systems, to provide actionable insights for decision-makers.

Similarly, other governments or government agencies around the world utilize models in their missions. These models are used in diverse areas, including disaster response planning, policy formulation, economic forecasting, and public health management. Models enable governments to make evidence-based decisions, allocate resources effectively, and mitigate risks. They provide a framework for analyzing complex systems and understanding their behavior, facilitating better decision-making at various levels of government.

In conclusion, prescriptive analytics builds upon descriptive and predictive analytics to provide actionable recommendations. Static models represent a snapshot of a system at a specific point in time, while dynamic models incorporate time and changing factors. Optimistic and pessimistic approaches offer different perspectives on decision-making under assumed uncertainty. Problem-solving under uncertainty often involves assuming risk and using techniques to quantify and evaluate options. The U.S. Department of Homeland Security and other governments use models and their solutions in various contexts to enhance national security and support decision-making processes.