Strategic Economic Decision Making

Abstract  This chapter provides an introduction to BBN experimental protocol, the experimental protocol for BBN, the  characteristics of a random experiment, and the conduct a Bayesian experiment, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication.[1]

Abstract This chapter highlights an example of a Bayesian Belief Network (BBN) in a manufacturing scenario by evaluating the variables of “Transistor Quality” and “Suppliers.” Here, quality control and costs have great utility in the company’s ability to make a profit, gain a competitive advantage, and maintain their reputation as an industry leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario. 

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a national economic scenario by evaluating the variables of “County” and “Political Affiliation.” Here, the balance between Democrats and Republicans has great utility in a politician’s ability to determine her or his position in the political arena to maintain their reputation as a government servant. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a gaming scenario by evaluating the variables of “Die Randomness” and “Fair Die.” Here, the balance between winning and losing has great utility in a casino’s ability to remain profitable while hedging risk to maintain their reputation as a gaming establishment. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a national economic scenario by evaluating the variables of “Altman Z-Scores” and “Health Status.” Here, the balance between international company default and investments has great economic utility in a country’s ability to warn its international investors and to hedge global effects of default to maintain their reputation as a global financial leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in an insurance scenario by evaluating the variables of “Risk Category” and “Fatality Status.” Here, the balance between risk and premiums have great economic utility in the company’s ability to make a profit, gain a competitive advantage, and maintain their reputation as an industry leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a potential hostile scenario by evaluating the variables of “Country” and “Fatality Status.” Here, citizen safety has great economic utility in a person’s ability to maintain safety while living and traveling abroad in countries with terrorist cells who desire to launch their economic and political agenda on innocent citizens. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.