Readcase 63 Electronic Timing System For Olympics Pag 864930

Readcase 63 Electronic Timing System For Olympicson Pages 275 276 Of

Read case 63: Electronic Timing System for Olympics on pages 275-276 of the textbook. For this assignment, you will assess and use the correct support tool to develop a decision tree as described in Part “a” of Case 6.3. Analyze and apply the best decision-making process to provide answers and brief explanations for parts “a”, “b”, “c”, and “d”. The answers and explanations can be placed in the same Excel document as the decision tree. Develop a decision tree that can be used to solve Chang’s problem.

You can assume in this part of the problem that she is using EMV (of her net profit) as a decision criterion. Build the tree so that she can enter any values for p1, p2, and p3 (in input cells) and automatically see her optimal EMV and optimal strategy from the tree. If p2 = 0.8 and p3 = 0.1, what value of p1 makes Chang indifferent between abandoning the project and going ahead with it? How much would Chang benefit if she knew for certain that the Olympic organization would guarantee her the contract? (This guarantee would be in force only if she were successful in developing the product.) Assume p1 = 0.4, p2 = 0.8, and p3 = 0.1.

Suppose now that this is a relatively big project for Chang. Therefore, she decides to use expected utility as her criterion, with an exponential utility function. Using some trial and error, see which risk tolerance changes her initial decision from “go ahead” to “abandon” when p1 = 0.4, p2 = 0.8, and p3 = 0.1. In your Excel document, develop a decision tree using the most appropriate support tool as described in Part a. Calculate the value of p1 as described in Part b. Show calculations. Calculate the possible profit using the most appropriate support tool as described in Part c. Show calculations. Calculate risk tolerance as described in Part d. Show calculations.

Paper For Above instruction

Sarah Chang faces a strategic decision that involves significant uncertainty regarding her company's ability to secure a lucrative contract for an electronic timing system for the upcoming Olympic Games. The decision-making process in such a scenario necessitates a comprehensive analysis of possible outcomes, associated probabilities, and the utility or value derived from each potential result. Employing decision trees and expected utility theory provides a systematic approach to evaluate options, incorporate risk preferences, and determine the most advantageous course of action.

To develop an effective decision model, the first step is constructing a decision tree that encapsulates all possible choices and their consequences. In Chang's context, the primary decision is whether to proceed with the project or abandon it. Proceeding involves subsequent branches: success in developing the microprocessor, which influences the likelihood of winning the contract, and the potential for an alternative inferior system to still secure the project if development fails. The probabilities associated with these outcomes include p1 (success in microprocessor development), p2 (chance of winning with the microprocessor if successful), and p3 (chance of winning with an inferior system if development fails).

Utilizing economic decision rules, the Expected Monetary Value (EMV) provides a quantitative basis for decision-making. EMV calculations involve multiplying the profit or payoff of each outcome by its probability and summing these across all pathways. By constructing a flexible decision tree in software such as Excel, Chang can input different values for p1, p2, and p3, instantly seeing the optimal decision and expected value. This dynamic model offers valuable insights into how variations in probabilities influence her strategy, including identifying the critical p1 value where Chang remains indifferent between pursuing or abandoning the project. Based on the given probabilities (p2=0.8, p3=0.1), the calculation of this indifference point guides her understanding of under what success probabilities she should commit to the project.

Furthermore, the scenario emphasizes the value of certainty. If the Olympic organization guarantees the contract, Chang would benefit significantly, with the expected value rising to the full payoff of $1 million, adjusted for her success probability. Quantifying this benefit involves comparing the EMV under certainty with the probabilistic EMV, highlighting the value of eliminating risk in decision-making.

Recognizing that projects of this magnitude entail risk, Chang considers her risk tolerance via utility theory. Adopting an exponential utility function, she demonstrates risk preferences by varying her degree of risk tolerance. Using trial and error, she identifies the risk tolerance level at which her decision shifts from "go ahead" to "abandon," reflecting her attitude toward risk and reward trade-offs. This personalized approach ensures her decision aligns with her risk appetite, especially in high-stakes contexts like Olympic contract bidding.

The decision tree and utility analysis collectively inform Chang's strategic choices, illustrating the importance of probabilistic evaluation, utility theory, and decision support tools in complex business decisions. These methods enable managers to incorporate uncertainty and risk preference, ensuring more informed and resilient strategic planning.

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