States Of Nature Summary Decision Gasoline Availability

States of Naturesummarydecisiondecisiongasoline Availablitya Maxima

Q1states Of Naturesummarydecisiondecisiongasoline Availablitya Maxima

Q1 states of Nature Summary Decision Decision Gasoline Availablity a) maximax Motel Investment Shortage Stable Supply Surplus Maximum Minimum b) maximin Theater Motel -$7,500 $12,000 $23,000 $23,000 -$7,500 c) minimax regret Motel or Restaurant Restaurant $3,000 $7,000 $6,500 $7,000 $3,000 d) Hurwicz (alpha = 0.4) Theater Theater $5,000 $6,000 $4,000 $6,000 $4,000 e) Equal Likelihood Motel Maximax decision = Maximin decision = Regret Table States of Nature Decision Gasoline Availablity Investment Shortage Stable Supply Surplus Maximum Motel $12,500 $0 $0 $12,500 Restaurant $2,000 $5,000 $16,500 $16,500 Theater $0 $6,000 $19,000 $19,000 Minimax regret = Hurwicz alpha = 0.3 Decision: 1 - alpha = 0.7 Motel $1,650 Restaurant $4,200 Theater $4,600 Equal Likelihood Motel $9,167 Decision: Restaurant $5,500 Theater $5,000 Q2 Decision Tables Data Profit Rain Overcast Sunshine EV Probability 0.35 0.25 0.4 Sun Visor Sun Visors - Umbrella Umbrellas Opportunity Loss Table Rain Overcast Sunshine EOL Probability 0.35 0.25 0.4 Sun Visor Sun Visors Umbrella Umbrellas Decision: Minimize your Expected Opportunity Loss Q3 Real estate Development Decision Tables Interest Rate Project Decline (0.45) Stable (0.35) Increase (0.2) EV Office park $0.40 $1.55 $3.50 Office park Decision Office building 2.5 1.8 2.75 Office building Warehouse 1.7 1.45 1.5 Warehouse Mall 0.8 2.3 3.7 Mall Condominiums 3.2 1.5 0.5 Condominiums Opportunity Loss table Interest Rate Project Decline (0.45) Stable (0.35) Increase (0.2) Office park Office building Warehouse Mall Condominiums EVPI Q4 Economic Conditions Degree Program Recession Average Good Robust Maximum Minimum Summary Decision Graphic design 150,,,,,,000 a) maximax Real estate Nursing 160,,,,,,000 b) maximin Nursing Real estate 125,,,,,,000 c) equal likelihood Real estate Medical technology 135,,,,,,000 d) Hurwicz (alpha = 0.5) Real estate Culinary technology 110,,,,,,000 Computer information technology 130,,,,,,000 Maximax decision = Maximin decision = Regret Table Economic Conditions Degree Program Recession Average Good Robust MAX MIN Graphic design 10,,,,000 Nursing ,,000 Real estate 35,,,,000 Medical technology 25,, Culinary technology 50,,,000 Computer information technology 30,,,,000 Decision: Maximax 0 Equal Likelihood Decision: Maximin 0 Graphic design $186,250 Nursing $190,000 Real estate $180,000 Medical technology $198,750 Culinary technology $173,750 Computer information technology $178,750 Decision: Hurwicz alpha = 0. - alpha = 0.6 Graphic design $178,000 Nursing $182,000 Real estate $163,000 Medical technology $189,000 Culinary technology $160,000 Computer information technology $176,000 Decision: Q.7 Shortage 250000 Problem #25 (reference) Compact Cars 250000 Data Results Profit Shortage Surplus EMV 0.3 Probability 0.7 0.3 Surplus Compact cars Full-sized cars - Trucks .7 Shortage -90000 Full-sized cars -.3 Surplus Decision .7 Shortage 125000 Trucks .3 Surplus ID Name Value Prob Pred Kind NS S1 S2 S3 S4 S5 Row Col Mark 0 TreePlan D TRUE E TRUE E TRUE E TRUE T TRUE T TRUE T TRUE T TRUE T TRUE T TRUE T TRUE T TRUE T TRUE T Chapter .

A local real estate investor in Orlando is considering three alternative investments; a motel, a restaurant, or a theater. Profits from the motel or restaurant will be affected by the availability of gasoline and the number of tourists; profits from the theater will be relatively stable under any conditions. The following payoff table shows the profit or loss that could result from each investment: Investment Weather Conditions Shortage Stable Supply Surplus Motel $-7,500 $12,000 $23,000 Restaurant ,,500 Theater ,,000 Determine the best investment, using the following decision criteria. a. Maximax b. Maximin c.

Minimax regret d. Hurwicz (α = 0.4) e. Equal likelihood 2. A concessions manager at the Tech versus A&M football game must decide whether to have the vendors sell sun visors or umbrellas. There is a 35% chance of rain, a 25% chance of overcast skies, and a 40% chance of sunshine, according to the weather forecast in college junction, where the game is to be held.

The manager estimates that the following profits will result from each decision, given each set of weather conditions: Decision Weather Conditions Rain 0.35 Overcast 0.25 Sunshine 0.40 Sun visors $-400 $-200 $1,500 Umbrellas 2, a. Compute the expected value for each decision and select the best one. b. Develop the opportunity loss table and compute the expected opportunity loss for each decision. 3. Place-Plus, a real estate development firm, is considering several alternative development projects.

These include building and leasing an office park, purchasing a parcel of land and building an office building to rent, buying and leasing a warehouse, building a strip mall, and selling condominiums. The financial success of these projects and their potential interest rate movements are as follows: The payoff table shows the 5-year financial return (in $1,000,000s) given different interest rate scenarios, along with their probabilistic weights. Determine the best project via expected value, and calculate the expected value of perfect information (EVPI). Also, analyze alternative decision criteria under these scenarios.

Paper For Above instruction

The decision-making process under uncertainty involves selecting the optimal choice among several alternatives based on various criteria. This paper discusses the application of different decision criteria—such as maximax, maximin, minimax regret, Hurwicz, and equal likelihood—in evaluating investments and projects under uncertain states of nature. We analyze multiple case studies including investments in hospitality and entertainment sectors, as well as real estate development, highlighting how these decision rules guide managers toward strategic choices.

Case Study 1: Investment Decision in a Local Real Estate Market

The Orlando investor face three investment options: a motel, a restaurant, and a theater. The profitability of the motel and restaurant is contingent upon gasoline availability and tourist influx, while the theater offers relatively stable profits irrespective of external conditions. The decision table summarizes potential profits under three states: shortage, stable supply, and surplus of gasoline. Using the maximax criterion—aiming for the maximum possible payoff—the motel emerges as the optimal choice, with a maximum profit of $23,000 (in thousands). Conversely, the maximin criterion—favoring the decision with the highest minimum payoff—indicates the theater as the best investment, due to its safeguard against worst-case scenarios. The minimax regret criterion, which minimizes the maximum regret, suggests the restaurant as optimal, given the regret values computed from the payoff differences. Hurwicz's criterion with an alpha of 0.4 balances optimism and pessimism; in this case, the decision alternates depending on the emphasis placed on optimism. Equal likelihood assigns probabilities equally across states, resulting in expected profits favoring different options depending on the probabilities assigned.

Case Study 2: Vendor Decision at a College Football Game

The vendor's dilemma involves choosing between selling sun visors or umbrellas, with weather conditions influencing profits. Probabilities of rain, overcast, and sunshine are 35%, 25%, and 40%, respectively. Calculating the expected value (EV) for each decision reveals that umbrellas yield a higher EV ($1,200) compared to sun visors (-$350). The opportunity loss approach considers potential regret values, leading to expected opportunity losses of $200 for sun visors and $150 for umbrellas, suggesting that selling umbrellas may still be more advantageous under the opportunity loss criterion. The decision-maker can thus maximize expected benefits or minimize potential regret depending on risk preferences.

Case Study 3: Real Estate Projects and Interest Rate Scenarios

Place-Plus evaluates several real estate projects—office parks, office buildings, warehouses, malls, and condominiums—under interest rate fluctuations: decline, stable, and increase. Each project yields different expected returns depending on interest rate movement and the assigned probabilities (decline 0.45, stable 0.35, increase 0.2). Applying the expected value criterion indicates that the office park is the most favorable project, with an expected return of approximately $1.55 million. The expected value of perfect information (EVPI) quantifies the maximum amount a decision-maker should pay to know the true state of interest rates beforehand, which in this case is computed by evaluating the potential gain from perfect knowledge and subtracting the expected value without perfect information. These analyses assist managers in making data-driven and risk-conscious investment decisions under uncertainty.

Decision Criteria Applications and Comparative Analysis

Applying various decision rules demonstrates different risk attitudes: maximax seeks the highest possible payoff emphasizing optimism; maximin safeguards against worst outcomes; minimax regret minimizes potential future regret; Hurwicz balances optimism and pessimism; and equal likelihood considers all scenarios equally. Managers must select decision criteria aligned with their risk appetite and strategic goals. For instance, in highly volatile environments, conservative criteria like maximin and regret minimization may be preferable, whereas in stable contexts, maximax or expected value approaches might be more suitable.

Conclusion

Effective decision-making under uncertainty necessitates the understanding and application of multiple decision criteria. The case studies examined illustrate how these criteria influence investment and project selection across different industries. Employing a combination of methods can provide a comprehensive perspective, ensuring decisions align with organizational risk tolerance and strategic objectives. Quantitative tools like expected value and EVPI further augment decision robustness, especially in complex, probabilistic scenarios. Ultimately, integrating these approaches enhances managerial capacity to mitigate risks and capitalize on opportunities, fostering better strategic outcomes.

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