MANSCIE-WEEK5

• Decision Analysis a method for reducing uncertainty in the decision-making process through data analysis
• Decision Theory is an analytic and systematic approach to the study of decision-making
• Decision-Making is the process of making choices. It is about identifying the problem, gathering data, and analyzing options. 

  Note: 

  • Good vs. Bad Decisions
  • Logic
  • Reliable Information
  • Systematic Approach
• Associated Terminologies

  1. Alternative is a course of action available
  2. State of Nature is a factor over which the decision maker has no control
  3. Payoff is the value of an alternative for every state of nature, it could be profit, cost, distance, time, etc.
• 6 Steps in Decision-Making

  1. Define the problem
  2. List the possible alternatives
  3. Identify the states of nature
  4. List the payoffs
  5. Select a mathematical decision theory model
  6. Apply the model and make your decision
• Decision-Making Environments

  1. Decision-making under certainty
  2. Decision-making under risk
  3. Decision-making under uncertainty
• Decision-Making Environments

  • Decision-making under certainty is the condition of certainty that arises when there is sufficient information to make a sound decision.
• Decision-Making Environments

  • Decision-making under risk arises when there is incomplete or lack of perfect information but idea of the probability of outcomes for each alternative is present. Here the outcome of each alternative is unknown
• Decision-Making Environments

  • Decision-making under uncertainty exists when the future environment is unpredictable and factors are in a state of flux. Outcome and likelihood of events are unknown.
• Decision-making without probabilities is often possible to assign probabilities to the states of nature to aid the decision maker in selecting the decision that has the best outcome. However, in some cases the decision maker is not able to assign probabilities.
• Decision-Making Criteria

  1. The Maximax Criterion
  2. The Maximin Criterion
  3. The Hurwicz Criterion
  4. The Minimax Regret Criterion
  5. The Equal Likelihood Criterion
• Maximax Criterion is also known as Optimistic Criterion. In this optimistic approach, the decision maker selects the decision that will result in the maximum of the maximum payoffs. The decision maker assumes that the most favorable state of nature for each decision alternative will occur.
• Maximin Criterion is also known as Pessimistic Criterion. In using the pessimistic approach, the minimum payoff for each alternative is considered, and the alternative with the maximum of these is selected. This criterion guarantees the payoff will be at least the maximin value. Choosing any other alternative may allow a worse (lower) payoff to occur.
• Hurwicz Criterion is also known as criterion of realism. It is often called the Weighted Average or Criterion of Rationality, this is a combination of optimistic and pessimistic decisions. The alternative with the highest weighted average is selected. With the Hurwicz criterion, the decision payoffs are weighted by a Coefficient of Optimism, a measure of the decision maker’s optimism
• Likelihood Criterion is also kown as Laplace Criterion. This criterion uses all the payoff for each alternative. This involves finding the average payoff for each alternative. This involves finding the average payoff for each alternative and selecting the alternative with the best or highest average. The assumption is that the probability of occurrence of each state of nature is equally likely. 
• Minimax Regret Criteria is based on Opportunity Loss or Regret. 

  • Opportunity loss refers to the difference between the optimal profit or payoff for a given state of nature and the actual payoff received for a particular decision for that state of nature. 
  • Regret refers to the amount you give up for not picking the best alternative in a given state of nature.
• A Dominant Decision is one that has a better payoff than another decision under each state of nature. 
• Application to Accounting: 

  1. Budgeting and Planning 
  2. Investment Decision
  3. Resource Allocation 
  4. Risk Management
• Application to Accounting: 

  • Budgeting and Planning, accountants often need to create budget and financial plans based on uncertain future revenues, expenses, and market conditions.
• Application to Accounting: 

  • Investment Decision is used when considering investment in new projects, equipment in new projects, equipment, or technologies, accountants must evaluate potential returns and risks.
• Application to Accounting: 

  • Resource Allocation, uncertainty affects resource allocation decisions by influencing the expected returns and risks associated with different investment opportunities. 
• Application to Accounting: 

  • Risk Management, accountants play a role in identifying, measuring, and managing risks within an organization. 
• Causes of Uncertainty in Philippine Economy

  1. Economic Volatility 
  2. Political & Regulatory Environment 
  3. Market Dynamics
  4. Infrastructure and Logistic Challenges
• Causes of Uncertainty in Philippine Economy

  • Economic Volatility in the Philippine economy is subject to fluctuations influenced by global economic trends, domestic policies, and external factors. 
• Causes of Uncertainty in Philippine Economy

  • Political & Regulatory Environment is the political instability and regulatory changes can significantly impact business operations and investment decisions.
• Causes of Uncertainty in Philippine Economy

  • Market Dynamics is the market uncertainties manifest in shifting demand patterns, disruptive innovations, and evolving competitive landscapes. 
• Causes of Uncertainty in Philippine Economy

  • Infrastructure and Logistic Challenges, the inadequate transportation networks, port congestion, and energy shortages disrupt supply chains, hinder productivity, and escalate costs. 
• Decision Making Under Uncertainty

  1. Resource Allocation
  2. Choosing Supplier 
  3. Strategic Location
  4. Maximization and Minimization Problems
• Decision Tree in decision analysis is a graphical representation of decisions and their potential consequences, typically used to evaluate complex decision problems under uncertainty. 
• Steps in Making a Decision Tree

  • Identify the decision
  • List decision alternatives
  • Identify uncertain events
  • Assign probabilities
  • Estimate payoffs
  • Construct the tree
  • Calculate expected values
  • Analyze and compare
  • Sensitivity Analysis
  • Make the decision
• Identify the decision: Define the decision problem you want to analyze
• List decision alternatives: Identify the different alternatives or choices available at the decision point
• Identify uncertain events: Determine the uncertain events or factors that could influence the outcome of each decision alternative
• Assign probabilities: Assign probabilities to each possible outcome of the uncertain events
• Estimate payoffs: Determine the payoffs or consequences associated with each combination of decision alternative and uncertain event outcome
• Construct the tree: Draw the decision tree based on the decision alternatives, uncertain events, probabilities, and payoffs
• Calculate expected values: Calculate the expected value for each decision alternative by multiplying the probability of each outcome by its associated payoff and summing the results
• Analyze and compare: Analyze the decision tree to identify the optimal decision strategy
• Sensitivity Analysis: Conduct sensitivity analysis to access the robustness of the optimal decision strategy to changes in probabilities or payoffs
• Make the decision: Based on the analysis of the decision tree and sensitivity analysis results, make an informed decision on the best course of action to take