Decision making environment
- 1. OPERATION RESEARCH DECISION MAKING ENVIRONMENT SUBMITTED TO :- PARESH ANDHARIYA
- 2. SUBMITTED BY :- Vaghela Shubham Department of business administration M k Bhavnagar university Bhavnagar
- 3. 1.Introduction 2.type of decision making environment i. decision making under certainty ii. decision making under uncertainty iii. decision making under risk 3. decision tree 4.Bayesian analysis
- 4. DECISION MAKING ENVIRONMENT Introduction History Meaning Definition Explanation Example Concept Objective
- 5. INTRODUCTION DECISION MAKING PROCESS :- 1. Recognizing & defining the situation 2. Identifying the alternatives 3. Evaluating the alternatives 4. Apply the model 5. Selecting the best alternatives 6. Conduct a sensitivity of the solution 7. Implementing the chosen alternatives 8. Following up & evaluating the result
- 6. TYPE OF DECISION MAKING ENVIRONMENT Decision making under certainty Decision making under uncertainty Decision making under risk
- 8. DECISION MAKING UNDER CERTANITY Assume that complete knowledge is available ( deterministic environment ) Example U.S. treasury bill investment Typical for structured problem with short time horizons Some time DSS approach is needed for certainty situation
- 9. there is only one type of event that can take place. It is very difficult to find complete certainty in most of the business decisions. in many routine type of decisions, almost complete certainty can be noticed. In uncertainty the decision are of very little significance to the success of business.
- 11. The decision maker is not in a position, even to assign the probabilities of happening of the events. In the environment of uncertainty, more than one type of event can take place and the decision maker is completely in dark regarding the event that is likely to take place. Such situations generally arise in cases where happening of the event is determined by external factors. For example, demand for the product, moves of competitors, etc. are the factors that involve uncertainty.
- 12. MAXIMAX CRITERION OR CRITERION OF OPTIMISM : This criterion provides the decision maker with optimistic criterion. The working method is summarizing as follow. Locate the maximum payoff value corresponding to each alternative, then select an alternative with maximum payoff value.
- 13. MAXIMIN CRITERION OR CRITERION OF PESSIMISM : This criterion provides the decision maker with pessimistic criterion. The working method is summarizing as follow. Locate the minimum payoff value corresponding to each alternative, then select an alternative with maximum payoff value
- 14. MINIMAX CRITERION OR MINIMUM REGRET CRITERION : This criterion is also known as opportunity loss decision criterion or minimum regret criterion. The working method is summarizing as follow. Determine the amount of regret corresponding to each state of nature. Regret for jth event corresponding to ith alternative is given by Ith regret = (maximum payoff- Ith payoff) for the Jth event Determine the maximum regret amount for each alternative. Choose the alternative which corresponding to the minimum of the maximum regrets.
- 17. DECISION MAKING UNDER RISK Here more then one state of nature exists and the decision maker has sufficient information to assign probabilities to each of this states. These probabilities could be obtained from the past records and simply the subjective judgment of the decision maker. Under condition of risk, knowing the probability distribution of the state of nature, the best decision is to select the course of action which has the largest expected pay off value.
- 18. Under the condition of risk, there are more than one possible events that can take place. The decision maker has adequate information to assign probability to the happening or non- happening of each possible event. Such information is generally based on the past experience. Every decision in a modern business enterprise is based on interplay of a number of factors. New tools of analysis of such decision making situations are being developed. These tools include risk analysis, decision trees and preference theory.
- 21. EXPECTED OPPORTUNITY LOSS CRITERION : EOL represent the amount by which maximum possible profit will be reduced under various possible stock actions. The course of action that minimizes these losses of reductions is the optimal decision alternative. The procedure the calculate expected opportunity losses is as follow. Prepare the conditional profit table for each decision event combination and write associated probabilities. Foe each event determine the conditional opportunity loss (COL) by subtracting the payoff from the maximum payoff for that event. Calculate the expected opportunity loss for each decision alternative by multiplying the COL’s by the associated probabilities and then adding the value. Select the alternative that yields the lowest EOL.
- 23. DECISION TREE : Instances describable by attribute-value pairs e.g Humidity: High, Normal Target function is discrete valued e.g Play tennis; Yes, No Disjunctive hypothesis may be required e.g Outlook=Sunny Wind=Weak Possibly noisy training data Missing attribute values Application Examples: Medical diagnosis Credit risk analysis Object classification for robot manipulator (Tan 1993)
- 24. TOP-DOWN INDUCTION OF DECISION TREES ID3 1. A the “best” decision attribute for next node 2. Assign A as decision attribute for node 3. For each value of A create new descendant 4. Sort training examples to leaf node according to the attribute value of the branch 5. If all training examples are perfectly classified (same value of target attribute) stop, else iterate over new leaf nodes.
- 25. BAYESIAN ANALYSIS : ( | ) ( ) ( | ) ( ) j j j p x P P x p x Suppose the priors P(wj) and conditional densities p(x|wj) are known,
- 26. UTILITY THEORY : Step for determine the utility for money : 1. Develop a payoff table using monetary values 2. Identify the best and worst payoff value 3. For every other monetary value in the original payoff table 4. Convert the payoff table from monetary value to calculate utility value. 5. Apply the expected utility criterion to the utility table and select the decision alternative with the best expected utility.
Editor's Notes
- #2: introduction