Introduction to Decision Analysis<br />The field of decision analysis provides a framework for making important decisions.<br />Decision analysis allows us to select a decision from a set of possible decision alternatives when uncertainties regarding the future exist.<br />The goal is to optimize the resulting payoff in terms of a decision criterion. <br />Slide No.3<br />
What Mean to Decision Making?<br /> Decision making can be defines as:<br />A mental processes (cognitive process) resulting in the selection of an alternative among several alternative. <br />OR in simple worlds decision making is a process in which we Choosing an best alternative from a set of alternatives.<br />Every decision making process produces a final choice.<br />Slide No.4<br />
Decision Making :Uncertainty & Risk In decision making<br />A decision can be for short to intermediate as well as to long term. We also know that decision are made for the future and future contain uncertainty.<br />Many decision tools have been develop to solve the (problem of ) uncertainty in decision.<br />These tools form an important component of discipline Called decision analysis.<br />Slide No.5<br />
Our discussion in this presentation:<br /><ul><li> Decision criteria for one time decision involving uncertainty
Decision Tree to use for Sequential Decisions under uncertainty.</li></ul>Slide No.6<br />
Decision Making:<br />Normally in decision we have two or more then two alternative from which to be choose one.<br />The consequence of the alternative depends upon the outcomes of some future random event ( or states of nature)<br />Outcomes of such decision are usually unexpected but possible to predict by information carry chosen one alternative.<br />Slide No.7<br />
Criteria For Decision Making :<br /> If a decision maker carryout information about an alternative upon it become easy to takes decision. <br /> Like converting available information into a measure of desirability.<br /> An appropriate one criteria for decision can be:<br /> => Probability estimates of future outcomes.<br />=> And willingness of decision maker to take risk.<br />Slide No.8<br />
Decision Making Under Uncertainty:<br />The decision when are made in situation in which simply decision maker know what could occurs ,but have no idea for the probability of each outcome occurring. <br />Decision that are made under above describe situation are called “Decision Making under uncertainty”<br />Slide No.9<br />
Decision Criteria For Decision Under Uncertainty:<br /> Criteria's for decision under uncertainty:<br />Maximax:<br /> Determine the best possible payoff for each alternative and then select that one.<br />Maximin: <br /> Determine the worst possible payoff that can occur for alternative, then select them by looking to the worst thing that can occur with each alternative and try to minimize the bad that can result.<br />Slide No.10<br />
Continue…..<br />Laplace:<br /> Select the alternative that has the average payoff. This criterion treats all feature outcomes as being equally likely.<br />Minimax regret:<br /> Computed the regret for each payoff, which is the difference that payoff and the payoff that would have occurred if we have choose the best alternative. Then select the alternative that carry smallest regret value.<br />Slide No.11<br />
Decision Making & Risk<br />When the decision maker have the probability estimate for the future events occurrence.<br />Then decision under such situation are term as decision making under risk.<br />Slide No.12<br />
Decision Criteria For,Decision Under Risk:<br />Decision criteria for such situation are usage of information that posses the decision maker about an alternative. <br />And then selecting that alternative on the basis of information carry by alternative.<br />Slide No.13<br />
Sequential Decision & Decision Tree<br /><ul><li>In some decision a decision maker doesn’t simply have to make a single decision at one point at time, rather then single, serious of decision are take to make a decision.
A special tool for such situation is used called Decision tree.</li></ul>Slide No.14<br />
Slide No.15<br />Decision Tree:Meaning And Usage<br />decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.<br />Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal.<br />
Elements In a Decision Tree<br /> A decision Tree consists of 3 types of nodes(Path):-<br /> 1. Decision nodes - commonly represented by squares2. Chance nodes - represented by circles3. End nodes - represented by triangles<br />Slide No.16<br />
Slide No.17<br /> Diagram Shows Element in Decision Tree<br />
Thanks to all for giving concentration.<br />question & answer secession<br />Slide No.18<br />
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