decision analysis

Table of content Case Study 2 1.0 Definition of Decision Tree 3 2.0 The certainty and uncertainty in decision-making environment 5 3.0 The principal steps of decision analysis 7 4.0 Decision Tree step-by-step 8 5.0 Benefits of using decision analysis 14 6.0 Summary 17 7.0 References 18 8.0 Coursework 19 Case Study Patrick Oil Company has leased the drilling rights on a large parcel of land in Tawke that may not contain an oil reserve. A competitor has offered to lease the land for $200,000 cash in return for drilling rights and all rights to any oil that might be found. The offer will expire in three days. If Patrick does not take the deal, it will be faced with the decision of whether to drill for oil on its own. Drilling costs are projected to be $400,000. The company feels that there are four possible outcomes from drilling: dry hole (no oil or natural gas) natural gas natural gas and some oil oil only If drilling yields a dry hole, the land will be basically worthless, because it is located in the badlands of Tawke. If natural gas is discovered, Patrick will recover only its drilling costs. If natural gas and some oil are discovered, revenue is projected to be $800,000. Finally, if only oil is discovered, revenues will be $1600, 000. Assignment: Describe the certainty and uncertainty in decision-making environments. Structure the decision problem as a decision tree. Solve for Patrick Oil Company’ optimal decision strategy. 1.0 Definition of Decision Tree Decision Trees are excellent tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can lay out options and investigate the possible outcomes of choosing those options. They also help you to form a balanced picture of the risks and rewards associated with each possible course of action. In another words, decision tree is a schematic tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive. Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. It is one of the predictive modeling approaches used in statistics, data mining and machine learning. More descriptive names for such tree models are classification trees or regression trees. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data but not decisions; rather the resulting classification tree can be an input for decision making. In my opinion, decision trees provide an effective method of Decision Making because they are clearly lay out the problem so that all options can be challenged. Besides that, decision trees allow us to analyze fully the possible consequences of a decision. It also provides a framework to quantify the values of outcomes and the probabilities of achieving them. Last but not lease, decision tress help us to make the best decisions on the basis of existing information and best guesses. As with all Decision Making methods, decision tree analysis should be used in conjunction with common sense. This is why decision trees are just one important part of your Decision Making tool kit. 2.0 The certainty and uncertainty in decision-making environment The decisions are taken in different types of environment. The type of environment also influences the way the decision is made. There are two types of environment in which decisions are made. 1. Certainty: In this type of decision making environment, 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. However, in many routine type of decisions, almost complete certainty can be noticed. These decisions, generally, are of very little significance to the success of business. Condition under certainty is which the decision maker has full and needed information to make a decision. Decision is made under the condition of certainty. The manager knows exactly what the outcome will be, as he or she has enough clarity about the situation and knows the resources, time available for decision-making, the nature of the problem itself, possible alternatives to resolve the problem, and undoubtedly clarify or certain with the result of alternatives. In most situations, the solutions are already available from the past experiences or incidents and are appropriate for the problem at hand. The decision to restock food supply, for example, when the goods in stock fall below a determined level is a decision-making under circumstances of certainty. 2. Uncertainty: 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. The decision maker is not in a position, even to assign the probabilities of happening of the events. In a situation of uncertainty, on the other hand, people have only a meager database, they do not know whether or not the data are reliable, and they are very unsure about whether or not the situation may change. Moreover, they cannot evaluate the interactions of the different variables. For example, a corporation that decides to expand its Operation to an unfamiliar country may know little about the country’s culture, laws, economic environment, and politics. The political situation may be volatile that even experts cannot predict a possible change in government. 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 and so on are the factors that involve uncertainty. 3.0 The principal steps of decision analysis To solve a decision tree problem, the following steps are used. Step 1: Grow a decision tree. Arrange the decisions and events in the order in which they will occur. This is often difficult with complex decision problem with complex decision problems, but unless the decision tree accurately represents the situation, the decision made may not be the best decision. Step 2: Assign the possibilities. Make the necessary probability assessments and show them on the event branches. These probabilities can be determined using classical assessment, relative frequency of occurrence, or subjective techniques. (Remember that probabilities are associated with the uncertain events and not with the decision alternatives.) Step 3: Assign cash flow Assign cash flow by showing costs and payoffs on the branches where they occur. Accumulate these cash flows and determine the end value for each branch of the decision tree. Step 4: Fold back the decision tree At each decision fork, select the decision that maximizes expected payoffs or minimize expected costs. 4.0 Decision Tree step-by-step Step 1: Grow the decision tree. The decision tree is developed by organizing the decisions and events in chronological order. In this example, the initial decision to be made is whether to accept the lease. The tree is started as: If the land is leased, no further decisions are required. However, if the land is not leased, Patrick faces the decision of whether to drill on the property. The tree then grows to: Now, if Patrick decides to drill, there are four possible events that could occur. These are shown on the decision tree. When finished, the decision tree should show all the decisions and events. Step 2: Assign probabilities to the event outcomes on the tree. In this example, the only event deals with the production result if Patrick decides to drill. The company has subjectively assessed of probability of each of the four possible outcomes as follow: Outcomes Probability Dry hole 0.2 Natural gas only 0.4 Natural gas and some oil 0.3 Oil only 0.1 Step 3: Assign the cash flows to the tree. At each branch of the tree at which revenue or a cost occurs, show the dollar value. These revenues and costs are then totaled across the tree and the end values for each branch are determined. These cash flows are placed on the tree as follows. END VALUE Step 4: Fold back the decision tree and compute the expected values for each decision. We need to compute the expected value for each decision alternative. This is done starting from the right side of the tree and working back to the left. We first determine the expected value for the Drill branch as follows: E [Drill] = $400,000(0.20) + $0(0.40) + $400,000(0.30) + $1,200,000(0.10) = $160,000 As we fold back the tree, we block all decision alternatives that do not have the highest expected value. This is shown in the decision tree as follows. END VALUE Note that we always select decisions with the highest expectation payoff. In this example, the best decision is to lease the land and accept $200,000 payment because it exceed $160,000 expected value of the not non-lease option. 5.0 Benefits of using decision analysis There are couples of benefits of using a decision tree. First of foremost, it is very easy to understand and interpret for any reader. To better understand how decision trees work, it is best to consider some examples. The decision tree below provides its students are on a company trying to decide whether or not to invest in an online training program. The main decision to be made, which starts in the first box, is whether or not to spend money on a new training program. From that box, the two options are yes or no, so one branch is drawn from the first box with a "yes" on it and another with a "no" written on it. Since the outcome from each is still unknown, circles are drawn at the end of each branch From the yes branch, there are then two options for different training programs, system 1 and system 2. So, from the circle at the end of the yes branch, two more branches are drawn: one for system one and the other for system two. From there, branches are drawn out of for how the system will be paid for, either in full or monthly installments. On the "no" side of the tree, branches are drawn to continue using the existing training method or hire live training experts. Since the existing plan is already in place, no more branches are drawn out from that branch. From the hiring live trainers branch, two more branches are drawn for those to be either field technicians or sales engineers. Once the tree is complete, the costs and probabilities are assigned to each branch in order to calculate and evaluate each option. By building the decision tree, small details that may have been missed are taken into consideration. For the examples, since there are a lot of calculations involved in creating decision trees, many businesses use dedicated decision tree software to help them with the process. Decision tree software helps businesses draw out their trees, assigns value and probabilities to each branch and analyzes each option. An addition to that, decision trees require relatively little effort from users for data preparation. To overcome scale differences between parameters - for example if we have a dataset which measures revenue in millions and loan age in years, say; this will require some form of normalization and scaling before we can fit a regression model and interpret the coefficients.  Such variable transformations are not required with decision trees because the tree structure will remain the same with or without the transformation. Another feature which saves data prep time: missing values will not prevent splitting the data for building trees. This article describes how decision trees are built. Decision trees are also not sensitive to outliers since the splitting happens based on proportion of samples within the split ranges and not on absolute values. Decision trees help in brainstorming outcomes. Decision trees help you think of all possible outcomes for an upcoming choice. The consequences of each outcome must be fully explored, so no details are missed. Taking the time to brainstorm prevents overreactions to any one variable. The graphical depiction of various alternatives makes them easier to compare with each other. The decision tree also adds transparency to the process. An independent party can see exactly how a particular decision was made. Another benefit of decision tree is displaying the sequencing and interrelations of tasks and events. The schematic display of starting events, secondary and terminating events allow for insights into input / output relationship and start/stop phasing as branching is extending into the future. Priorities can be established from the difficulties, complexities, and time requirements suggested by each path. 6.0 Summary Through this assignment, I am now know essentially all of the important points related to learning decision trees as well as many points seminal to learning in general. First and foremost, I know that learning a classifier can be useful in decision making. Next, I know what a decision tree is. Besides that, I have learn that how to write a basic decision tree analysis step by step. Last but not lease, I know how to make decision accurately by constructing a decision tree when facing the conflicts. In conclusion, decision tree analysis really guides me a lot in term of making my decision. It is useful for me for the future when I own a company. Of course, my point of views is just the tip of the iceberg; there are many other areas to investigate such as some fairly well resolved, while others remain topics of very active research, which will continue to lead to interesting, and financially rewarding, results. 7.0 References http://www.investopedia.com/terms/d/decision-tree.asp http://www.mindtools.com/dectree.html http://en.wikipedia.org/wiki/Decision_tree_learning http://www.businessnewsdaily.com/6147-decision-tree.html http://www.simafore.com/blog/bid/62333/4-key-advantages-of-using-decision-trees-for-predictive-analytics http://smallbusiness.chron.com/advantages-decision-trees-75226.html http://decisiontreemodel.blogspot.com/ http://www.clt.astate.edu/asyamil/groebner6ed/ppt/chap18.pdf http://webdocs.cs.ualberta.ca/~aixplore/learning/DecisionTrees/InterArticle/9-DecisionTree.html http://www.yourarticlelibrary.com/decision-making/decisions-making-environments-certainty-uncertainty-and-risk/10269/ http://www.studymode.com/essays/Decision-Making-Under-Certainty-Uncertainty-And-153805.html 8.0 Coursework NAME: CHOCK MEI YAN NRIC: 930330-10-5616 H/P NUMBER: +6016-9827381 STUDENT ID: 200106 Decision Management Systems are differing from traditional Decision Support System in five ways. Explain carefully. Decision Support Systems provide information that describes the situation and perhaps historical trends so that humans can decide what to do and which actions to take. Decision Management Systems automate or recommend the actions that should be taken based on the information that is available at the time the decision is being made. The policies, regulations, and best practices that determine the best action are embedded, at least in part, in a Decision- Management System where a Decision Support System requires the user to remember them or look them up separately. The information and insight presented in a Decision Support System is typically backward looking, and Decision Support Systems are generally reactive—helping human decision-makers react to a new or changed situation by presenting information that might help them make a decision. In contrast, Decision Management Systems use information to make predictions and aim to be proactive. Learning is something that happens outside a Decision Support System and inside a Decision Management System. Users of Decision Support Systems are expected to learn what works and what does not work and to apply what they learn to future decisions. Decision Management Systems have experimentation or test-and-Learn infrastructure built in so that the system itself learns what works and what does not. Decision Management Systems are integrated into an organization's runtime environment. They make decisions for applications and services in the organization's enterprise application architecture. In contrast, Decision Support Systems are often desktop or interactive applications that execute outside the core application portfolio. A Business-centric rules management environment has a number of characteristics. Explain carefully. Each business expert sees only the business rules for which he is responsible: Multiple business experts might use the rule management environment, and they should be able to read those rules they have read permissions for and change those rules they have read/write permissions for. They should not have to navigate through a lot of other rules or rule repository structures to find them. These business rules are presented in context Business users are making these changes in a business context—for instance, they are responding to new regulations or trying to improve the performance of the decision service. This context should be reflected in the rule management environment so that changing the rules feels like just part of running the business. The business rules editing environment allows only those changes that make sense: Some rules can only be changed in certain ways because of the underlying data—only certain values can be set as a consequence of a rule, for instance. Conditions and consequences that make no business or technical sense should not be allowed; if the overall decision service constrains the specific rules in question to behave within a range of allowed behaviour, then this should also be enforced. The business expert can rapidly see the impact of her proposed changes: The rule management environment should be linked to the impact analysis tools and techniques described later. No unnecessary technical information is presented Page 2 of 22