The complexity of decisions involving significant uncertainty brings challenges such as unpredictable consequences, multi-layered factors, potential reduction of uncertainty through additional information and the influence of the decision maker's attitude to risk. To manage this complexity, users often look for best practices. Leverage the effectiveness of decision making by applying a decision tree, a proven analytical approach that provides clarity and leads to informed decisions in the midst of uncertainty. At the heart of the decision tree app is a user-friendly approach that transforms decision making. Start by entering alternative decisions for the basic problem and define outcomes with associated win values and probabilities. The robust tree engine adapts to any depth and allows the creation of multi-level strategies by adding new alternatives to the outcomes. Using a built-in algorithm, the app uses expected value criteria to determine the optimal strategy and ensure a comprehensive and effective decision-making process. Features: -Intuitively construct a visual decision tree for a comprehensive decision analysis. -Generate alternative elements with detailed descriptions and associated costs. -Define outcomes for each alternative and specify payoff amounts and probabilities for thorough evaluation. -Dynamically arrange elements to create parent-child relationships at any depth in the tree. -Construct sequential decision chains that allow subsequent decisions based on specific outcomes. -Enter the cost of each alternative to accurately assess the impact of the decision. -Specify nominal profit values and probabilities for each outcome to refine the decision criteria. -Automatically calculate the path value resulting from the multiplication of value and probability. -Calculate the net profit, i.e. the result obtained by subtracting the cost of the alternative from the path value of the outcome. -Automatically visualize the best strategy based on the criteria for the optimal expected value. A decision tree created with the decision tree app is a diagram for a decision. This diagram is built and read from left to right and from top to bottom. The top left node in a decision tree is called the root node and contains the main problem to be solved by the decision tree. The branches extending from a decision node to the right represent the available decision alternatives. One, and only one, of these alternatives can be selected. The branches to the right and below the alternative show the possible outcomes. On the branch representing each possible outcome, the probable sales revenue (or value) for the alternative is shown, assuming either a success or a failure for the alternative. Finally, the net profit (net value) is shown at the bottom of the outcome of the tree for each possible combination of alternative and outcome. The probability value of the outcome is calculated from the nominal value, which is inserted using the slider at the bottom, and the probability of the outcome. The net value is calculated from the difference between the probable value and the cost of the corresponding higher-level alternative. To decide which alternative to select in a decision problem, the Decision tree app uses a specific decision criterion, i.e. a rule for decision making. The expected value is a decision criterion that takes into account both the possible outcomes for each decision alternative in the decision tree and the probability that each outcome will occur. The expected value for an uncertain alternative is calculated by multiplying each possible outcome of the uncertain alternative by its probability and adding the results together. In winning situations where "more is better", the alternative with the highest expected value is the best. The complete specification of all preferred decisions in a sequential decision problem is called a decision strategy and can be called up via the Decision strategy button.
