5 main Benefits of Decision Tree by Paul Mali:

Paul Mali points out the following benefits of Decision Tree:

1. The impact of possible future decisions:

A tree’s branching and its spreading chain of events can clarify potential barriers, changes, and problems. The analyst can probe a variety of effects on his model tree by deliberately imposing faults and critical conditions to foresee their impact.

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2. The impact of uncertainty on confidence:

Layout of the events in a tree structure makes more visible the alternatives that occur. Riskfactor assignments or probabilities give better insight to and confidence in the future effects of a decision made in the present.

3. The impact of varying commitments of payoff:

Trying a variety of tentative objectives in a decision tree can reveal comparative advantages and disadvantages. These can be analysed in a payoff table for criteria such as present or future profits. This validation procedure can frequently lead to restating the objective or selecting a new one.

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4. 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.

5. The measurement of risk:

Each path of the decision tree contains, in addition to the elements of the paths, and assigned risk factor. This is the estimated likelihood of occurrence of the terminal event in the path.

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The decision trees satisfy a more complex need where a series of decisions are to be made simultaneously.

Barry Shore, has proposed the following procedure to solve a problem by the decision tree method.

(i) The problem is illustrated by developing tree diagram. Each course of action is represented by a separate emerging branch.

(ii) Each outcome for each course of action is assigned a probability, which is the most likely chance of that particular outcome occurring.

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(iii) Determine the financial results of each outcome.

(iv) The expected value for each outcome is calculated and the alternative which will yield the highest expected value is chosen.

Decision trees depict future decision points and possible chance events. It adds to the confidence and accuracy of the decisions. Decision trees can be drawn to meet all sorts of situations.

Decision tree enables a planner: (a) to consider various courses of action; (b) to adding financial results to them (c) to modify these results by their probability; and (d) then to make comparisons.

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Some decisions involve series of steps. Each step is not self-contained, but dependent on the outcome of the preceding step. For example, second step depending on outcome of second and so on. Thus with certainty mounting up with each step complexity comes in the problem’s solutions.

Under such situations decision tree models provide an answer to such problems. Decision trees depict future decision points, and possible chance events – various uncertain things happening in future. It adds to the confidence and accuracy of the decisions. Decision trees can be drawn to meet all sorts of situations.