**Quote from ****Roy Disney**:

It’s not hard to make decisions when you know what your values are.

Decision making can be regarded as the processes of selecting one or more choices. The choices that we select often have monumental impacts on ourselves and the people around us. A critical ability for successful decision making is to correctly weigh the criteria in terms of relative importance to the outcome.

The highest set of decision making techniques is termed cumulative and combined intelligent techniques. This involves the use of historical data and a group analysis of the decision. This method involves 9 main steps.

Step 1: Assemble an intelligence team with historical tools to complete the decision analysis as a group.

Step 2: Define the process to the group and the rules to bind all participants.

Step 3: Define the intended outcome.

Step 4: Define the criteria that the intended outcome will be judge upon.

Step 5: Rank the order of importance of each criteria.

Step 6: Assemble the options available.

Step 7: Measure each options criteria against each other using historical and up to date specialist information.

Step 8: Determine the Weighting methods for the scoring of each criteria.

Step 9: Apply the weighting and calculate the best decision.

In this article we will focus on items 8 and 9 of the process. Often in decision making team and individuals do not have the insight or the time to determine the weighting difference between each criteria in a decision making process. To overcome this a natural weighting that is general for most situation would be of an advantage. The natural weighting derived here is call the Deesidar calculation.

The first step in decision weighting is to rank in order the most important criteria to the least important criteria. This is called the creation of the criteria of importance ladder. This ranking can be done by sampling pair wise comparison through the list of criteria and ranking it terms of more important and less important. If you list out the criteria in any order then start at the top of the list and compare the first item with the second, then if the first item is more important leave it as is if not then switch it with the second item. Then compare the second item with the 3rd item. If the third item is more important leave it as is if not then switch it with the second item. Continue this process until the order is complete.

For example: Criteria for the purchase of a Dinner set

The brain storm for criteria produced:

- Aesthetics
- Price
- Safety
- Durability
- Ease to clean

Pairwise comparison 1st Pass results:

- Aesthetics
- Safety
- Price
- Ease to Clean
- Durability

Pairwise comparison pass 2nd Pass results:

- Safety
- Aesthetics
- Price
- Ease to Clean
- Durability

Pairwise comparison pass 3rd Pass results:

- Safety
- Aesthetics
- Price
- Ease to Clean
- Durability

(No change from Part D and C)

Once the order of importance of the criteria has been established then determining a weighting method to distinguish the importance of each criterion is required. Often people that make decisions in groups or on complex subjects do not have the knowledge or the emotional balance to differentiate the importance of all the criteria. It is in these situations that a natural weighting method can be used to enhance the chances of obtaining the best outcome. Note that I did place the word “chances” in the previous sentence because the use of natural weighting for decision making does rely on probability.

A number of natural weighting methods can be applied to a structured decision making process. Pairwise weighting is when a decision maker compares and weighs in order how much 1 criterion is more important than the other. For decision making groups with participant having variable degrees of communications skills and knowledge levels and decision makers with a weak information grasp on the criteria then this method could result in a more bias weighting than a natural weighting.

A standardised weighting method of decision criteria is applying a set of mathematical rules to determine the weighting of each criterion based on their position in the criteria of importance ladder. To reduce the chances of unbalancing anomalies in weighting each criterion a standardised weighting method that is based criteria importance ladder will offer an unbiased and rapid solution for decision makers.

A weighting method that combines the key natural calculation methods in a balance fashion would be most obviously sort for the development of an unbiased standardised weighting system. The main decision making natural calculation methods are: Raw score, Cascading and the 80-20 rule. Raw score is when no weighting is issued and all criteria are consider balanced and equal. Cascading is when a natural factor reduction factor is issued to each criterion on the ladder. The 80-20 rule is when the top 20% of the criteria receive 80% of the score. The Deesdar equation takes each of these natural calculation methods and applies them to the weighting equally.

**Cascading**

Cascading is derived from factorial and series mathematics. Christian Kramp a French mathematician was a major contributor to factorial mathematics. Cascading the weighting scores occurs by applying a common weighting factor to the order of the criteria.

Cascade 50

When we say 50% cascading of the weighting we mean that each criterion is 50% less important than its previous criteria on the criteria of importance ladder. For example: Criterion 1 has a weighting factor of 1, criterion 2 has a weighting factor of 0.5 and criterion 3 has a weighting factor of 0.25 etc.

Here are the mathematical details:

In Mathematics terms, the score is applied as such:

Scores are: *x _{0,} x_{1,} x_{2,} x_{3,} x_{4,} x_{5}*

Weighting of the scores using cascade 50% = Ycas50

Ycas25= xn +( *x _{n+1}*x ((100-50)/100)n+1 )..

Ycas50 = *x _{0}* +(

*x*x ½ ) + (

_{1 }*x*x ¼ ) + (

_{2}*x*x 1/8) + (

_{3}*x*4 x 1/16) + (x5 x 1/32)…

Cascade 25

Cascade 25 or 25% Cascading weighting is applied as follows:

Ycas25= xn +( *x _{n+1}*x ((100-25)/100)n+1 )..

Ycas25= xn +( *x _{1}* x ¾ ) + (

*x*x 9/16)

_{2}*+( x*x 27/64) + (

_{3}*x*x 81/256) + (x

_{4}*x 243/1024)*

_{5}Cascade 50 and Cascade 25 are the most regularly used form of cascading in decision making. These 2 weighting methods have be applied in the Deesidar Equation.

**80-20 Weighting**

The 80-20 rule is often known by most as the Pareto principle. After Pareto developed his formula, many other researchers observed a similar relationship in their own field of research. Quality management expert, Dr. Joseph Juran, recognised a universal phenomenon that he termed as the principle of the “vital few and trivial many” which was similar to the Pareto’s principle. According to Dr. Juran’s observation of the “vital few and trivial many,” 20% of the tasks are always responsible for 80% of the results. This phenomenon can also be carried over for use in decision making weighting criteria.

As part of the Deesidar equation the 80-20 rule is applied to the criteria with the most import 20% of the criteria being allocated 80% of the Total Score weighting and the bottom 80% criteria being allocated 20% of the total score weighting

For example: if we have 10 criteria each with a score out of 10. Each of the top 2 criteria will be out of 40 and each of the lower 8 criteria will be each out of a score of (20/8 = 2.5)

**The Deesidar Equation**

The Deesidar equation takes into equal consideration the following decision scoring methods: (1) Raw score, (2) Cascade 25, (3) Cascade 50 and (4) The 80-20 rule. This is achieved by allocating each of these 4 weighting methods 25% of the total score. The aim of the Deesidar equation is to generate a natural weighting for a decision making calculation that is unbiased and a reflection of the most probable natural weighting of an informed decision maker.

Although this method seems arduous, with modern computing systems the weighting can be applied rapidly. Testing the Deesidar natural weighting so far from personal experience has left me satisfied. But it must be warned that the decision is only as good as the person or team that enters the data.

Below is a worked example:

The purchase of a Dinner set. A consumer has the option of purchasing 3 different dinner sets and builds his decision criteria with her partner.

- Build the Criteria ladder

Firstly, they settle and rank the criteria for the purchase of a Dinner set

The brain storm for criteria results in:

- Aesthetics
- Price
- Safety
- Durability
- Ease to clean

Pairwise comparison 1st Pass results in:

- Aesthetics
- Safety
- Price
- Ease to Clean
- Durability

Pairwise comparison pass 2nd Pass results in:

- Safety
- Aesthetics
- Price
- Ease to Clean
- Durability

Pairwise comparison pass 3rd Pass results in:

- Safety
- Aesthetics
- Price
- Ease to Clean
- Durability

2. Build the decision Table and Score Each out of 100

Criteria

Option 1

Red-Untempered Glass set

Option 2

Black tempered glass set

Option 3

Yellow Plastic Set

Safety

80

90

100

Aesthetics

90

80

60

Price

70

60

90

Ease to Clean

70

70

70

Durability

70

70

60

3. Generate the raw Score Result

Option 1 – 380, Option 2- 370, Option 3-380, (Possible maximum Score: 500)

Therefore: Option 1= 76%, Option 2= 74%, Option 3 = 76%

4. Generate Cascade 25 Score

Option 1-238.5, Option 2- 235.4, Option 3- 244.1, (Possible maximum Score: 305.1)

Therefore: Option 1= 78.2%, Option 2= 77.1%, Option 3 = 80%

5. Generate Cascade 50 Score

Option 1-155.625, Option 2- 158.125, Option 3- 165.1, (Possible maximum Score: 193.75)

Therefore: Option 1= 80.0%, Option 2= 81.6%, Option 3 = 85.2%

6. Generate the 80-20 Score

Option 1-23.24, Option 2- 23.1, Option 3- 23.88, (Possible maximum Score: 29.72)

Therefore: Option 1= 78.2%, Option 2= 77.7%, Option 3 = 80.5%

7. Total the scores and distribute at 25% for each calculation methods

Option 1 Total Score = 312.4, Option 2 Total Score = 310.4, Option 3 Total Score = 321.7 (Possible Maximum Score: 400)

Therefore: Option 1 Total Weighted Score = 78.1%, Option 2 Total Weighted Score = 77.6%, Option 1 Total Weighted Score = 80.425%,

8. Determine the highest score and the Best Option:

The best option is calculated as Option 3, The Yellow Plastic Dinner set.