At the California Department of Public Health, we promote decision intelligence (DI)—improving how we make team decisions to find, select, and solve problems and to improve outcomes. A foundational component of DI is decision quality—the six requirements of good decision making. At a minimum, a decision quality checklist (DQ) (Table 1) improves the quality of decisions at any stage of problem solving. A good decision is only as strong as its weakest link. (To learn more about DQ watch 3.5min video from the Decision Education Foundation.)

Table 1: Key questions to answer for decision quality (DQ)

No.

Requirement

Key question to ask

1

Frame

Are we clear on the problem we are solving?

2

Values

Have we identified what we truly want?

3

Alternatives

Do we have a good set of alternatives?

4

Information

Have we gathered the relevant information?

5

Reasoning

How will we evaluate alternatives to find the one that gets us the most of what what we truly want?

6

Commitment

Are we committed to follow through on our choice.

Making higher quality decisions as a group can be challenging. Our brains prefer intuitive decision making. Deliberations are cognitively exhausting, especially in the face of competing objectives, uncertainty, and time constraints. That is why, unfortunately, decision-making is often deferred to an authority figure. It’s better to embrace team decision making but practice with simple tools and methods that promote group engagement, deliberation, and decision quality.

2 Making single and multi-attribute decisions

2.1 Single-attribute decision

Suppose our team needs to select from several alternatives and it will be based on a single attribute; for example, choosing one of five available colors to paint our office. That attribute—color—becomes our single criterion and we apply the ranking method illustrated below. In this example the single attribute (color) represents the alternatives (five colors to choose from).

2.2 Multi-attribute decision

However, more commonly, based on our frame (values, objectives, and key results), our team needs to select from among several alternatives, each of which has mutliple attributes. Selection examples include buying a smartphone, hiring an employee, or selecting a vendor. As individuals and as a group, we have preferences for these attributes based on what we know (information) and what consequences and impacts we care about (prospects). We need sound reasoning but it will not require a complex decision analysis. Engaging the team in fair and transparent deliberative decision making builds trust, consensus, and commitment.

Our main task is to (a) develop criteria based on these attributes, (b) weight the criteria based on importance to us (preference), and (c) rate the alternatives based on the criteria and evidence (data, community, experts, analysis). Ideally, the criteria should be weighted without any knowledge of the alternatives. This is to prevent evaluators from biasing the criteria weights in favor of their favorite alternative.

In this blog posting I show how to weight criteria using a simple ranking method. In a future blog post, I will show how to apply weighted criteria to rank and select alternatives. This first step, weighting criteria, is very powerful and practical. We will use a trivial example to nail down the concepts.

Now, suppose we wish to buy a car and our choices are a Honda Civic, and Subaru Impreza, or Toyota Corolla. We have data on the following attributes: safety (S), mileage (M), reliability (R), color (C), price (P), and resale value (V). Table 2 summarizes the DQ requirements for buying our car.

Table 2: Decision quality requirements for buying a car

No.

Requirement

Key question to ask

Answer

1

Frame

Are we clear on the problem we are solving?

Need personal transportion.

2

Values

Have we identified what we truly want?

Car within my budget.

3

Alternatives

Do we have a good set of alternatives?

Civic, Corolla, or Impreza

4

Information

Have we gathered the relevant information?

Color, mileage, price, reliability, safety, resale value

5

Reasoning

How will we evaluate alternatives to find the one that gets us the most of what what we truly want?

Ranking algorithm using weight calculations

6

Commitment

Are we committed to follow through on our choice.

Yes, my spouse approves.

3 Calculating criteria weights—the easy way

Group deliberative decision-making is cognitively exhausting. So, anything you can do to make the process easier will keep team members engaged. Do not let “perfection become the enemy of the good”. The easiest way to generate criteria weights from a team of evaluators is to use a rank ordinal method [1].

Give evaluators small pieces of paper with one criterion printed on on each. If we have five criteria, they get five small pieces of paper. Have them rank them from top to bottom. Once they have ranked them, tape their ranking onto an 8.5in x 11in paper and hand to the facilitator. This ranking is entered into the computer for analysis (see below).

3.1 Ratio ordinal method in Julia

I will demonstrate this method using the Julia language. This method can also be implemented using R, Python, or Microsoft Excel.

For rating the cars we have six criteria (attributes) for which we need to calculate weights:

Color (C)

Mileage (M)

Price (P)

Reliability (R)

Safety (S)

Value, resale (V)

We have five evaluators that will rank the criteria based on their knowledge, experience, expertise, and wisdom. It is much better for them to rank the criteria independently and without thinking about specific cars, otherwise they may game (bias) the weighting.

Here are the steps:

Select a ranking method to calculate weights for a specific number of criteria, in this case we have six criteria. We will write and use a Julia function that implements the SR method from [1].

Have each evaluator independently rank the criteria.

Use Julia to calculate the final criteria weights. We will use the split-apply-combine workflow that I introduced in a previous blog post and book review [2].

3.1.1 Step 1: The SR method for generating criteria weights

Here is the formula where \(N\) is the number of criteria, and \(w_i^{SR}\) is the weight for the \(i^{th}\) criterion [1].^{1}

usingDataFramesusingStatsBasefunctioncalculate_rank_weights(n::Int64) num =zeros(n)for i in1:n num[i] = (1/i) + ( (n +1- i) / n )end wi = num /sum(num)return wiendwi =calculate_rank_weights(6)

Next, we organize the evaluator criteria rankings and the SR method criteria weights into a data frame with three columns:

evaluators (optional)

evaluator_rankings (required)

weights (required)

ne =5; # number of evaluatorsnc =6; # number for criteriaevaluators =repeat(["eval".*string.(1:ne)...], inner=repeat([nc]))evaluator_rankings =vcat( eval1, eval2, eval3, eval4, eval5 )weights =repeat( wi, ne )df =DataFrame(hcat(evaluators, evaluator_rankings, weights), ["evaluator", "criteria", "weight"] )

30×3 DataFrame

5 rows omitted

Row

evaluator

criteria

weight

Any

Any

Any

1

eval1

Mileage

0.336134

2

eval1

Color

0.22409

3

eval1

Price

0.168067

4

eval1

Safety

0.12605

5

eval1

Reliability

0.0896359

6

eval1

Value

0.0560224

7

eval2

Mileage

0.336134

8

eval2

Color

0.22409

9

eval2

Safety

0.168067

10

eval2

Reliability

0.12605

11

eval2

Value

0.0896359

12

eval2

Price

0.0560224

13

eval3

Color

0.336134

⋮

⋮

⋮

⋮

19

eval4

Mileage

0.336134

20

eval4

Value

0.22409

21

eval4

Color

0.168067

22

eval4

Safety

0.12605

23

eval4

Reliability

0.0896359

24

eval4

Price

0.0560224

25

eval5

Safety

0.336134

26

eval5

Price

0.22409

27

eval5

Color

0.168067

28

eval5

Reliability

0.12605

29

eval5

Mileage

0.0896359

30

eval5

Value

0.0560224

3.3 Step 3: Calculate mean criteria weights

Calculate mean criteria weights using split-apply-combine workflow. In other words, stratifying by criterion, what is the mean weight for each criterion (attribute)?

## splitgdf =groupby(df, :criteria)

GroupedDataFrame with 6 groups based on key: criteria

These are the final criteria weights and, as expected, they sum to 1.

round(sum(crit_weights.weight_mean))

1.0

In this example, mileage (M) has the highest weight (24.5%) and reliability (R) has the lowest weight (10.4%).

In a future blog, I will show how to score the alternatives (Honda Civic, Toyota Corolla, and Subaru Impreza) with these weighted criteria, and then making a final ranking of which car to select.

4 Appendix

4.1 Function to automate calculating mean weights

We created the calculate_rank_weights function to calculate the weights for \(n\) criteria using the SR method. Then we used Julia to calculate the mean weights for five evaluators.

Now we create a final function to automate calculating the mean weights. This function will handle an arbitrary number of vectors with rankings, or a matrix created from those vectors beforehand. Notice that this new function will call our previous calculate_rank_weights function.

We see that r2 is a NamedTuple and it contains two data frames named weights and data. We can index each separately.

r2.weights

6×2 DataFrame

Row

criteria

weight_mean

Any

Float64

1

Mileage

0.244818

2

Color

0.22409

3

Safety

0.162465

4

Price

0.134454

5

Value

0.129972

6

Reliability

0.104202

r2.data

30×3 DataFrame

5 rows omitted

Row

evaluator

criteria

weight

Any

Any

Any

1

eval1

Mileage

0.336134

2

eval1

Color

0.22409

3

eval1

Price

0.168067

4

eval1

Safety

0.12605

5

eval1

Reliability

0.0896359

6

eval1

Value

0.0560224

7

eval2

Mileage

0.336134

8

eval2

Color

0.22409

9

eval2

Safety

0.168067

10

eval2

Reliability

0.12605

11

eval2

Value

0.0896359

12

eval2

Price

0.0560224

13

eval3

Color

0.336134

⋮

⋮

⋮

⋮

19

eval4

Mileage

0.336134

20

eval4

Value

0.22409

21

eval4

Color

0.168067

22

eval4

Safety

0.12605

23

eval4

Reliability

0.0896359

24

eval4

Price

0.0560224

25

eval5

Safety

0.336134

26

eval5

Price

0.22409

27

eval5

Color

0.168067

28

eval5

Reliability

0.12605

29

eval5

Mileage

0.0896359

30

eval5

Value

0.0560224

The calculate_mean_weights function worked for both vectors or a matrix. This was possible because of the splat operator (...) as in the two lines below.

functioncalculate_mean_weights(x...) x =hcat(x...)

4.2 Review of selected Julia code

Did you know you can use tuples to bind variable names to values?

x, y =33, 99

(33, 99)

x

33

y

99

This is super handy when we want to swap values between variables in one step.

Danielson M, Ekenberg L. Trade-offs for ordinal ranking methods in multi-criteria decisions. In: Bajwa D, Koeszegi ST, Vetschera R, editors. Group decision and negotiation Theory, empirical evidence, and application [Internet]. Cham: Springer International Publishing; 2017. p. 16–27. Available from: https://doi.org/10.1007/978-3-319-52624-9_2

2.

Kaminski B. Julia for data analysis. New York, NY: Manning Publications; 2023.

Footnotes

The SR method was selected because it was the best performing.↩︎