3.1. Novelty

Sarkar & Chakrabarti (Reference Sarkar and Chakrabarti2011) focus on novelty. They consider the novelty of a product to be evaluated relative to existing products that meet the same need. They evaluate both functions and structures to determine the level of novelty for the product. They focus on identifying the novelty of an idea, rather than of a set of ideas. They also recognize that design creativity involves both novelty and usefulness.

Sluis-Thiescheffer et al. (Reference Sluis-Thiescheffer, Bekker, Eggen, Vermeeren and De Ridder2016) applied SVS’s novelty calculations to actual data resulting from young children’s design activities. They found some problems with novelty scores, including trivial solutions that have high novelty scores, solutions that have different novelty scores depending on their method of generation, and that there is a relatively narrow distribution of novelty scores, which means we know very little about actual novelty. Instead of a continuous novelty rating for each idea, they propose a binary score (novel, not novel).

Hernandez, Shah, & Smith (Reference Hernandez, Shah and Smith2010) introduce two possible metrics for set novelty: the average novelty of the ideas in the set (average novelty) and the novelty of the most novel idea in the set (best novelty). They make no recommendation as to which is best, and consider that each of the set-based novelty metrics contributes a different view of the set novelty.

Jagtap et al. (Reference Jagtap, Larsson, Hiort, Olander and Warell2015) introduce individual average novelty (IAN), which evaluates ideas only relative to a specific individual idea set, as compared with SVS’s average novelty (AN), which evaluates ideas relative to all available idea sets for a given design challenge. They study the interdependency between novelty and variety for both of these novelty metrics.

Ahmed et al. (Reference Ahmed, Ramachandran, Fuge, Hunter and Miller2018) use pairwise comparisons of ideas to place a small set of ideas on an idea map. They then combine the ratings of multiple raters to identify the ideas with the highest novelty.

3.2. Variety

Nelson et al. (Reference Nelson, Wilson, Rosen and Yen2009) found SVS’s variety metric to be flawed. They claim that, using the SVS variety metric and evaluation approach, it was possible for an idea set with subjectively low variety to receive an erroneously high variety score. They also found that average variety scores are flawed, and therefore propose to use total, rather than normalized variety. They attempt to resolve the problems with SVS’s measure by using different weights for the various tree levels, but provided limited justification that the selected weights were correct.

Verhaegen et al. (Reference Verhaegen, Vandevenne, Peeters and Duflou2015) noted that when idea distributions (trees) were unbalanced, SVS’s and Nelson’s methods both led to problems. They propose the use of a Shannon entropy measure at each level of the tree to calculate variety and provide an online software tool for testing new metrics.

Ahmed et al. (Reference Ahmed, Ramachandran, Fuge, Hunter and Miller2020) suggest that tree-based methods of variety evaluation are unreliable and recommend that entropy-based methods be used instead. They estimated the ground truth variety by creating subsets from a ground set of design items, having human raters make pairwise comparisons of the subsets, and calculating tree-based metrics for the subsets. They find that the Herfindahl–Hirschman Index for Design (HHID) shows better accuracy and discrimination and can be used to identify high-variety subsets.

Ahmed & Fuge (Reference Ahmed and Fuge2017) seek to obtain ranked sets of ideas for diversity and quality, with a goal of obtaining a relatively small set of ideas with high diversity and quality that can serve as a base for future ideation. They demonstrate three methods of numerically calculating similarity and show that these similarity ratings can form the basis of optimal searches for high-diversity, high-quality idea sets.

Henderson et al. (Reference Henderson, Helm, Jablokow, McKilligan, Daly and Silk2017) define a variety metric that evaluates the amount of design space covered by looking at the scores for ideas on other metrics as coordinates in an $ n $ -dimensional space. This provides a quantitative measure for variety, but makes variety a dependent parameter of the other metrics evaluated for the set.

3.3. Quality

Cheeley et al. (Reference Cheeley, Weaver, Bennetts, Caldwell and Green2018) focused on quality scores and attempted to match ratings to ground truth. They identified two fundamental elements of quality: effectiveness and feasibility. They performed a statistical test to select relative weightings for effectiveness and quality that would allow quality rankings using their formula to match intuitive quality rankings by expert raters. This study showed that quality could be reliably evaluated by evaluating effectiveness and feasibility.

Kudrowitz & Wallace (Reference Kudrowitz and Wallace2013) identified three attributes of quality early-stage ideas: novel, feasible and useful. They evaluated individual product ideas, rather than the complete set of ideas. They considered this evaluation to be a first-pass evaluation.

Reinig, Briggs, & Nunamaker (Reference Reinig, Briggs and Nunamaker2007) focus on the measurement of set quality. They address the importance of identifying unique ideas, and then describe a few known methods for evaluating idea quality, and then discuss three possible set quality metrics: sum-of-quality, average-quality and good-idea-count. They demonstrate that good-idea-count is superior to the other two set quality metrics.

Toh & Miller (Reference Toh and Miller2014) assess idea quality on a five-point scale: does it achieve the desired outcome, is it technically feasible, is it easy to execute, and is it a significant improvement over the existing design. They use the average quality of the ideas for the quality of the set. They assess novelty in two classes: form-based and function-based. They use the average novelty of the ideas for the quality of the set. They evaluated the effect of physical examples and interactions on the novelty and quality of the resulting ideas.

Some researchers – including Patel et al. (Reference Patel, Summers, Morkos and Karmakar2024) – have acknowledged the challenge of predicting how well an early-stage concept will meet design requirements. Given this challenge, Patel et al. (Reference Patel, Summers, Morkos and Karmakar2024) have used addressment as a surrogate for quality, where addressment is the quantity of design requirements the concept addresses.

3.4. Quantity

All the reviewed papers that measured quantity simply counted the number of ideas listed in the set, with no adjustments for nearly identical ideas. We believe that when two ideas are close enough to one another, they should not be counted as distinct ideas. We found no references in the literature that attempted to adjust quantity measures for closely related ideas.

3.5. Weaknesses of literature metrics

Some methods for evaluating idea sets for SVS’s attributes display weaknesses in their use for evaluating idea sets. For example, using the average idea quality as a measure of the set quality means that the number of low-quality ideas will affect the set quality measure. Similarly, using the average quality of the top Y% of the ideas as the set quality measure means that a high-quality idea that is not in the top Y% will have no effect on the set quality measure. Thus, these methods reward discarding low-quality ideas from the set.

Using the count-of-quality proposed by Reinig et al. makes the quality evaluation binary – high-quality ideas get a count of 1; other ideas get a count of 0. This draws no distinction between extremely high-quality ideas and moderately high-quality ideas. This lack of distinction can be problematic.

The average novelty and individual average novelty scores described above will both drop as low-novelty ideas are added to the set. But low-novelty ideas do not decrease the exploration of the design space; they just do not increase the exploration.

When average or other normalized variety scores are used, the number of low-variety ideas is important to the overall variety score. However, the presence of low-variety ideas should not detract from the contribution to variety provided by other ideas in the set. Further, as Nelson et al. have observed, when level weightings are arbitrarily chosen, variety calculations can be inconsistent with subjective variety evaluations.

If overlap between closely related ideas is not considered, quantity can be artificially inflated by making many ideas that are virtually, but not exactly, identical.

3.6. Summary of literature

Many authors have provided methods for evaluating the SVS attributes. However, none have all of the attributes desired for our work, namely:

• • The ability to evaluate both individual ideas and the set of ideas for all metrics

• • An a priori description of how the aggregate scoring of an idea set should vary as the individual ideas vary

• • The ability to quickly evaluate a large number of ideas that have a relatively low level of detail

• • A well-defined procedure for making subjective judgments, thus helping to increase consistency between raters

• • Set metrics that encourage the inclusion of all ideas, whether good or bad, in the set to be evaluated

Therefore, we define both simple idea evaluation tools and axiom-based aggregation functions that achieve these goals.

4.1. Aggregation functions

Aggregation functions are used to calculate the properties of a set of ideas based on the characteristics of the individual ideas. Higher scores on the aggregation functions correspond to higher quality, quantity, novelty and variety. Aggregation functions should be designed so that the scores encourage good ideation practices.

For the best possible ideation, the free flow of ideas is encouraged. During ideation, ideas should not be filtered for quality or novelty, because even objectively bad ideas can serve as triggers for better ideas.

Consider a hypothetical situation where design ideation is a formal competition between teams. The teams will be judged on a numeric score for novelty, variety, quality and quantity. The numeric score will be calculated from the novelty, variety, quality and quantity of the individual ideas using aggregation functions. The goal is to find aggregation functions that will encourage desired ideation behaviors and ignore undesired ideation behaviors. Poor ideas should not be penalized, because teams should not be filtering ideas during ideation. The desired behavior of aggregation functions is that they will always reward adding additional ideas and never reward filtering or removing ideas.

Desirable idea evaluation and aggregation functions share the following characteristics:

• • For any set of ideas, the aggregate score should never be increased by removing low-scoring ideas. This characteristic is necessary to discourage idea filtering either during or after ideation.

• • Duplicate ideas should not affect the aggregate score for any metrics.

• • Adding ideas should never reduce the aggregate score for a metric.

• • Ideas at higher levels of abstraction explore (and if novel, expand) the design space more than ideas at lower levels of abstraction. This is because an idea at a high level of abstraction contains within it the seeds of many less abstract ideas.

• • Ideas that are closely related contribute less to the exploration and expansion of design space than ideas that are distantly related.

• • Novelty and quality of an idea can be evaluated independently of the location of the idea in the idea space.

• • When considering the novelty of a set, both the novelty of an idea and the idea’s location in the idea space should be considered.

5.1. Axioms of variety

We propose the following axioms to describe how variety should change when adding new elements to an idea set:

1. Axiom V-1: Adding a unique entry at any level of abstraction should increase the total variety of the idea set. As a corollary, removing a unique entry at any level of abstraction should reduce the total variety of the set.

2. Axiom V-2: Adding a unique entry at a higher level of abstraction should increase the total variety more than adding a unique entry at a lower level.

3. Axiom V-3: Adding a unique entry at a given level of abstraction that is similar to many other ideas should increase the total variety less than adding a unique entry at the same abstraction level that is different from other ideas.

5.2. Axioms of novelty

We propose four axioms that describe how novelty should change when adding new elements to an idea set. These axioms recognize that novelty describes the expansion of design space, so both the amount of space occupied by an idea and the novelty of the idea will affect the set novelty.

1. Axiom N-1: At a given location in the idea space, an added idea with high novelty adds more to the set novelty than an added idea with low novelty.

2. Axiom N-2: A novel idea at a high level of abstraction adds more to the set novelty than an idea of equivalent novelty at a low level of abstraction.

3. Axiom N-3: An idea with a given novelty located far from other ideas in the idea space adds more to the set novelty than an idea with the same novelty located close to other ideas in the idea space.

4. Axiom N-4: The set novelty should be increased by adding a novel idea. It should be unchanged by adding an idea with zero novelty. As a corollary, the set novelty should never be increased by removing an idea.

5.3. Axioms of quantity

The following axioms are proposed for set quantity scores:

1. Axiom U-1: Duplicates of previously counted ideas should not be counted again when determining set quantity.

2. Axiom U-2: A unique idea added to the tree at a low abstraction level should add the same quantity as a unique idea added at a high abstraction level.

3. Axiom U-3: Only ideas generated by members of the team should be included in the set quantity. Ideas that may be generated by those evaluating the quantity should not add to the set quantity.

5.4. Axioms of set quality

The following axioms are proposed for set quality scores:

1. Axiom Q-1: High-quality ideas should increase the set quality more than moderate- or low-quality ideas

2. Axiom Q-2: Removing low-quality ideas from the set should not increase the set quality; adding low-quality ideas to the set should not decrease the set quality.

3. Axiom Q-3: Regardless of the number of high-quality ideas in the set, adding a high-quality idea should increase the set quality.

When these axioms are met by appropriate aggregation functions, the desired behaviors from “Aggregation Functions” will be achieved.

5.5. Axiom violations with legacy methods

To demonstrate the value of our methodology, we will now show the conditions under which the Axioms are not met with certain legacy methods, particularly SVS, because it is so well-known.

5.5.1. Variety

In Shah et al. (Reference Shah, Smith and Vargas-Hernandez2003), the SVS method is used to evaluate variety for a set with 11 ideas that result in two branches at the physical principle level, five branches at the working principle level, six branches at the embodiment level and four branches at the detail level. With SVS, these abstraction levels are scored as 10, 6, 3 and 1, respectively. Thus, the total Variety score is calculated as:

(1) $$ {M}_3=\left(\left(2\ast 10\right)+\left(5\ast 6\right)+\left(6\ast 3\right)+\left(4\ast 1\right)\right)/11=72/11=6.54 $$

If one of the branches is removed from the detail level, the calculation becomes:

(2) $$ {M}_3^{\ast }=\left(\left(2\ast 10\right)+\left(5\ast 6\right)+\left(6\ast 3\right)+\left(3\ast 1\right)\right)/10=71/10=7.1 $$

The SVS variety score has increased as a result of removing an idea, which violates Axiom V-1.

5.5.4. Quality

The SVS method calculates set quality through a weighted sum of component scores that reflect how well ideas meet certain functions or characteristics. In Shah et al. (Reference Shah, Smith and Vargas-Hernandez2003), an example is presented of four ideas (A-D) that are evaluated for manufacturability and minimum weight, which have a relative weighting of 2 and 1, respectively. The minimum weight scores for ideas A-D are 3.57, 6.14, 1.0 and 10, respectively. The manufacturability scores are 10, 8, 4 and 1, respectively. Thus, the total Quality score is calculated as:

(3) $$ {\displaystyle \begin{array}{c}{M}_2=\left(2\ast 3.57+1\ast 10\right)+\left(2\ast 6.14+1\ast 4\right)\\ {}\hskip1.5em +\left(2\ast 1.0+1\ast 8\right)+\left(2\ast 10+1\ast 1\right)\Big)/4\ast \left(2+1\right)\\ {}=64.42/12=5.37\end{array}} $$

If idea C is removed from the set, the calculation becomes:

(4) $$ {\displaystyle \begin{array}{c}{M}_2^{\ast }=\Big(\left(2\ast 3.57+1\ast 10\right)+\left(2\ast 6.14+1\ast 4\right)\\ {}\hskip17em +\left(2\ast 10+1\ast 1\right)\Big)/3\ast \left(2+1\right)\\ {}\hskip15.5em =54.42/9=6.05\end{array}} $$

The SVS quality score has increased as a result of removing an idea. It is similarly trivial to show that adding a 5th idea that is low quality would decrease the set quality score (a 5th idea with a weighted quality score of 10, like idea C, would result in a set quality score of 4.96). This violates Axiom Q-2. Because the SVS quality calculation is essentially a weighted average, it is also easy to show that it does not meet Axiom Q-3.

6.1. Design trees

A common way of representing the structure in an idea set is to use a tree structure (sometimes called a “genealogy tree”) (Shah et al. Reference Shah, Smith and Vargas-Hernandez2003; Nelson et al. Reference Nelson, Wilson, Rosen and Yen2009; Verhaegen et al. Reference Verhaegen, Vandevenne, Peeters and Duflou2015; Ahmed et al. Reference Ahmed, Ramachandran, Fuge, Hunter and Miller2020).

A tree is a structure consisting of elements and edges. Elements are nodes in the design tree. There are two different types of elements in a design tree. Idea elements are ideas generated by the design team that have been placed as nodes in the tree. Organizational elements are nodes that have been placed in the tree during the analysis of an idea set to ensure that each element has a parent element. Edges connect elements at different levels of the tree, indicating a relationship between the elements. Levels in the tree range from the lowest level, which contains the most detailed information, to the highest level, which contains the most general information. When an edge connects two elements, the element at the higher level is considered a parent in the relationship, while the element at the lower level is considered a child in the relationship. A parent can have multiple children, but a child can have only one parent. A family of elements is defined as a set of elements sharing a common parent. An element with no children is considered a leaf element of the tree. A branch of the tree consists of an element and all of its descendants.

Figure 1 shows a simple genealogy tree. Elements are shown as circles. Leaf elements are labeled with the letter L. Families are enclosed in vertical dashed ovals. Branches are enclosed in polygons with rounded corners.

Figure 1.

A sample four-level design tree. The level increases from right to left. Elements are shown by circles; their numbering is arbitrary. Leaf elements have an L following the element number. Families (groups of elements sharing a common parent, and which may have only a single member) are enclosed by dashed lines. They are labeled with F plus the level of the family. Branches are shown by shaded polygons with rounded corners. They are labeled with B plus the element number of the root element in the branch. There are eight leaf elements, seven families and seven branches in this tree.

A design tree is created by placing design ideas into a tree as elements of the tree. In this paper, when we evaluate the characteristics of an idea independent of its location in the tree, we calculate the idea properties. In contrast, when we evaluate the characteristics of an idea considering its relationships as an element of the tree, we calculate the element properties.

In order to evaluate the exploration and expansion of the design space (variety and novelty), we organize the idea set into a hierarchical tree structure. The structure we use is called an objective–principle–embodiment–detail (OPED) tree. Note that any tree structure would work in place of the OPED tree. For example, the physical principle – working principle – embodiment – detail tree used by Shah et al. (Reference Shah, Smith and Vargas-Hernandez2003) would work just as well. Every idea in the set is placed in the tree as an idea element at a particular tree level with an edge connecting it to a parent element at the next-higher level of the tree. Organizing the ideas in this fashion may require the evaluator to add organizational elements to the tree to ensure that each idea element in the tree has a parent. This is necessary to implement the scoring algorithm that follows, but does not bias the scoring, as will be demonstrated below.

For this paper, we define the levels in the OPED tree as follows:

As described above, the Objective level contains the desired outcome, not ideas for achieving the desired outcome. Thus, the Objective is not part of the generated ideas and serves only as an organizational element. The trees in this paper have only a single objective, but if desired, a tree can be created with multiple objectives.

It is anticipated that during ideation, there will often be ideas generated at the principle, embodiment and detail levels. However, if the team has not explicitly placed the ideas into a tree structure, it is unlikely that the idea elements will properly complete the design tree. For a well-formed tree, every element must ultimately be linked to the objective through a series of ancestors. Thus, every detail needs a parent embodiment, every embodiment needs a parent principle, and every principle needs a parent objective. During the analysis of the idea set, organizational elements will be added to the tree as needed to provide a parent for each idea element of the set. Guidelines for evaluating the relationships between ideas are given in “Idea Uniqueness.”

6.2. Design trees and design space exploration

Recall that SVS indicates that variety is a measure of how well the ideas explore the design space and novelty is a measure of how well the ideas expand the design space. The OPED tree is a representation of the explored region of design space. If novelty scores are assigned to individual ideas, the novelty can be used to estimate how much the design space is expanded. The detailed framework for doing this is described in “Evaluating and Aggregating Variety (Exploration of Design Space)” and “Evaluating and Aggregating Novelty (Expansion of Design Space).”

We recognize that the OPED tree cannot describe the total design space. It can only describe the explored space (because the ideas that have been generated represent exploration). Given this limitation, the OPED trees can only produce relative measures, but we believe that even the relative measures are useful.

The fundamental basis of this method is the recognition that every idea, regardless of its tree level, explores a finite amount of design space. Closely related ideas will overlap one another in the design space, so the amount of space explored per idea is less than for distantly related ideas. The total amount of explored space can be calculated from the sum of the individual spaces minus the overlapping spaces. The amount of explored design space is used in the calculation of set variety and set novelty.

Figure 2 shows a schematic representation of the design space captured by a design tree. Circles in the figure represent the amount of design space explored by an idea. In this figure, ideas at the principle level ( $ l=3 $ ) are labeled $ {P}_i $ , ideas at the embodiment level ( $ l=2 $ ) are labeled $ {E}_{ij} $ (where index $ i $ is the index of the principle that is the parent of the embodiment) and ideas at the detail level ( $ l=3 $ ) are labeled $ {D}_{ijk} $ (where index $ ij $ is the index of the embodiment, i.e., the parent of the detail). To help make the parent/child relationships clearer, a line connects each parent to the cluster of ideas that are its children.

Figure 2.

Schematic representation of the design space explored by ideas in a tree structure. Two important concepts are illustrated: (1) individual ideas at higher levels of the tree explore more space than individual ideas at lower levels of the tree, and (2) closely related ideas overlap one another in the design space. The circles for principles, embodiments and details represent the amount of design space explored by an element at the principle ( $ l=3 $ ), embodiment ( $ l=2 $ ) and detail ( $ l=1 $ ) levels, respectively. This figure does not indicate the absolute location of any of the ideas in the design space.

Although drawn as a two-dimensional space, the design tree space has no axes. The absolute location of an idea is arbitrary. However, the relative locations of two ideas are not arbitrary. Closely related ideas (siblings in the design tree) overlap one another. Distantly related ideas (those that are not siblings) do not overlap. The only conclusion we can draw about the distance between non-overlapping ideas is that their separation is greater than the characteristic size of the explored space for an idea at a given level.

The area of the rectangle in the figure represents the amount of design space available for meeting the objective. Each idea explores a part of the area of the design space shown by a circle in the figure. The size of the circle represents $ {\Omega}_l $ , which is the amount of design space explored by an idea at level $ l $ . Principles ( $ l=3 $ ) explore more design space (and thus have larger circles representing $ {\Omega}_3 $ ) than embodiments ( $ l=2 $ , circle of $ {\Omega}_2 $ ), which explore more design space than details ( $ l=1 $ , circle of $ {\Omega}_1 $ ). When ideas are closely related, their circles overlap. When they are distantly related, their circles will not overlap. When circles overlap, the amount of design space uniquely explored by a given idea is reduced by the overlap with closely related ideas.

6.3. Design space for family members

When a first child is added to an element in the tree, a family consisting of that child is created. Each member of a family can be considered to explore an amount of design space (including overlaps) of $ {\Omega}_l $ , which depends on the level of the family. However, due to overlaps with family members, the amount of design space uniquely explored by an idea (called $ {X}_{i,l} $ ) may be less than $ {\Omega}_l $ . We define the member fraction $ {f}_i $ to be $ {X}_{i,l}/{\Omega}_l $ . As shown schematically in Figure 3, the member fraction for the first member of the family ( $ {f}_1 $ ) is 1. The member fraction for family member $ 2 $ will be less than 1 and is denoted $ {f}_2 $ . $ {f}_3 $ , the member fraction for family member $ 3 $ , is less than $ {f}_2 $ . Exact formulas for $ {f}_i $ are provided later. Note that the total design space uniquely allocated to family member $ i $ at level $ l $ is given by $ {X}_{i,l}={f}_i{\Omega}_l $ .

Figure 3.

Schematic representation of member fraction, total fraction and average fraction for a set of three ideas in a family at level $ l $ . For this representation, the area of the circle is $ {\Omega}_l $ , and the fraction is a part of the circle. In part (a), we see the three overlapping ideas on the same level. All three circles have the same explored area of $ {\Omega}_l $ . Member 1 has a member fraction $ {f}_1 $ of 1, as all the space is assumed to be uniquely explored by Member 1. Member 2 has a member fraction $ {f}_2 $ less than 1 due to its overlap with member 1, so its uniquely explored design space is less. Member 3 has a member fraction $ {f}_3 $ less than $ {f}_2 $ due to overlap with both members 1 and 2. Part (b) shows the total design space explored by the family, which is a multiple of $ {\Omega}_l $ called $ {f}_t $ . Part (c) shows the average fraction $ {f}_a $ for each member when we have no basis for determining which idea explores the most design space.

As shown in Figure 3(b), the total design space explored by a family will be greater than the space explored by any single idea in the family. The total family space is a multiple of $ {\Omega}_l $ called the total fraction $ {f}_t $ . $ {f}_t $ is the sum of the member fractions for all family members.

(5) $$ {f}_t=\sum \limits_{i=1}^m{f}_i $$

where $ m $ is the number of family members.

As we add members to the family, the total fraction $ {f}_t $ increases, but the member fraction for each additional member $ {f}_i $ decreases, due to greater overlap of ideas in the family. This is shown schematically in Figure 3.

When evaluating the novelty of design ideas, we number the ideas according to decreasing novelty. The most novel idea then explores the most design space, and as the novelty of ideas decreases, the amount of design space explored by the less-novel idea decreases as well.

When evaluating variety, the numbering scheme for members is arbitrary (i.e., there is no reason for calling one idea member 1 and another idea member 2). This makes it inappropriate to assume that one idea explores more design space than another. In this case, we calculate an average fraction $ {f}_a $ to assign to every member of the family, as shown in Figure 3(c). $ {f}_a $ is the total fraction divided by the number of members.

(6) $$ {f}_a=\frac{f_t}{m}=\frac{\sum_{i=1}^m{f}_i}{m} $$

7.1. Tree variety

1. Axiom TV-1: Adding a unique entry at any level of the tree should increase the total variety of the idea set.

2. Axiom TV-2: Adding a unique entry at a higher level of the tree should increase the total variety more than adding a unique entry at a lower level.

3. Axiom TV-3: Adding a unique entry at a given level of the tree that has many family members should increase the total variety less than adding a unique entry at the same level with few family members.

An example may help explain the meaning of the last axiom. Figure 4 shows partial results of an ideation session focused on improving home security. As shown, an embodiment idea of a bio-inspired mobile robot sentry was generated ( $ {E}_{11} $ ). Detailed ideas for this embodiment included robot dog ( $ {D}_{111} $ ), robot chicken ( $ {D}_{112} $ ), robot lion ( $ {D}_{113} $ ), robot snake ( $ {D}_{114} $ ) and robot cat ( $ {D}_{115} $ ), for a total of five details.

Figure 4.

A sample design tree showing a subset of the ideas generated. The objective is to improve home security. The only principle shown in the figure is detecting intrusion. There are two embodiments shown: $ {E}_{11} $ and $ {E}_{12} $ . $ {E}_{11} $ has four details, with a potential fifth shown in gray. $ {E}_{12} $ has one detail, with a potential second shown in gray. As discussed in the text, adding $ {D}_{122} $ should add more variety to the set than adding $ {D}_{116} $ . Also, if $ {D}_{122} $ and $ {D}_{116} $ are equally novel ideas, adding $ {D}_{122} $ should add more novelty to the set than adding $ {D}_{116} $ .

An alternative embodiment idea is a system of stationary sensors scattered throughout the house ( $ {E}_{12} $ ). The only detailed idea for this embodiment is a camera network ( $ {D}_{121} $ ).

An alternative idea at the detail level for a robot kangaroo ( $ {D}_{116} $ ) should add less variety to the set than an idea at the detail level for a stationary LiDAR detection system ( $ {D}_{122} $ ).

7.2. Tree novelty

1. Axiom TN-1: At a given location in the design tree, an idea with high novelty adds more to the set novelty than an idea with low novelty.

2. Axiom TN-2: A novel idea at a high level of the tree adds more to the set novelty than an equivalent idea at a low level of the tree.

3. Axiom TN-3: An idea with a given novelty in a small family adds more to the set novelty than an idea with the same novelty in a large family.

4. Axiom TN-4: The set novelty should be increased by adding a novel idea. It should be unchanged by adding an idea with zero novelty. As a corollary, the set novelty should never be increased by removing an idea.

7.3. Tree quantity

1. Axiom TU-1: Duplicates of previously counted ideas should not be counted again when determining set quantity.

2. Axiom TU-2: The set quantity should not depend on the tree level at which the ideas are expressed.

3. Axiom TU-3: Organizational elements added to the tree by evaluators should not affect the set quantity.

7.4. Tree quality

1. Axiom TQ-1: High-quality ideas should increase the set quality more than moderate-quality ideas.

2. Axiom TQ-2: Removing low-quality ideas from the set should not increase the set quality; adding low-quality ideas to the set should not decrease the set quality.

3. Axiom TQ-3: Regardless of the number of high-quality ideas in the set, adding a high-quality idea should increase the set quality.

Note that the tree quality axioms are identical to the general quality axioms, as the quality axioms refer to neither the level of abstraction nor the distance from adjacent ideas. However, we list them here for convenience.

8.1. Element variety

As described in “Design Space for Family Members,” each idea placed in the tree to form an element explores a certain region of design space, the amount of which depends on the level of the element and the number of siblings. We call the amount of design space explored by an element the explored space $ {X}_{i,l} $ .

(7) $$ {X}_{i,l}={f}_i{\Omega}_l $$

where $ {\Omega}_l $ is the amount of design space explored by each idea at level $ l $ , and $ {f}_i $ is the fraction of $ {\Omega}_l $ uniquely explored by member element $ i $ in the family.

In this section, we develop functions for $ {\Omega}_l $ and $ {f}_i $ that can be used with simple aggregation functions to meet the variety axioms. We will start with $ {f}_i $ .

The function $ {f}_i $ should have a value of 1 for the first family member, and decrease with increasing value of $ i $ . However, it should have a non-zero asymptote, because regardless of the number of family members, an additional unique idea will increase the amount of design space explored.

The function chosen to meet these criteria $ {f}_i $ is:

(8) $$ {f}_i=\alpha +\left(1-\alpha \right){\beta}^{\left(i-1\right)} $$

where $ \alpha $ and $ \beta $ are parameters with values between 0 and 1 that are used to adjust the amount of design space allocated to members of a family.

Note that for $ i=1 $ , $ {f}_i=1 $ ; for $ i=\infty $ , $ {f}_i $ has the limiting value $ \alpha $ . Thus $ \alpha $ describes the lower limit of the design space a single idea can occupy. $ \beta $ is a shape parameter that defines how rapidly $ {f}_i $ falls toward its limiting value of $ \alpha $ . The choice of specific values for $ \alpha $ and $ \beta $ is discussed in “Choosing Values for Evaluation Parameters.”

The total fraction for the family is given by the sum of the member fractions in the family:

(9) $$ {f}_t=\sum \limits_{i=1}^m{f}_i=\sum \limits_{i=1}^m\left[\alpha +\left(1-\alpha \right){\beta}^{\left(i-1\right)}\right]= m\alpha +\left(1-\alpha \right)\sum \limits_{k=0}^{m-1}{\beta}^k= m\alpha +\left(1-\alpha \right)\frac{\beta^m-1}{\beta -1} $$

where $ m $ is the number of family members (the identity for this finite series is found in Abramowitz & Stegun (Reference Abramowitz and Stegun1972)).

In order to meet Axiom TV-2, $ {\Omega}_l $ is chosen to ensure that the minimum possible explored design space for an idea at level $ l+1 $ is greater than the maximum possible explored space for an element at level $ l $ . The minimum explored design space at level $ l+1 $ is for an idea with infinite family members, where the member fraction is $ \alpha $ . Thus the minimum value of explored design space on level $ l+1 $ is $ {X}_{\infty, l+1}=\alpha {\Omega}_{l+1} $ . The maximum explored design space at level $ l $ is for the first idea in a family $ {X}_{1,l}={\Omega}_l $ . Setting $ {X}_{\infty, l+1}={X}_{1,l} $ we obtain a recurrence relation for $ \Omega $ :

(10) $$ {\Omega}_{l+1}={\Omega}_l/\alpha $$

Without loss of generality, we can define $ {\Omega}_1=1 $ , then

(11) $$ {\Omega}_l={\alpha}^{1-l}=\frac{1}{\alpha^{l-1}} $$

With this substitution, equation (7) becomes

(12) $$ {X}_{i,l}={f}_i{\Omega}_l=\frac{\alpha +\left(1-\alpha \right){\beta}^{\left(i-1\right)}}{\alpha^{l-1}} $$

When calculating variety, there is no basis for deciding which idea is the first, and which is the last, so we do not use a different value $ {f}_i $ for each family member. Instead, we use the average fraction for the family $ {f}_a $ , given by

(13) $$ {f}_a=\frac{f_t}{m}=\frac{m\alpha +\left(1-\alpha \right)\frac{\beta^m-1}{\beta -1}}{m} $$

We then define the variety for each idea in the family to be an average amount of explored design space per family member $ {X}_{a,l} $

(14) $$ {V}_E={X}_{a,l}={f}_a{\Omega}_l=\frac{m\alpha +\left(1-\alpha \right)\frac{\beta^m-1}{\beta -1}}{\alpha^{l-1}m} $$

As described in “Choosing Values for Evaluation Parameters,” for this paper $ \beta =0.32 $ and $ \alpha =0.46 $ . Table 1 lists the member fraction, total fraction and average fraction for members 1–10 given these values of $ \beta $ and $ \alpha $ .

Table 1.

Member fraction, total fraction and average fraction for family sizes up to 10 with $ \alpha =0.46 $ and $ \beta =0.32 $

8.2. Branch variety

Starting at the leaf elements and working up to higher levels of the tree, the element variety can be aggregated into branch variety. For any element, we can calculate a branch variety, which is the sum of the element variety and the branch varieties for all children of the element.

The branch variety of an element is given by

(15) $$ {V}_B={V}_E+\sum {V}_{B_c} $$

where $ {V}_B $ is the branch variety of an element, and $ {V}_{B_c} $ is the branch variety of a child of the element.

For convenience, we can show the descendant variety $ {V}_D $ for an element, which is the sum of the branch varieties for all children of the element.

(16) $$ {V}_D=\sum {V}_{B_c}=\sum \left({V}_{E_c}+{V}_{D_c}\right) $$

where $ {V}_{E_c} $ and $ {V}_{D_c} $ are the element variety and descendant variety, respectively, for a child of the element.

8.3. Total variety

The total variety for a tree ( $ V $ ) is equal to the sum of the branch varieties for all elements at the highest level of the tree.

8.4. Meeting the variety axioms

We can demonstrate that all three axioms are met by the given aggregation function.

Axiom TV-1: Adding a unique entry to any level of the tree should increase the total variety of the tree.

This axiom is met because each idea (regardless of its level) has a positive element variety, and the total variety is the sum of the element varieties for all elements. Thus, adding an element will increase the total variety.

Axiom TV-2: Adding a unique entry to a higher level of the tree should increase the total variety more than adding a unique entry to a lower level.

This axiom is met because the maximum element variety added by an idea at level $ l $ is

(17) $$ {V}_{E_{{\mathit{\max}}_l}}={X}_{1,l}={\Omega}_l={\alpha}^{1-l} $$

The minimum variety added by a new element $ i $ at level $ l+1 $ is given by

(18) $$ \underset{i\to \infty }{\lim}\left[\alpha +\left(1-\alpha \right){\beta}^{\left(i-1\right)}\right]{f}_i=\alpha {\Omega}_{l+1}={\alpha \alpha}^{1-\left(l+1\right)}={\alpha}^{1-l} $$

For a finite design tree (and all design trees are finite) $ i<\infty $ , so $ {V}_{E_{i_{{\mathit{\min}}_{l+1}}}}>{V}_{E_{{\mathit{\max}}_l}} $ .

Axiom TV-3 Adding a unique entry at a given level to a family with many members should increase the total variety less than adding a unique entry at the same level to a family with few members.

To show that this axiom is met, consider the effect of adding an idea $ A $ at level $ l $ in a family with $ {m}_1 $ existing members. The added variety will be $ {V}_E $ for this new idea, which is $ {V}_{E_A}=\left(\alpha +\left(1-\alpha \right){\beta}^{\left({m}_1+1-1\right)}\right){\Omega}_l $ . Now consider adding a different idea $ B $ also at level $ l $ in a family with $ {m}_2 $ members, where $ {m}_2<{m}_1 $ . The added variety will be $ {V}_{E_B}=\left(\alpha +\left(1-\alpha \right){\beta}^{\left({m}_2+1-1\right)}\right){\Omega}_l $ . If $ \Delta V={V}_{E_B}-{V}_{E_A} $ is positive, the axiom is met.

(19) $$ \Delta V=\left(\alpha +\left(1-\alpha \right){\beta}^{\left({m}_2\right)}\right){\Omega}_l-\left(\alpha +\left(1-\alpha \right){\beta}^{\left({m}_1\right)}\right){\Omega}_l=\left(1-\alpha \right)\left({\beta}^{\left({m}_2\right)}-{\beta}^{\left({m}_1\right)}\right){\Omega}_l $$

As all three terms in the product above are positive, $ \Delta V $ is positive. Thus, Axiom V-3 is met.

Note that the variety axioms are all met regardless of the specific values chosen for $ \alpha $ and $ \beta $ . However, the actual numerical value for the variety of a set of ideas will vary with $ \alpha $ and $ \beta $ . The same is true for other parameters in our evaluation for novelty and quality. The rationale for the specific values chosen for these evaluation parameters is discussed in “Choosing Values for Evaluation Parameters.”

9.1. Types of novelty

As mentioned previously, there are two different types of novelty with which we are concerned, idea novelty and element novelty. The novelty of branches and complete sets of ideas can be calculated from the element novelty of the elements in the tree.

To calculate the novelty of a set of ideas, we must calculate idea novelty, element novelty and branch novelty. We address each of these novelty calculations in turn.

9.2. Idea novelty

Organizational elements (parent items that are added by the evaluators for the purpose of organizing the tree) are given a novelty value of 0. This ensures that the elements added by evaluators do not contribute to the total set novelty.

For each idea element (ideas generated by the design team) in the tree, the novelty of the idea is evaluated by methods such as those used by SVS, or by the method we propose below. Any consistent method for calculating idea novelty for an element will work with the calculations described here.

Although not required, we find it helpful to scale idea novelty to be in the interval [0,1]. One can think of the novelty score as the fraction of design space occupied by the idea that lies outside of the known design space. If the novelty is 0, the idea occupies no space outside the known space. If the novelty is 1, the idea lies entirely outside the known design space.

9.3. Evaluating idea novelty

In this section, we present a new method for evaluating idea novelty. This method is neither the SVS a priori method nor the SVS a posteriori method. Like the SVS a priori method, this method uses the rater’s experience to assign a novelty rating. However, in contrast to the SVS a priori method, the rater makes no judgments as to what ideas would be novel before making the rating. Similar to the SVS a posteriori method, each of the ideas in the set is evaluated by the rater. However, in contrast to the SVS a posteriori method, the rater’s judgment is used for each of the ideas, rather than just the frequency of occurrence of the ideas. This method allows the rater to focus on the ideas present in the idea set and follows a straightforward, albeit subjective, method.

The rater completes four steps to evaluate each idea. The four steps are:

1. 1 Considering the idea exclusive of other ideas in the current set, how commonly do you believe the idea is used or proposed in this design context? Give a response of C, R or N (common, rare or never).

2. 2 Describe the mechanism or technique by which the idea operates to achieve the design objective.

3. 3 Can you identify another context (other than this design context) where this idea (or a very similar idea) uses the mechanism described to achieve an objective? If so, list this other context. If not, the response for this step is “None,” and the response for the next step is “N.”

4. 4 How commonly do you believe this idea is used or proposed as described in this other context? Provide a response of C, R or N (common, rare or never).

In steps 1 and 4, where we evaluate the prevalence of an idea, we use the phrase used or proposed because many common ideas have never been actually implemented. For example, time machines and faster-than-light travel are well-known in science fiction, even though they have never been used in a real product. So, the idea of a time machine should not be considered rarely or never used.

The “other context” in steps 3 and 4 refers to any usage context for the idea with which the rater is familiar. There is no unique right answer to the other context question. However, the other context is specifically identified in order to help justify the prevalence evaluation.

It is possible that the evaluator cannot identify another context in which the idea is used. In this case, the response for question 3 becomes “None” and the answer to question 4 automatically becomes “N” (never).

After the rater has provided ratings of C, R or N for the idea in the design context and a similar idea in some other familiar context, the software assigns point values for both contexts, $ {P}_d $ for the design context and $ {P}_o $ for the other context. The point values for a rating of C, R or N will be $ {S}_c $ , $ {S}_r $ or $ {S}_n $ , respectively. It is required that $ {S}_c<{S}_r<{S}_n $ , so that rarer ideas will have a higher point score. Specific values of $ {S}_c $ , $ {S}_r $ and $ {S}_n $ are chosen as described in “Choosing Values for Evaluation Parameters.”

The novelty point value for the idea is calculated as

(20) $$ P=\left\{\begin{array}{ll}{P}_d{P}_o,& \mathrm{if}\;{P}_d\ge {P}_o\\ {}{P}_d^2,& \mathrm{if}\;{P}_d<{P}_o\end{array}\right. $$

If the current design context has a lower score than the other context, the composite score is the square of the score for the design context, because rarity in another context does not increase the novelty in the design context.

The composite score $ P $ is then normalized to the interval [0,1] by calculating the ratio

(21) $$ n=\frac{P-{P}_{min}}{P_{max}-{P}_{min}} $$

where $ P $ is the composite score, and $ {P}_{max}={S}_n^2 $ and $ {P}_{min}={S}_c^2 $ are the maximum and minimum possible composite scores.

This composite scoring method was chosen because it allows consideration of both the specific use of an idea (the current design context) and the general use of an idea (the other context). An idea that is common in both the current context and general use has very little novelty. An idea that is rare in the current context but common in general use has some novelty. An idea that is never seen in the current context and is rare in general use has significant novelty.

9.4. Element novelty

The element novelty is the product of the design space explored by the idea and the novelty of the idea (which represents the fraction of the explored space that is novel). The amount of design space explored by an idea has been previously defined as $ {X}_{i,l} $ . In contrast with the variety calculation, when calculating set novelty, there is a rational basis for choosing the order of family members. We consider the first member of the family to be the one with the highest novelty. Thus, we determine the member index $ i $ by sorting the members in decreasing order of idea novelty. We then calculate the design space allocated to the idea, not by the average fraction, but instead by the individual fraction. Thus, the design space allocated to family member $ i $ is given by

(22) $$ {X}_{i,l}={f}_i{\Omega}_l $$

If there are multiple family members with equal idea novelty, $ {f}_i $ is the average fraction for all ideas having the same idea novelty (but not including any ideas with different novelty).

The element novelty is given by

(23) $$ {N}_E={nX}_{i,l}={nf}_i{\Omega}_l $$

9.5. Branch and set novelty

Branch novelty is the sum of the element novelty of an element and the element novelty of its descendants ( $ {N}_D $ ).

(24) $$ {N}_B={N}_E+{N}_D $$

The element novelty of the descendants of an element is the sum of the branch novelties of the element’s children

(25) $$ {N}_D=\sum {N}_{B_c} $$

Therefore, branch novelty is given by

(26) $$ {N}_B={N}_E+\sum {N}_{B_c}={N}_E+\sum \left({N}_{E_c}+{N}_{D_c}\right) $$

The total novelty for a set ( $ N $ ) is the sum of the branch novelties for all elements at the highest level of the tree.

9.6. Meeting the novelty axioms

We can demonstrate that all four novelty axioms are met by the given aggregation function.

Axiom N-1: At a given location in the design tree, an idea with high novelty adds more to the set novelty than an idea with low novelty.

Consider two possible alternative ideas at a given location in design space. The first idea has idea novelty $ {n}_A $ ; the second has idea novelty $ {n}_B $ . Assume idea A is more novel than idea B, so $ {n}_A>{n}_B $ . Thus, we can define $ {n}_A={n}_B+\delta {n}_{AB} $ where $ \delta {n}_{AB} $ is positive.

Both ideas are in the same location in design space (member of the same family at level $ l $ ). Define the member number for idea $ A $ as $ {i}_A $ and the member number for idea $ B $ as $ {i}_B $ . Because $ {n}_A>{n}_B $ , $ {i}_A\le {i}_B $ and $ {f}_{i_A}\ge {f}_{i_B} $ . Thus, we can define $ {f}_{i_A}={f}_{i_B}+\delta {f}_{AB} $ , where $ \delta {f}_{AB} $ is non-negative.

Let $ \Delta N $ be the difference in element novelty between the alternative ideas A and B. If the difference is positive, idea A adds more novelty than idea B.

(27) $$ {\displaystyle \begin{array}{c}\Delta N={N}_{E_A}-{N}_{E_B}\\ {}={n}_A{f}_{i_A}{\Omega}_l-{n}_B{f}_{i_B}{\Omega}_l\\ {}=\left({n}_A{f}_{i_A}-{n}_B{f}_{i_B}\right){\Omega}_l\\ {}=\left[\left({n}_B+\delta {n}_{AB}\right)\left({f}_{i_B}+\delta {f}_{AB}\right)-{n}_B{f}_{i_B}\right]{\Omega}_l\\ {}=\left[{n}_B\delta {n}_{AB}+\delta {n}_{AB}{f}_{i_B}+\delta {n}_{AB}\delta {f}_{AB}\right]{\Omega}_l\\ {}=\left[\delta {f}_{AB}\left({n}_B+\delta {n}_{AB}\right)+\delta {n}_{AB}{f}_{i_B}\right]{\Omega}_l\end{array}} $$

The first term in the square brackets is positive (if $ \delta {f}_{AB}>0 $ ) or zero (if $ \delta {f}_{AB}=0 $ ). The second term in the square brackets is positive, as both of its factors are positive. Thus, the sum in the brackets is positive, $ \Delta N $ is positive and Axiom N-1 is met.

Axiom TN-2: A novel idea at a high level of the tree adds more to the set novelty than an idea with equivalent novelty at a low level of the tree.

Consider two alternative ideas having equal idea novelty $ n $ and member fraction $ {f}_i $ , but at different levels in the tree. Idea A is at level $ {l}_A $ , and idea B is at level $ {l}_B $ , where $ {l}_A>{l}_B $ .

We calculate the ratio of the element novelties $ {N}_{E_A}/{N}_{E_B} $ . If the ratio is greater than 1, idea A adds more set novelty than idea B.

(28) $$ \frac{N_{E_A}}{N_{E_B}}=\frac{n\left[\alpha +\left(1-\alpha \right){\beta}^{\left(i-1\right)}\right]{\Omega}_{l_A}}{n\left[\alpha +\left(1-\alpha \right){\beta}^{\left(i-1\right)}\right]{\Omega}_{l_B}}=\frac{\Omega_{l_A}}{\Omega_{l_B}}=\frac{\alpha^{1-{l}_A}}{\alpha^{1-{l}_B}}={\alpha}^{-\left({l}_A-{l}_B\right)} $$

where $ {l}_A-{l}_B $ is positive. By definition, $ \alpha <1 $ , so $ {\alpha}^{-\left({l}_A-{l}_B\right)}>1 $ , and Axiom Axiom N-2 is met.

Axiom N-3: An idea with a given novelty located far from other ideas in the design tree adds more to the set novelty than an idea with the same novelty located close to other ideas in the design tree.

In the tree-based design space, closely related ideas are placed in the same family. As the number of ideas in a family increases, the overlap between ideas increases, so the ideas are more closely related.

Remember that, for novelty calculations, we consider that the more-novel ideas are added first, and the less-novel ideas are added later. Consider two alternative ideas having equal idea novelty $ n $ , and equal level $ l $ . Idea A is placed in a small family $ (A) $ with a previous total of $ {m}_A $ members, and idea B is placed in a large family $ (B) $ with a previous total of $ {m}_B $ members, where $ {m}_A<{m}_B $ .

When idea A is placed into family $ A $ , it becomes member $ {m}_A+1 $ (because it is the least-novel idea in the family at this point). When idea B is placed into family $ B $ it becomes member $ {m}_B+1 $ .

The element novelty of idea A is given by

(29) $$ {N}_{E_A}={nf}_{m_A+1}{\Omega}_l $$

and the element novelty of idea B is given by

(30) $$ {N}_{E_B}={nf}_{m_B+1}{\Omega}_l $$

We calculate the ratio of the element novelties $ {N}_{E_A}/{N}_{E_B} $ . If the ratio is greater than 1, idea A adds more set novelty than idea B.

(31) $$ \frac{N_{E_A}}{N_{E_B}}=\frac{f_{m_A+1}}{f_{mB+1}} $$

Because $ {m}_A+1<{m}_B+1 $ , $ {f}_{m_A+1}>{f}_{mB+1} $ , so the ratio is greater than 1, and Axiom TN-3 is met.

Axiom TN-4 The set novelty should never be reduced by adding an idea. As a corollary, the set novelty should never be increased by removing an idea.

When an idea is added to the set, the element novelty of the idea is added to the set. The element novelty $ {N}_E $ is given by

(32) $$ {N}_E={nf}_i{\Omega}_l $$

As all terms in Eq. (32) are non-negative (it is possible for $ n $ to be 0), $ {N}_E $ is greater than or equal to 0, and thus the novelty cannot be reduced by adding any idea. As a corollary, every idea in a set has a non-negative element novelty, so eliminating an idea cannot increase the set novelty.

10.1. Idea uniqueness

We define the uniqueness of an idea $ u $ . $ u $ has two possible values. If an idea has at least one feature that is different from all other ideas in the set, $ u=1 $ . If all features of the idea are shared in common with at least one other idea in the set, $ u=0 $ .

There are four general ways that two ideas can share common features, as illustrated by the Venn diagrams of features in Figure 5. In case (a), the two ideas share no common features, so they are both unique. In case (b), all of the features are found in both ideas, which means the ideas are duplicates. Duplicates should be combined into a single idea that is placed into the OPED tree.

Figure 5.

Four different relationships two ideas can have relative to sharing common features.

In case (c), all of the features in idea 1 are found in idea 2, but idea 2 has some features not found in idea 1. In this case, idea 2 is unique but idea 1 is not, as it is contained in idea 2. This case is generally found in a parent–child tree relationship. The child idea 2 contains all the features of the parent, plus some additional features.

In case (d), ideas 1 and 2 share some common features, but each has features not found in the other. Both ideas are unique. This case is generally found in a sibling relationship. The parent contains all the common features, but each child has some unique features.

With this understanding of sharing common features, we can define the value of $ u $

(33) $$ u=\left\{\begin{array}{cc}0,& \hskip-0.16em \mathrm{if}\hskip0.5em {m}_c>0\\ {}1,& \hskip0.22em \mathrm{if}\hskip0.5em {m}_c=0\end{array}\right. $$

where $ {m}_c $ is the number of children. Note that any organizational element added to the tree will have children, so $ u $ will be zero for organizational elements and organizational elements will not add to the set quantity.

10.2. Evaluating set quantity

The set quantity is the count of the unique ideas in the tree, or the sum of the idea uniqueness.

(34) $$ U=\sum {u}_k $$

where the sum is over all elements in the tree.

The branch quantity is the count of unique ideas in a branch, or the sum of the idea uniqueness and the branch quantity of the children:

(35) $$ {U}_B=u+\sum {U}_{B_C} $$

Note that we have no children if $ u $ is non-zero, so only one of the terms will apply for any branch.

10.3. Meeting quantity axioms

Axiom TU-1: Duplicates of previously counted ideas should not be counted again when determining set quantity.

Duplicate ideas are combined into a single idea, which has a single uniqueness score (0 if it has children, 1 if it has no children).

Axiom TU-2: The set quantity should not depend on the level of detail at which the ideas are expressed.

It does not matter the level at which a unique idea is expressed; all unique ideas have a quantity score of 1, regardless of the level.

Axiom TU-3: Organizational elements added to the tree by evaluators should not affect the set quantity.

Because all parent ideas share elements with their children, they have a uniqueness score $ u $ of 0. Elements added to organize the tree are always parent elements and will thus not add to the set quantity.

11.1. Idea quality

Cheeley et al. (Reference Cheeley, Weaver, Bennetts, Caldwell and Green2018) proposed a quality metric including feasibility and effectiveness. They note that having only two dimensions to rate increases the efficiency; that having separate evaluations for two important aspects of quality supports repeatability; and that these dimensions define quality in a manner consistent with the literature. For this reason, we adopted these dimensions for our quality rating.

We propose a simple, three-level evaluation for feasibility and effectiveness that combines to give a quality evaluation. It is valuable to recognize that this evaluation method is not for the purpose of choosing or improving specific design solutions. Rather, it provides details for assessing the overall quality of the ideation, as manifest in the resulting idea set.

Some researchers have found that a rater’s ability to evaluate an idea can be influenced by how well the idea is represented – such as the level of detail of the idea description (Hannah et al. (Reference Hannah, Joshi and Summers2012)). As seen in the next section, this paper describes a simple 3-point quality evaluation scale that can be completed for ideas described at various levels of detail. The present paper does not attempt to characterize the degree to which the level of idea representation affects a rater’s ability to assess it using scales more detailed than the simple 3-point feasibility and effectiveness evaluations described in the next sections.

To provide a numerical rating of idea quality, we assign feasibility ( $ F $ ) and effectiveness ( $ E $ ) scores based on answers to questions about feasibility and meeting design specifications:

• • Considering both technical and resource limitations, how feasible is it to implement this idea to solve the design problem?

  1. a. Certainly feasible: $ F={F}_c>{F}_p $

  2. b. Possibly feasible: $ F={F}_p>0 $

  3. c. Not feasible: $ F={F}_n=0 $

• • If it were implemented, what impact would the idea have in meeting the design requirements?

  1. d. Major positive impact: $ E={E}_{maj}>{E}_{min} $

  2. e. Minor positive impact: $ E={E}_{min}>0 $

  3. f. No impact or negative impact: $ E={E}_n=0 $

Because the composite score is the product of the feasibility and effectiveness scores, and infeasible or ineffective ideas have zero quality, $ {F}_n={E}_n=0 $ Feasibility and effectiveness are evaluated only for idea elements. For organizational elements, $ E $ and $ F $ remain at the default value of 0. Thus, organizational elements have $ q=0 $ and do not add to the set quality.

The feasibility and effectiveness scores are multiplied to give a composite idea quality score. Composite scores lie in the range $ \left[0,{F}_c{E}_{maj}\right] $ . Infeasible ideas and ineffective ideas have a composite score of 0. Thus, if an idea is either infeasible or has no impact on meeting requirements, it is only necessary to answer one question.

The idea quality score $ q $ is calculated by normalizing the composite score to the range of [0,1] by dividing by the maximum possible composite score of $ {F}_c{E}_{maj} $ .

(36) $$ q=\frac{FE}{F_c{E}_{maj}} $$

For this paper, $ {F}_c={E}_{maj}=2 $ , and $ {F}_p={E}_{min}=1 $ . The rationale behind these choices is explained in “Choosing Values for Evaluation Parameters.”

11.2. Element quality

To calculate the set quality, we first define the element quality, which is the quality of an idea in the context of the design tree. Element quality will be zero for all low-quality elements, and it will be positive for all elements that are not considered to be low quality.

Low-quality elements are elements with an idea less than or equal to a defined quality threshold $ {q}_{th} $ . Ideas with quality scores above $ {q}_{th} $ are considered to have positive quality, meaning they will add to the set quality. For this paper, we choose $ {q}_{th}=0.2 $ . The justification for this choice is given in “Choosing Values for Evaluation Parameters.”

The element quality $ {Q}_E $ is given by

(37) $$ {Q}_E=\left\{\begin{array}{ll}0,& \mathrm{if}\;q\le {q}_{th}\\ {}\frac{q-{q}_{th}}{1-{q}_{th}},& \mathrm{if}\;q>{q}_{th}\end{array}\right. $$

Note that, like $ q $ , $ {Q}_E $ lies in the interval [0,1]. The idea quality and element quality questions and results are shown in Figure 6.

Figure 6.

A graphical representation of the quality calculations. Feasibility ratings and scores are shown along the top; Effectiveness ratings and scores are shown on the left. Idea quality $ q $ and element set quality $ {Q}_E $ are shown in the intersection of the Feasibility column and the effectiveness row. High-quality idea scores are shaded. For this paper, $ {q}_{th}=0.2 $ .

Note that this definition allows for low-quality ideas (those with $ q\le {q}_{th} $ ) that do not add to the set quality. It also allows for a range of element quality scores for those that exceed the threshold. Thus, an idea that greatly exceeds the threshold adds more quality to the set than an idea that only slightly exceeds the threshold.

Also, note that the element quality $ {Q}_E $ can be calculated regardless of the method used to assign an idea quality $ q $ . All that is needed to calculate $ {Q}_E $ is a method for calculating $ q $ and a threshold value for quality ideas $ {q}_{th} $ .

11.3. Branch and set quality

Because the element quality will apply to all elements in the branch, the branch quality is equal to the product of the element quality and the branch quantity plus the branch qualities of its children:

(38) $$ {Q}_B={U}_B{Q}_E+\sum {Q}_{B_C} $$

The set quality score is the sum of the branch qualities for all elements at the highest level of the tree.

11.4. Meeting the quality axioms

We can demonstrate that all three quality axioms are met by the given aggregation function.

Axiom TQ-1: High-quality ideas should affect set quality more than moderate-quality ideas.

Items with a high quality have a higher $ {Q}_E $ than moderate quality ideas. Therefore, they contribute more to the set quality.

Axiom TQ-2: Removing low-quality ideas from the set should not increase the set quality; adding low-quality ideas to the set should not decrease the set quality.

No ideas have a negative element quality score. Therefore, it is not possible to raise the set quality score by removing an idea. The lowest possible element quality score is zero, so adding low-quality ideas will not reduce the set quality.

Axiom TQ-3: Regardless of the number of high-quality ideas in the set, adding a high-quality idea should increase the set quality.

Every idea with $ q>{q}_{th} $ has a positive element quality $ {Q}_E $ , which will increase the set quality $ Q $ .

12.1. Design space exploration parameters

There are two parameters that govern $ {X}_{i,l} $ , the amount of design space explored by an idea that is member $ i $ in a family at level $ l $ : $ \alpha $ and $ \beta $ . $ \alpha $ is the minimum fraction of $ {\Omega}_l $ that can be explored by an idea at level $ l $ . In addition, the recurrence relation in Eq. 10 shows that as the idea level increases by one, $ {\Omega}_l $ grows by a factor of $ 1/\alpha $ . The equations have been defined so that as long as both $ \alpha $ and $ \beta $ lie between 0 and 1, the axioms will hold. There must be some additional considerations in selecting values for $ \alpha $ and $ \beta $ .

We have chosen two characteristics of design space that we believe are reasonable and allow us to find specific values for $ \alpha $ and $ \beta $ . The first is the concept of a “full” family. Although there is technically no limit to the number of family members, in practice, we believe the benefit of adding more family members drops off significantly as the number of members increases. As shown in Equation 8, the fraction for a family member is $ \alpha $ plus a bonus fraction $ {\epsilon}_i={\beta}^{i-1}\left(1-\alpha \right) $ that decreases exponentially with $ i $ . Figure 7 shows how $ {\epsilon}_i $ varies with $ i $ and $ \beta $ .

Figure 7.

The variation in $ {\epsilon}_i $ with family member for multiple values of $ \beta $ . The smaller the value of $ \beta $ , the more quickly the value of $ {\epsilon}_i $ drops. A family can be considered full when the value of $ {epsilon}_i $ drops below a user-chosen value of $ {\epsilon}_{i_f} $ .

A full family will have $ {i}_f $ members, and the value of $ {\epsilon}_{i_f} $ should be small. $ \beta $ can be calculated from $ {i}_f $ and $ {\epsilon}_{i_f} $ .

(39) $$ \beta ={\epsilon}_{i_f}^{1/\left({i}_f-1\right)} $$

Table 3 shows the values of $ \beta $ for various values of $ {i}_f $ and $ {\epsilon}_{i_f} $ . We chose $ {i}_f=3 $ and $ {\epsilon}_{i_f}=0.1 $ which gives a value of $ \beta =0.32 $ .

Table 3.

Values of $ \beta $ that give the specified value of $ {\epsilon}_{i_f} $ at a given family size $ {i}_f $

The second characteristic of design space that we believe is reasonable is that $ \alpha $ should be chosen such that the total design space occupied by a full family of size $ {i}_f $ is equal to the design space occupied by the parent. The total design space occupied by a family at level $ l $ is given by $ {f}_t{\Omega}_l $ , where $ {f}_t $ is defined in Equation 9. The maximum design space occupied by the parent of a family at level $ l $ is $ {\Omega}_{l+1} $ . Thus

(40) $$ {\Omega}_l\left[{i}_f\alpha +\left(1-\alpha \right)\frac{\beta^{i_f}-1}{\beta -1}\right]={\Omega}_{l+1} $$

Dividing both sides of the equation by $ {\Omega}_{l+1} $ and applying Equation 10, we obtain

(41) $$ \alpha \left[{i}_f\alpha +\left(1-\alpha \right)\frac{\beta^{i_f}-1}{\beta -1}\right]=1 $$

This equation can be solved numerically to determine a value for $ \alpha $ . Table 4 shows values of $ \beta $ and $ \alpha $ for $ {\epsilon}_{i_f}=0.1 $ and various values of $ {i}_f $ .

Table 4.

Values of $ \beta $ and $ \alpha $ that give equal design space for the parent and the children for $ {\epsilon}_{i_f}=0.1 $ and various values of $ {i}_f $ . Values of $ \alpha $ and $ \beta $ have been rounded to two decimal places

We note that in many early-stage design trees, families are relatively small. We also wish to keep $ \alpha $ between 0.4 and 0.5, to keep $ {\Omega}_l $ from increasing too rapidly. We chose a full family size of $ {i}_f=3 $ and $ {\epsilon}_{i_f}=0.1 $ , which gives us $ \beta =0.32 $ and $ \alpha =0.46 $ . Other values for $ {i}_f $ and $ {\epsilon}_{i_f} $ could be chosen, leading to different calculated values for $ \beta $ and $ \alpha $ .

12.2. Novelty parameters

There are three parameters that must be chosen to calculate novelty scores using our method. These are $ {S}_c $ , $ {S}_r $ and $ {S}_n $ , the numerical scores for a common, rare and never-seen idea. We choose $ {S}_c=1 $ , because we multiply two scores together to obtain the novelty points, and 1 is the multiplicative identity.

We chose to use a geometric series for the values of $ {S}_c $ , $ {S}_r $ and $ {S}_n $ in order to raise the score of rare ideas. We chose to use the smallest integral geometric series of $ {2}^n $ giving scores of 1, 2 and 4 for $ {S}_c $ , $ {S}_r $ and $ {S}_n $ , respectively.

12.3. Quality parameters

There are seven parameters to be chosen to calculate quality scores: $ {F}_n $ , $ {F}_p $ , $ {F}_c $ , $ {E}_n $ , $ {E}_{min} $ , $ {E}_{maj} $ and $ {q}_{th} $ . An idea that is infeasible has zero quality, so $ {F}_n=0 $ . Similarly, an idea that has no effect or a negative effect on meeting requirements has zero quality, so $ {E}_n $ = 0. Equal weighting of feasibility and effectiveness when determining quality requires $ {F}_p={E}_{min} $ and $ {F}_c={E}_{maj} $ . For simplicity, we choose a ratio scale starting at 0 and integer values, which leads to $ {F}_p={E}_{min}=1 $ and $ {F}_c={E}_{maj}=2 $ . Note that because of the normalization found in Equation 36, the score is insensitive to the values chosen, as long as the scale is a ratio scale.

$ {q}_{th} $ is chosen to establish the minimum quality level that can be considered to have a non-zero quality. There are four possible idea quality scores $ q $ : 0, 0.25, 0.5 and 1. A score of 0 is given to an idea that is infeasible or will have no impact or a negative impact on meeting requirements. Such ideas are not quality ideas, so $ {q}_{th}>0 $ . A score of 0.25 is given to an idea that is possibly feasible and will have a minor positive impact on meeting requirements. Such ideas are quality ideas, but not high-quality ideas. If $ {q}_{th}\ge 0.25 $ , these ideas would have $ {Q}_E=0 $ , which means $ {q}_{th}<0.25 $ . The smaller the value of $ {q}_{th} $ , the higher the value of $ {Q}_E $ for an idea with the minimum non-zero quality rating. A choice of $ {q}_{th}=0.2 $ gives an idea of major impact or certainly feasible a $ {Q}_E $ score six times that of one with the minimum non-zero quality rating, while an idea having both major impact and certain feasibility has a $ {Q}_E $ score 16 times higher. We believe this gives an appropriate weight to truly high-quality ideas.

13.1. Quantity and variety

To compare our method with the SVS method, we will use a tree that is modified from figure 4 in Shah et al. (Reference Shah, Smith and Vargas-Hernandez2003), shown in Figure 8. The SVS tree in part (a) has four levels, with level 1 at the top and level 4 at the bottom. A box at a given level represents an idea at that level that can be used to solve the problem. The number in the box shows the number of design solutions that use the referenced idea as part of their design. The numbers on the branches indicate the contribution of the branch to the sum $ {b}_k $ . The SVS weights of the four levels are shown as $ S1=10 $ , $ S2=6 $ , $ S3=3 $ and $ S4=1 $ .

Figure 8.

A comparison of quantity and variety for a design tree evaluated by (a) the SVS method, and (b) the axiom-based method proposed in this paper.

SVS quantity is calculated for each box by summing the numbers in the children of the box. The number in the top box represents the total quantity of ideas in the tree, so $ n=12 $ .

SVS variety is calculated as $ {M}_3=\sum {S}_k{b}_k/n $ :

$$ {M}_3=\left(1\cdot 3+3\cdot 7+6\cdot 5+10\cdot 2\right)/12=6.17 $$

Note that the $ n $ in the denominator means that an average variety is being calculated, so reducing the idea count by eliminating a low-variety idea can raise the variety score, thus violating Axiom V-1.

The tree shown in part (b) also has four levels, but level 1 is the lowest level, and level 4 is the highest level. The numbers in the boxes are the branch quantity $ {U}_B $ for the branch headed by the box. The numbers are almost the same as SVS, but the second box from the left on the lowest level has 1 in the axiom-based tree, where the SVS tree has 2. This is because we consider only unique ideas, and two ideas that cannot be distinguished at the lowest level of the tree cannot both be unique. Thus, there is duplication, and we count only a single idea.

The weights for each level of the axiom-based tree are not arbitrary values of $ 1 $ , $ 3 $ , $ 6 $ and $ 10 $ , but rather values of $ {\Omega}_l $ chosen to ensure the tree will meet Axiom TV-2: $ 1 $ , $ 1/\alpha $ , $ 1/{\alpha}^2 $ and $ 1/{\alpha}^3 $ , which for a value of $ \alpha =0.46 $ are $ 1 $ , $ 2.2 $ , $ 4.9 $ and $ 11 $ .

In contrast with the SVS method, the numbers on the branches vary with the number of family members. The more family members, the lower the number on each branch. The numbers on the branches are the $ {f}_a $ values for the idea at the bottom of the branch. The total fraction for the level is $ {f}_{t_l} $ . The total variety is $ {V}_B=\sum {\Omega}_l{f}_{t_l} $ :

$$ {V}_B=1\cdot 4.15+2.2\cdot 6.41+4.9\cdot 3.78+11\cdot 1.63=54.7 $$

Note that one cannot directly compare the variety scores from SVS and axiom-based aggregation, because they have a different normalization. Future work is planned on developing a normalized variety score that meets all of the axioms.

13.2. Novelty

It is difficult to directly compare the novelty scores for the SVS method and axiom-based aggregation. Both methods calculate an element novelty at a level of the tree, including a novelty score and a weighting parameter. The novelty scores are determined so differently in the two methods as to prevent direct comparison. The weighting functions are chosen by the rater in the SVS method and are determined by the area of design space explored by the idea in the axiomatic method.

SVS add the element novelties for the elements in a design solution to obtain a solution novelty. SVS give no means of measuring set novelty in the 2003 paper. Later work by Shah and Vargas-Hernandez alludes to using average solution novelty or best solution novelty. Axiom-based aggregation adds the element novelties for each element in the tree to give a total set novelty that accounts for each idea’s location in the tree (or, how much the design space has been expanded).

Where multiple design solutions share a tree element, the SVS average novelty will include the novelty of that element once for each solution. Axiom-based aggregation only adds the novelty of that element once.

13.3. Quality

SVS calculate an idea quality score at a given level of the tree as a weighted sum of the qualities of all individual functions needed at that level. It then multiplies the idea quality score by the weight of that level to obtain an element quality score. The element quality scores for a design alternative are then summed to give an alternative score. The set quality is the sum of the alternative qualities divided by the number of alternatives and the sum of function weights. Thus, the set quality reflects an average quality and can easily violate the quality axioms.

Axiom-based aggregation calculates an idea quality for each idea. There is no weighting for the levels, as quality does not appear to us to be related to the amount of design space explored by an idea. The element quality is determined from the idea quality by including the threshold quality for the tree, given by Equation 37. There is no increased weighting for quality scores at higher levels of the tree. The set quality score does increase more for a quality idea at a higher level of the tree because the branch quantity is larger at higher levels of the tree (see Equation 38). The set quality meets the axioms in all cases.