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# On importance indices in multicriteria decision making

Michel Grabisch, Christophe Labreuche, Mustapha Ridaoui

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Michel Grabisch, Christophe Labreuche, Mustapha Ridaoui. On importance indices in multicriteria decision making. 2018. halshs-01815012

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On importance indices in multicriteria decision making

Michel GRABISCH, Christophe LABREUCHE, Mustapha RIDAOUI

2018.08

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# On importance indices in multicriteria decision making*

Michel GRABISCH¹, Christophe LABREUCHE² and Mustapha RIDAOUI¹†

¹ Paris School of Economics, University of Paris I
106-112, Bd de l'Hôpital, 75013 Paris, France

michel.grabisch@univ-paris1.fr, mustapha.ridaoui@univ-paris1.fr

² Thales Research and Technology
1, Avenue Augustin Fresnel, 91767 Palaiseau, France

christophe.labreuche@thalesgroup.com

## Abstract

We address in this paper the problem of how to define an importance index in multicriteria decision problems, when a numerical representation of preference is given. We make no restrictive assumption on the model, which could have discrete or continuous attributes, and in particular, it is not assumed that the model is monotonically increasing or decreasing with respect to (w.r.t.) the attributes. Our analysis first considers discrete models, which are seen to be equivalent to multichoice games. We propose essentially two importance indices, namely the signed importance index and the absolute importance index, both based on the average variation of the value of the model induced by a given attribute. We provide several axiomatizations for these importance indices, extend them to the continuous case, and finally illustrate them with examples (classical simple models and a example of discomfort evaluation based on real data).

**Keywords:** Multiple criteria analysis, Multichoice game, Shapley value, Choquet integral.

## 1 Introduction

The notion of importance of attributes or criteria in multicriteria decision aid (MCDA) has always been central in the modelling and analysis of preference. What does it mean that a criterion is important and how to quantify it under the form of an *importance index*? This question can have a simple answer in simple models based on aggregation of scores on individual criteria, where the aggregation is done by the weighted arithmetic mean. Then it is tempting to say that the weights used in the aggregation may be considered as the importance indices of the criteria. This intuition reveals to be correct only under certain circumstances, namely that the scores to be aggregated are calibrated on the same scale and that no other device enters the decision process, like vetos, thresholds, etc.

Within the above framework, when more sophisticated aggregation models are used, like the Choquet integral (Choquet, 1953; Grabisch, 1996; Grabisch and Labreuche, 2010), the Shapley value, a concept borrowed from game theory, is often used. The reason is that the Choquet integral is a generalization of the weighted arithmetic mean, whose “weights” are replaced by a set function *v* called *capacity* or *fuzzy measure*. Capacities can be seen as particular coalitional (TU) games, which are functions *v* assigning to coalitions of players their benefit or worth. The

<footnote>*The paper is a follow-up of two papers of the authors presented at ECSQARU 2017 and ADT 2017 (Ridaoui et al., 2017; Ridaoui et al.)</footnote>

<footnote>†Corresponding author.</footnote>
Shapley value (Shapley, 1953) is a vector assigning to each player its “final” benefit or worth, which is a sharing of the benefit $v(N)$ achieved by the set $N$ of all players. Then, in the MCDA context, $N$ is taken as the set of criteria, and $v(S)$ for any $S \subseteq N$, is intuitively the “weight of importance” of the group of criteria $S$. Considering $v(N)$ as the total importance of all criteria, the Shapley value then expresses the individual importance of criteria.

For other sophisticated models or not necessarily of the aggregation type, like the GAI (Generalized Additive Independence) model of (Fishburn, 1967), we are not aware of any general definition of an importance index. This paper precisely aims at filling this gap. Our approach is quantitative, as our aim is to define a numerical importance index, and therefore our starting point will be a numerical representation of the preference, rather than the preference itself. The strong point of our approach is that no specific assumption is needed on the numerical representation. In particular, we can deal with continuous or discrete-valued attributes, although most of our effort will bear on the discrete case, the continuous case being obtained by interpolation. Also, we do not assume monotonicity, that is, the satisfaction of the decision maker does not necessarily increase nor decrease with the values of the attributes.

Basically, we propose two families of importance indices, plus a natural generalization of the second one. The first one considers that the importance index of attribute *i* is a (weighted) average of the variation of the model along the *i* axis. This idea comes from the field of aggregation functions, and it is known from (Marichal, 1998) that applied to the Choquet integral, it yields the Shapley value. We show, however, that this approach is meaningful only if the model is monotone w.r.t. the attributes, otherwise the average variation along an axis could be close to zero, simply because positive variations cancel negative ones. This is why we propose a second approach, taking the cumulation of absolute variations rather than the average of variations. This leads to the family of *absolute importance indices*, contrasting with the former one called *signed importance indices*. Realizing that taking the sum of absolute values amounts to consider the $L_1$ norm, a third family, called *norm-based importance indices*, is proposed where the amount of variation is computed by a norm.

Our work makes also a contribution to the theory of multichoice games (Hsiao and Raghavan, 1993), by providing alternative definitions of Shapley-like values for multichoice games. Indeed, in the discrete case, a numerical model of preference can be seen as a multichoice game (see, e.g., Grabisch and Labreuche (to appear); Labreuche and Grabisch (to appear)).

The paper is organized as follows. Section 2 introduces the framework, notation and the necessary background. Section 3 is a discussion on how to define importance indices, introducing our three families. The related literature is given in Section 4. The main part of the paper consists in Sections 5 and 6, giving the axiomatizations in the discrete case (hence equivalent to a multichoice game) of the signed and the absolute importance indices (another axiomatization of both indices is also presented in Section 10). Section 7 gives an axiomatization of the family of norm-based indices. The usage of the signed and absolute importance indices are illustrated on an example in Section 8, and finally Section 9 addresses the continuous case. It shows that if interpolation is done by the Choquet integral, then the previous definitions of signed and absolute importance indices are still valid for the continuous case.

## 2 The framework

Throughout the paper, the cardinality of sets will be denoted by corresponding lower case letters, e.g., $|N| = n$, $|S| = s$, etc. For simplicity, singletons $\{i\}$ are often denoted by $i$.

We consider a multicriteria decision aid (MCDA) problem with $n$ attributes $X_1, \dots, X_n$. We denote by $N = \{1, \dots, n\}$ the index set of attributes. The set of potential alternatives is
therefore the Cartesian product $X = X_1 \times \dots \times X_n$.

There is no restriction on the $X_i$'s, i.e., they may be continuous ($X_i = \mathbb{R}$ or is an interval of $\mathbb{R}$) or discrete sets, however, in the former case, we select in each attribute $X_i$ a finite set of values $a_1^i, \dots, a_{k_i}^i$, called levels, ($\forall i \in N, k_i \in \mathbb{N}$, such that $k_i \ge 1$). There must be for each attribute at least two levels (these would be the upper and lower bounds of $X_i$ when it is an interval of $\mathbb{R}$). For the most part of our development (definition and axiomatization of some importance indices), we consider only these levels, so that all is working as if the attributes were discrete. We address the continuous case in Section 9.

The precise value of the chosen levels (or values of the discrete attributes) $a_j^i$ playing no rôle in our development (except in Section 9), we refer to them by simply giving their subindex, i.e., alternative $(a_{j_1}^1, a_{j_2}^2, \dots, a_{j_n}^n)$ is represented by $(j_1, j_2, \dots, j_n)$. This amounts to considering alternatives as elements of the Cartesian product $L := L_1 \times \dots \times L_n$, with $L_i = \{0, 1, \dots, k_i\}$. Also, we introduce $\Gamma(L) = \{0, k_1\} \times \dots \times \{0, k_n\}$, the set of “vertices” of $L$. Although a discrete attribute $X_i$ is not necessarily ordered, our notation using $L_i$ automatically induces an implicit ordering on the values of the attribute: $a_j^i \le a_k^i$ iff $j \le k$. This also induces a partial order on $L$ by taking the product order: for $x, y \in L$, $x \le y$ if $x_i \le y_i$ for all $i = 1, \dots, n$.

As customary in decision theory, for any $x \in X$ (or $x \in L$) and $S \subseteq N$, $x_S$ denotes the restriction of $x$ to the coordinates in $S$, while $x_{-S}$ denotes the restriction to the coordinates in $N \setminus S$. Accordingly, we use $L_S, X_S, L_{-S}, X_{-S}$ for the restricted Cartesian products of attributes. Hence, with $x_{-i} \in L_{-i}$ and $\ell \in L_i$, $(x_{-i}, \ell_i)$ is an element of $L$ whose coordinate $i$ is $\ell$ and $x_{-i}$ is the vector of the other coordinates. As far as no confusion arises, $(x_{-i}, (k_i)_i)$ is simply denoted by $(x_{-i}, k_i)$. The vector $0_N = (0, \dots, 0)$ is the null alternative of $L$, and $k_N = (k_1, \dots, k_n)$ is the top element of $L$. For each $x \in L$, we denote by $\Sigma(x) = \{i \in N | x_i > 0\}$ the support of $x$, and by $K(x) = \{i \in N | x_i = k_i\}$ the kernel of $x$. Their cardinalities are respectively denoted by $\sigma(x)$ and $\kappa(x)$.

We suppose that the preference of the decision maker over alternatives in $L$ can be represented numerically by a value function $v: L \to \mathbb{R}$ (Note: It is out of the scope of this paper to say how $v$ can be obtained; depending on its chosen form, many classical methods in MCDA and MAUT can be applied for this). We suppose that $v(0_N) = 0$, which is no restriction since most of the time numerical representation of preferences are given up to a positive affine transformation.

Apart this, we make no special assumption on $v$: the value function could be additive, or be of more sophisticated form (e.g., Generalized Additive Independence (GAI) model, etc.), anyway, we use in the sequel only its numerical values $v(x)$ for each $x \in L$.

In particular, the classical property of (increasing) monotonicity, which says that

$$x \le y \Rightarrow v(x) \le v(y), \qquad (1)$$

or its converse (decreasing monotonicity) or any mixed version, are not assumed here. Monotonicity means that the higher (or the lower) the value of the attributes, the better the alternative. If this holds in many practical examples (cost, production time, benefit, etc.), it is not true in general that maximizing or minimizing attribute values maximizes the satisfaction of the decision maker: it may be the case that the ideal value of the attribute is not one of the bounds. The following example shows this.

**Example 1 (Employee working conditions).** We consider a function $v$ measuring the overall degree of well-being at work. A high degree indicates that the employee is comfortably seated with ideal conditions allowing a good productivity. To keep it simple, we consider three attributes: the temperature $X_1$ of the room, the light intensity $X_2$ (for artificial lighting), and the level of comfort $X_3$ of the chair. One can readily see that the optimal temperature is
not reached for extreme values. The ideal temperature is more likely to be optimal around 22 degree Celsius. Likewise, the ideal light intensity should not be too strong for the employee's eyes. Finally the level of comfort of the chair that is best for the standpoint of his productivity is neither too low (which can generate pain in the back of the employee) nor too high (the employee might fall asleep). To sum-up, for each attribute, there exists an optimal value $\hat{l}_i \in L_i$ such that $v$ is increasing in $x_i$ below $\hat{l}_i$, and then decreasing in $x_i$ above $\hat{l}_i$.

Observe that in Example 1, the preference is single-peaked: on each attribute, the function $v$ is increasing till an optimal value is reached, and then decreasing. A drastic example of single-peaked value function is the $\delta_x$ Dirac function, defined by, for any $x \in L \setminus \{0_N\}$,

$$ \delta_x(y) = \begin{cases} 1, & \text{if } y = x \\ 0, & \text{otherwise} \end{cases} \qquad (2) $$

It is worth noting that this general framework permits to recover some classical concepts. When $k_1 = \dots = k_n = 1$, $v$ is a pseudo-Boolean function (Crama and Hammer, 2011; Hammer and Rudeanu, 1986) $v: \{0, 1\}^N \to \mathbb{R}$ vanishing at $0_N$. Through the correspondence between a set $S$ and its characteristic function $1_S$, it can be put in the form of a function $\mu: 2^N \to \mathbb{R}$, which is a TU-game in cooperative game theory (see, e.g., Peleg and Sudhölter (2003); Peters (2008)). When monotonicity (1) is enforced, we obtain capacities introduced by Choquet (1953). When $k_i \ge 1$ for each $i \in N$, multichoice games, introduced by Hsiao and Raghavan (1993), are recovered. Monotone multichoice games, i.e., satisfying (1), were introduced by Grabisch and Labreuche (2003b) under the name of $k_N$-ary capacities.

It turns out that in any case, our numerical representation $v$ on $L$ can be seen as a multichoice game. We will therefore work on the set of multichoice games on $L$, which we denote by $\mathcal{G}(L)$. In addition, $\mathcal{G}_M(L)$ is the set of monotone multichoice games.

We finish this section by introducing some technical notions on multichoice games. The derivative of $v$ w.r.t. an attribute $i$ at point $x \in L$ such that $x_i < k_i$ is defined by

$$ \Delta_i v(x) = v(x + 1_i) - v(x). \qquad (3) $$

The set $\mathcal{G}(L)$ of multichoice games is a vector space of dimension $k_1k_2\cdots k_n - 1$. A trivial basis of $\mathcal{G}(L)$ is the set $\{\delta_x\}_{x \in L \setminus \{0_N\}}$ introduced above. Another one is the basis of unanimity games $u_x$, which are defined for any $x \in L \setminus \{0_N\}$ by

$$ u_x(y) = \begin{cases} 1, & \text{if } y \ge x \\ 0, & \text{otherwise.} \end{cases} \qquad (4) $$

Then, for any game $v \in \mathcal{G}$,

$$ v = \sum_{x \in L \setminus \{0_N\}} m^v(x) u_x. \qquad (5) $$

The coordinates $m^v(x)$, $x \in L \setminus \{0_N\}$, of a game $v$ are known to be the Möbius transform of $v$ (Rota, 1964), which can be seen as a function $m^v: L \to \mathbb{R}$ with $m^v(0_N) := 0$. It is useful to consider $\mathcal{G}_+(L)$ the set of games whose Möbius transform is nonnegative, and to note that $\mathcal{G}_+(L) \subseteq \mathcal{G}_M(L)$ is a cone. It is easy to see, as for classical games, that any multichoice game $v \in \mathcal{G}(L)$ can be uniquely decomposed into

$$ v = v^+ - v^- \qquad (6) $$

with $v^+, v^- \in \mathcal{G}_+(L)$ ($v^+, v^-$ simply correspond respectively to the nonnegative coefficients and negative coefficients of the Möbius transform of $v$).
### 3 How to define importance indices

Our aim is to define an importance index in this general framework, where no special assumption is made on the model, numerically represented by $v$, apart that for the moment, $v$ is defined on discrete attributes.

The value function $v$ expresses the intensity of satisfaction of the decision maker for the different alternatives in $L$, which are multidimensional vectors $x = (x_1, ..., x_n)$. In this sense, $v$ can be seen as an aggregation function, yielding a single real number from $n$ inputs. The importance index of an attribute should reflect to which extent the attribute influences the output value. As this influence may depend on the value of the other attributes, a kind of average influence should be taken. As the output value is a real number, influence is visible only through a change or variation of this value. Hence, the importance of an attribute is related to the amount of variation this attribute can produce on $v$. This view is in fact the one taken in the domain of aggregation functions $F: I^n \to I$, where $I$ is some real interval (see, e.g., Grabisch et al. (2009)[§10.3]). The importance index of attribute (dimension) $i$ is defined by, assuming $I = [a, b]$,

$$ \phi_i(F) = \frac{1}{(b-a)^n} \int_{[a,b]^n} \frac{\Delta_i F(x)}{b-a} dx = \frac{1}{(b-a)^n} \int_{[a,b]^n} \frac{\partial F(x)}{\partial x_i} dx, \quad (7) $$

where $\Delta_i F(x) = F(b_i, x_{-i}) - F(a_i, x_{-i})$, $x \in I^n$. As aggregation functions are always nondecreasing in each argument, $\Delta_i F(x)$ is the total variation on dimension $i$ at $x$, while the second equality makes apparent that the importance index of $i$ is an average of the local variation due to $i$.

We apply the same idea to the value function $v$, although $v$ is not supposed to be monotone and is defined on a discrete domain. A general expression for the importance index of attribute $i$ w.r.t. $v$ would be, using the (discrete) derivative (3):

$$ \phi_i(v) = \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i \Delta_i v(x) \quad (8) $$

where the $p_x^i$ are real coefficients (weights). If the weights $p_x^i$ depend only on $p_{x_{-i}}^i$, then

$$ \sum_{x_i=0}^{k_i-1} p_{x_{-i}}^i \Delta_i v(x) = p_{x_{-i}}^i (v(x_{-i}, k_i) - v(x_{-i}, 0_i)) $$

which reminds the first equality in (7). We will specify further and axiomatize the family of importance indices defined in (8) in Section 5.

Observe that in case of an aggregation function or a monotone increasing value function $v$, the importance index defined in (7) or (8) is a positive quantity, while it becomes negative when the considered function is monotone decreasing. For this reason, we may call such type of importance index a *signed importance index*.

Signed importance indices may give counterintuitive results, though. Suppose that the value function $v$ is such that, roughly speaking, there is as much positive variation as negative variation along the $i$ axis. The total variation would be then zero, which seems to indicate that attribute $i$ has no importance, although it does have since it induces some variation of the function $v$. This is exactly the situation given in Example 1: for every attribute, when ranging from lowest value to highest value of the attribute, the variation is first positive and then negative, and we conclude that all attributes have a negligible importance. The extreme
example is given by the $\delta_x$ function (2), where all attributes have zero importance for all $x$ such that $0 < x_i < k_i$ for all $i \in N$ (provided that the weights have some suitable symmetry, e.g., $p_x^i$ does not depend on $x_i$).

A simple way to avoid this drawback is to *cumulate* the local variations, instead of summing them algebraically; put differently, the sum of the absolute value of the local variations should be taken:

$$ \phi_i(v) = \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i |\Delta_i v(x)|. \qquad (9) $$

We call this family the *absolute importance indices*. One may go one step further by remarking that the absolute value is the $L_1$ norm, and that any norm can be taken instead:

$$ \phi_i(v) = \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i \|\Delta_i v(x)\|, $$

or even in a more compact form, considering norms with weights on coordinates:

$$ \phi_i(v) = \|\Delta_i(v)\| \quad (i \in N). \qquad (10) $$

We may call this the *norm-based importance indices*. In Section 6, we will axiomatize and further specify the family of absolute importance indices, while Section 7 will address the case of norm-based importance indices.

# 4 Related works

Many models in MCDA are additive and based on the weighted arithmetic mean (AHP, ELEC-TRE, PROMETHEE, etc.), where the weights of the arithmetic are directly taken as weight of importance of the attributes. As for more sophisticated nonadditive models, the problem has been well studied for models based on the Choquet integral (Grabisch and Labreuche, 2010, 2016), however, as far as we know, there is no specific study on GAI models.

We elaborate on Choquet integral-based models. The most basic model in this category has the form:

$$ U(x) = C_{\mu}(u_1(x_1), \dots, u_n(x_n)) $$

where $C_\mu$ indicates the Choquet integral w.r.t. a capacity $\mu$ on $2^N$, and $u_1, \dots, u_n$ are marginal utility functions on the different attributes which are commensurable. Recall that a capacity is a monotone game, thus corresponding to the case $k_1 = \dots = k_n = 1$ of multichoice games. It has been suggested first by Murofushi (1992) and later developed by Grabisch (1996) that the *Shapley value* (Shapley, 1953), a concept borrowed from cooperative game theory, should be used as importance index for such models.

Generally speaking, a *value* is a mapping $\phi: \mathcal{G}(2^N) \to \mathbb{R}^N$ assigning a payoff vector to any game $v$. The expression of the Shapley value is given by, for any $v \in \mathcal{G}(2^N)$,

$$ \phi_i^{\text{Sh}}(v) = \sum_{S \subseteq N \setminus i} \frac{(n-s-1)!s!}{n!} (v(S \cup \{i\}) - v(S)), \forall i \in N. \qquad (11) $$

Through the identity between a set $S$ and its characteristic function $1_S$, the comparison of (11) with (8) shows that the Shapley value is a particular signed importance index, with $k_i = 1$ ($\forall i \in N$) and the weights given by $p_S^i = \frac{(n-s-1)!s!}{n!}$. Remembering that a capacity is monotone, it turns out that the Shapley value is also an absolute importance index.
More interestingly, it is shown in Grabisch et al. (2000) that taking F in (7) as the Choquet integral w.r.t. $\mu$, the importance index $\phi_i(C_\mu)$ coincides with $\phi_i^{\text{Sh}}(\mu)$, the Shapley value of $\mu$, showing the well-foundedness of the Shapley value as importance index for Choquet integral-based models.

We note that a corresponding result holds for the MLE (multilinear extension of Owen (1972)) model and the Banzhaf value (Banzhaf, 1965) (see, again, Grabisch et al. (2000)).

The previous paragraph shows the relation between importance indices in MCDA and values (Shapley, Banzhaf) in game theory. As our function $v$ when defined on a discrete domain can be interpreted as a multichoice game, the question arises whether Shapley-like values defined for multichoice games can be used as importance indices. Among the many definitions proposed in the literature, let us mention Hsiao and Raghavan (1993) (historically the first one), van den Nouweland et al. (1995), Klijn et al. (1999), Peters and Zank (2005), Grabisch and Lange (2007), etc. The value of Peters and Zank reads:

$$ \phi_i^{\text{PZ}}(v) = \sum_{x_{-i} \in \Gamma(L_{-i})} \frac{(n - \kappa(x_{-i}) - 1)! \kappa(x_{-i})!}{n!} (v(x_{-i}, k) - v(x_{-i}, 0)), \quad (12) $$

with $k_i = k, \forall i \in N$.

Clearly, (12) has the form of a signed importance index, although one can remark that only vertices of $L_{-i}$ are taken in the sum.

All these values, for classical games or multichoice games, are characterized by axioms, among which we always find linearity ($\phi$ is a linear operator on $G(L)$), a null axiom (a player with zero marginal contribution receives zero), a symmetry axiom (the numbering of the players has no influence on the payoff) and efficiency (the sum of the payoffs for all players should be equal to $v(k_N)$, where $k_N$ is the top element of $L$). While the first three axioms are easily transposable in the MCDA framework, the efficiency axiom is rooted in game theory and there is no convincing interpretation in MCDA. Indeed, efficiency means that the total benefit of the cooperation, represented by $v(k_N)$, should be shared among the players with no waste. In MCDA, the concept of sharing is not natural, and there is no reason why the sum of importance indices should be equal to $v(k_N)$. When $v$ is monotone, then $v(k_N)$ is the evaluation of the best possible alternative, and in this case it may be meaningful to consider the importance index of a criterion as a percentage of the best possible evaluation. However, this interpretation fails when $v$ is not monotone. Especially, in Example 1, the value of $v(k_N)$ should be close to 0, as the highest values of the attributes are not considered as comfortable.

This explains why multichoice game values of the literature cannot be used as importance indices, and this motivates our study. It also explains why it is not enough to specify a general form for the family (signed importance index, absolute importance index) as in (8), (9), but one has to specify the weights, in order that the resulting index has suitable properties. This is why we think that the only way is to proceed axiomatically. The next two sections axiomatize some signed and absolute importance indices, which are meaningful in terms of MCDA.

# 5 Axiomatization of signed importance indices

Let us denote by $\phi: G(N) \to \mathbb{R}^N$ an importance index, where $G(N)$ denotes the set of all multichoice games on $N$ (that is, with arbitrary number of levels for each $i \in N$, i.e., with any $L$). The aim of this section is to give an axiomatization of the family defined by (8), and by imposing additional axioms, to further specify possible sets of weights. The first two axioms we will introduce are close to the classical axioms used by Shapley in his seminal paper (Shapley, 1953).
**Linearity axiom (L):** $\phi$ is linear on $\mathcal{G}(L)$, i.e., $\forall v, w \in \mathcal{G}(L), \forall \alpha \in \mathbb{R}$,

$$ \phi_i(v + \alpha w) = \phi(v) + \alpha\phi(w). $$

The linearity axiom means that if we have the preferences $v$ and $w$ of two DMs, and the resulting preference is a linear combination of them (yielding $v = \alpha v + \beta w$), then it is equivalent to apply $\phi$ before or after the linear transformation.

The second axiom used by Shapley is called the null player axiom. It says that a player $i \in N$ who brings no contribution (i.e., $\mu(S \cup i) = \mu(S), \forall S \subseteq N \setminus \{i\}$: no variation, in our language) should receive a zero payoff (in our language: has no importance). This definition can be easily extended to multichoice games and to a MCDA setting.

**Definition 1.** An attribute $i \in N$ is said to be *null* for $v \in \mathcal{G}(L)$ if

$$ v(x + 1_i) = v(x), \forall x \in L, x_i < k_i. $$

*Remark 1.* Let $i \in N$ be a null attribute for $v \in \mathcal{G}(L)$. Then

$$ \forall x \in L, v(x_{-i}, x_i) = v(x_{-i}, 0_i). \qquad (13) $$

If an attribute is null w.r.t. a function $v \in \mathcal{G}(L)$, then this attribute has no influence on $v$, and hence the importance of this attribute shall be zero. We propose the following axiom.

**Null axiom (N):** If an attribute $i$ is null for $v \in \mathcal{G}(L)$, then $\phi_i(v) = 0$.

These two axioms (L) and (N) suffice to find the general form of an importance index.

**Proposition 1.** Under axioms (L) and (N), for all $i \in N$, there exists $p_x^i \in \mathbb{R}$, for all $x \in L$ with $x_i < k_i$, such that $\forall v \in \mathcal{G}(L)$,

$$ \phi_i(v) = \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i (v(x + 1_i) - v(x)). \qquad (14) $$

**Proof.** It is easy to check that the above formula satisfy the two axioms. Conversely, we consider $\phi$ satisfying (L) and (N). We have $v = \sum_{x \in L} v(x)\delta_x$. Then by (L),

$$ \phi_i(v) = \sum_{x \in L} v(x)\phi_i(\delta_x), \forall i \in N. $$

Setting $a_x^i = \phi_i(\delta_x), \forall x \in L, \forall i \in N$, we obtain, assuming $i$ is null for $v$:

$$ \begin{aligned} 0 = \phi_i(v) &= \sum_{x \in L} a_x^i v(x) \qquad (15) \\ &= \sum_{x_{-i} \in L_{-i}} \sum_{x_i \in L_i} a_{(x_{-i}, x_i)}^i v(x_{-i}, x_i) \\ &= \sum_{x_{-i} \in L_{-i}} \left( \sum_{x_i \in L_i} a_{(x_{-i}, x_i)}^i \right) v(x_{-i}, 0_i) \text{ by (13).} \end{aligned} $$

Hence, $\forall x_{-i} \in L_{-i}, \sum_{x_i \in L_i} a_{(x_{-i}, x_i)} = 0$. Letting $p_{(x_{-i}, j_i)} = \sum_{\ell=j+1}^k a_{(x_{-i}, \ell_i)}$, $\forall x_{-i} \in L_{-i}, \forall j \in L_i \setminus k_i$, we have, for all $x_{-i} \in L_{-i}$,

$$ \begin{aligned} a_{(x_{-i}, 0_i)} &= -p_{(x_{-i}, 0_i)}, \\ a_{(x_{-i}, k_i)} &= p_{(x_{-i}, (k_i-1)_i)}, \\ a_{(x_{-i}, \ell_i)} &= p_{(x_{-i}, (\ell-1)_i)} - p_{(x_{-i}, \ell_i)}, \forall \ell_i \in L_i \setminus \{0, k_i\}. \end{aligned} $$

Therefore, it suffices to replace the values of $a_{(x_{-i}, x_i)}^i$ in Formula (15), and the result is established. $\square$
The next axiom is an invariance property. It says that the calculus of the importance index does not depend on the position on the “grid” L.

**Invariance axiom (I):** Let us consider two functions $v, w \in \mathcal{G}(L)$ such that, for some $i \in N$,

$$v(x + 1_i) - v(x) = w(x) - w(x - 1_i), \forall x \in L, x_i \notin \{0, k_i\}$$

$$v(x_{-i}, 1_i) - v(x_{-i}, 0_i) = w(x_{-i}, k_i) - w(x_{-i}, (k_i - 1)_i), \forall x_{-i} \in L_{-i}.$$

Then,

$$\phi_i(v) = \phi_i(w).$$

Axiom (I) says that two multichoice games $v, w$ should have the same importance index for attribute $i$ if $v$ and $w$ have the same derivative w.r.t. $i$, up to a shift of one unit. The axiom enables an easier computation of coefficients $p_x^i$ while reducing their number.

**Proposition 2.** Under axioms (L), (N) and (I), $\forall v \in \mathcal{G}(L), \forall i \in N$,

$$\phi_i(v) = \sum_{x_{-i} \in L_{-i}} p_{x_{-i}}^i (v(x_{-i}, k_i) - v(x_{-i}, 0_i)).$$

**Proof of Proposition 2.** It is easy to check that the given formula satisfies the axioms. Conversely, consider $\phi$ satisfying (L), (N) and (I). Let $v, w \in \mathcal{G}(L)$, and $i \in N$. By Proposition 1 and (I), we have:

$$\begin{aligned}
\phi_i(v) &= \sum_{\substack{x \in L \\ x_i < k_i}} p_{x_i; x_{-i}}^i (v(x + 1_i) - v(x)) \\
&= \sum_{x_{-i} \in L_{-i}} \left( \sum_{\substack{x_i \in L_i \\ x_i \notin \{0, k_i\}}} p_{x_i; x_{-i}}^i (v(x + 1_i) - v(x)) + p_{0_i; x_{-i}}^i (v(x_{-i}, 1_i) - v(x_{-i}, 0_i)) \right) \\
&= \sum_{x_{-i} \in L_{-i}} \left( \sum_{\substack{x_i \in L_i \\ x_i \notin \{0, k_i\}}} p_{x_i; x_{-i}}^i (w(x) - w(x - 1_i)) + p_{0_i; x_{-i}}^i (w(x_{-i}, k_i) - w(x_{-i}, (k_i - 1)_i)) \right) \\
&= \sum_{x_{-i} \in L_{-i}} \left( \sum_{\substack{x_i \in L_i \\ x_i < k_i - 1}} p_{x_i + 1; x_{-i}}^i (w(x + 1_i) - w(x)) + p_{0_i; x_{-i}}^i (w(x_{-i}, k_i) - w(x_{-i}, (k_i - 1)_i)) \right),
\end{aligned}$$

and,

$$\phi_i(w) = \sum_{x_{-i} \in L_{-i}} \left( \sum_{\substack{x_i \in L_i \\ x_i < k_i - 1}} p_{x_i; x_{-i}}^i (w(x + 1_i) - w(x)) + p_{(k_i - 1)_i; x_{-i}}^i (w(x_{-i}, k_i) - w(x_{-i}, (k_i - 1)_i)) \right),$$

then, $p_{x_i; x_{-i}}^i = p_{x_i + 1; x_{-i}}^i, \forall x_{-i} \in L_{-i}, \forall x_i \in L_i \setminus k_i$.

We conclude that the coefficient $p_{x_i; x_{-i}}^i$ does not depend on $x_i$. We denote by $p_{x_{-i}}^i$ this value. Therefore, it suffices to replace the values of $p_{x_i; x_{-i}}^i$ in Formula (14) by $p_{x_{-i}}^i$, and the result is established. $\square$

Axiom (I) implies that we only need to look at the variation of $v$ between the extreme values 0 and $k_i$. The evaluation on the intermediate elements of $L_i$ does not intervene.

We introduce now the symmetry axiom.
**Symmetry for Dirac games (SD):** Let $x \in L \setminus \{0_N\}$.

* $\forall i, j \in K(x), \phi_i(\delta_x) = \phi_j(\delta_x),$

* $\forall i, j \in N \setminus \Sigma(x), \phi_i(\delta_x) = \phi_j(\delta_x).$

The symmetry axiom means that for the Dirac game, there is an equal importance for attributes in the kernel, or outside the support of the game.

In game theory, it is common to use an efficiency axiom, meaning that the total sum $\sum_{i \in N} \phi_i(v)$ (recall that in this context $\phi_i(v)$ is interpreted as a payoff given to $i$) should be equal to the total worth or benefit, $v(k_N)$. As explained in Section 4, no such interpretation holds in our context, but some substitute to efficiency (normalization or calibration axiom) has to be found in order to determine the weights $p_{x-i}^i$ uniquely.

Recall that $\phi_i(v)$ computes the average variation of $v$ when $x_i$ is increased by one unit, i.e., when going from any point $x$ to $(x_i + 1, x_{-i})$. We interpret $\sum_{i \in N} \phi_i(v)$ as the variation of $v$ when all attributes are simultaneously increased by one unit, i.e., going from $x$ to $x + 1_N = (x_1 + 1, \dots, x_n + 1)$. Thus, we introduce the efficiency axiom as follows.

**Efficiency axiom (E):**

$$ \sum_{i \in N} \phi_i(v) = \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} (v(x + 1_N) - v(x)) \quad (16) $$

*Remark 2.* Interestingly, although the above equation is clearly in the spirit of variation calculus suitable to our approach, making $k_i = 1_i, \forall i \in N$ leads to $\sum_{i \in N} \phi_i(v) = v(N) - v(\emptyset) = v(N)$, i.e., the classical efficiency axiom. It shows that the usual definition of efficiency for multichoice games is not the only way to generalize the classical definition.

Applied to an arbitrary function $v$, the right handside of Equation (16) does not give any peculiar result. Hence, let us take the Dirac function $\delta_x$, central in our discussion. We have several cases:

* $\kappa(x) \neq 0$ and $\sigma(x) = n$. Then $x - 1_N \in L$ because no component of $x$ is equal to 0, and we have $\delta_x(x) - \delta_x(x - 1_N) = 1$. Note that $\delta_x(y + 1_N) - \delta_x(y) = 0$ for any $y \neq x - 1$ and $y, y + 1 \in L$. Therefore,

$$ \sum_{i \in N} \phi_i(\delta_x) = 1. \quad (17) $$

* $\kappa(x) = 0$ and $\sigma(x) < n$. Then $x + 1_N \in L$ because no component of $x$ is equal to $k_i$, and we have $\delta_x(x + 1_N) - \delta_x(x) = -1$. Note that $\delta_x(y + 1_N) - \delta_x(y) = 0$ for any other possible $y$, therefore,

$$ \sum_{i \in N} \phi_i(\delta_x) = -1. \quad (18) $$

* $\kappa(x) \neq 0$ and $\sigma(x) < n$. There are both components equal to 0 and to $k_i$ in $x$. Therefore, neither $x + 1_N$ nor $x - 1_N$ belong to $L$, and for any possible $y \in L$ s.t. $y + 1 \in L$, we have $\delta_x(y + 1_N) - \delta_x(y) = 0$. Therefore,

$$ \sum_{i \in N} \phi_i(\delta_x) = 0. \quad (19) $$
• $\kappa(x) = 0$ and $\sigma(x) = n$. We have $\delta_x(x + 1_N) - \delta_x(x) = -1$ and $\delta_x(x) - \delta_x(x - 1_N) = 1$, otherwise the difference is 0 for all other $y$. Therefore,

$$ \sum_{i \in N} \phi_i(\delta_x) = 0. \qquad (20) $$

**Theorem 1.** Under axioms (L), (N), (I), (SD) and (E), for all $v \in \mathcal{G}(L)$

$$ \phi_i(v) = \phi_i^s(v) := \sum_{x_{-i} \in L_{-i}} \frac{(n - \sigma(x_{-i}) - 1)!\kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} (v(x_{-i}, k_i) - v(x_{-i}, 0_i)), \forall i \in N \quad (21) $$

To prove this result, the following Lemma is useful.

**Lemma 1.** Let $v \in \mathcal{G}(L)$.

$$ \phi_i^s(v) = \sum_{x \in \{0, \dots, k-1\}^N} \phi_i^{Sh}(\mu_x^v), \forall i \in N, $$

with, $\mu_x^v(S) = v(x + 1_S) - v(x), \forall S \subseteq N, \forall x \in L$, such that $x_i < k_i, \forall i \in N$.

*Proof.* Let $v \in \mathcal{G}(L)$ and for any $x \in L$, such that $x_i < k_i, \forall i \in N$, we define the game $\mu_x^v$ such that for every $S \subseteq N, \mu_x^v(S) = v(x + 1_S) - v(x)$. Using the identity

$$ \sum_{S \subseteq [A, B]} \frac{(n - s - 1)!s!}{n!} = \frac{(n - b - 1)!a!}{(n - b + a)!}, \forall A, B \subseteq N, A \subseteq B, $$

we have

$$
\begin{align*}
\phi_i^s(v) &= \sum_{\substack{x \in L \\ x_i < k_i}} \frac{(n - \sigma(x_{-i}) - 1)!\kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} (v(x + 1_i) - v(x)) \\
&= \sum_{\substack{x \in L \\ x_i < k_i}} (v(x + 1_i) - v(x)) \sum_{\substack{A \subseteq \Sigma(x_{-i}) \\ A \supseteq K(x_{-i})}} \frac{(n - a - 1)!a!}{n!} \\
&= \sum_{\substack{x \in L \\ x_i < k_i}} (v(x + 1_i) - v(x)) \sum_{\substack{A \subseteq N \setminus i \cap \Sigma(x_{-i}) \\ A \supseteq K(x_{-i})}} \frac{(n - a - 1)!a!}{n!} \\
&= \sum_{\substack{x \in L \\ x_i < k_i}} (v(x + 1_i) - v(x)) \sum_{\substack{A \subseteq N \setminus i \\ \forall j \in A, x_j > 0 \\ \forall j \in N \setminus A, x_j < k_j}} \frac{(n - a - 1)!a!}{n!} \\
&= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \sum_{S \subseteq N \setminus i} \frac{(n - s - 1)!s!}{n!} (v(x + 1_{S \cup i}) - v(x + 1_S)) \\
&= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \sum_{S \subseteq N \setminus i} \frac{(n - s - 1)!s!}{n!} (\mu_x^v(S \cup i) - \mu_x^v(S)) \\
&= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \phi_i^{Sh}(\mu_x^v).
\end{align*}
$$

□
We now prove Theorem 1.

**Proof.** It is easy to check that the formula (21) satisfies the axioms (L), (N), (I) and (SD). Let us show that it satisfies the axiom (E). By Lemma 1, we have,

$$
\begin{align*}
\sum_{i \in N} \phi_i^s(v) &= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \sum_{i \in N} \phi_i^{Sh}(\mu_x^v) \\
&= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \mu_x^v(N) \\
&= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} (v(x + 1_N) - v(x)).
\end{align*}
$$

Conversely, we consider $\phi$ satisfying the axioms (L), (N), (I), (SD) and (E). Let $x \in L$, we write $x = (0_{N\setminus S \cup T}, x_S, k_T)$, with $x_S \in L_S \setminus \Gamma(L_S)$, $S = \Sigma(x) \setminus K(x)$, and $T = K(x)$.
By Proposition 2, we have,

$$
\phi_i(\delta_x) = p_{x_{-i}}^i (\delta_x(x_{-i}, k_i) - \delta_x(x_{-i}, 0_i)),
$$

from which we obtain

$$
\phi_i(\delta_{(x_{-i}, k_i)}) = p_{x_{-i}}^i = -\phi_i(\delta_{(x_{-i}, 0_i)}), \quad (22)
$$

and

$$
\phi_i(\delta_{(x_{-i}, x_i)}) = 0, \text{ for } x_i \in L_i \setminus \{0, k_i\}. \quad (23)
$$

We only have to determine $\phi_i(\delta_{(x_{-i}, k_i)})$, with $x_{-i} = (0_{N\setminus S \cup T}, x_S, k_{T\setminus i})$.

We have

$$
\sum_{i \in N} \phi_i(\delta_x) = \sum_{i \in T} \phi_i(\delta_x) + \sum_{i \in S} \phi_i(\delta_x) + \sum_{i \in N \setminus S \cup T} \phi_i(\delta_x)
$$

and by (23), we obtain,

$$
\sum_{i \in N} \phi_i(\delta_x) = \sum_{i \in T} \phi_i(\delta_{x_{-i}, k_i}) + \sum_{i \in N \setminus S \cup T} \phi_i(\delta_{x_{-i}, 0_i}). \quad (24)
$$

We distinguish the two following cases:

(i) If $S \cup T = N$,

$$
\sum_{j \in N} \phi_j(\delta_x) = (n-s)\phi_i(\delta_{x_{-i}, k_i}), \forall i \in T \quad (25)
$$

(ii) If $S \cup T \neq N$,

$$
\sum_{\ell \in N} \phi_{\ell}(\delta_x) = t \phi_i(\delta_{x_{-i}, k_i}) + (n-s-t) \phi_j(\delta_{x_{-j}, 0_j}), \forall i \in T, \forall j \in N \setminus S \cup T. \quad (26)
$$

Hence, $\phi_i(\delta_{(x_{-i}, k_i)})$ satisfies

• by (25) and (17),

$$
\forall i \in K(x), (\text{with } \sigma(x) = n), \phi_i(\delta_{(x_{-i}, k_i)}) = \frac{1}{n-s}, \forall s \in \{0, \dots, n-1\},
$$
• by (26) and (19), $\forall t \in \{1, \dots, n-1\}$, $\forall s \in \{0, \dots, n-1\}$ with $s+t \le n-1$,

$$ \phi_i(\delta_{(x_{-ij}, 0_j, k_i)}) = -\frac{n-s-t}{t} \phi_j(\delta_{(x_{-ij}, 0_j, k_i)}), $$

and by (22), we obtain

$$ \phi_i(\delta_{(x_{-ij}, 0_j, k_i)}) = \frac{n-s-t}{t} \phi_j(\delta_{(x_{-ij}, k_j, k_i)}). $$

Thus, recursively,

$$ \begin{align*} \phi_i(\delta_{(0_{N\setminus S \cup T}, x_S, k_{T \setminus i}, k_i)}) &= \frac{n-s-t}{t} \phi_j(\delta_{(0_{N\setminus S \cup T_1}, x_S, k_{T_1})}), & T_1 &= T \cup \{j\} \\ \phi_i(\delta_{(0_{N\setminus S \cup T_1}, x_S, k_{T_1 \setminus i}, k_i)}) &= \frac{n-s-t-1}{t+1} \phi_j(\delta_{(0_{N\setminus S \cup T_2}, x_S, k_{T_2})}), & T_2 &= T_1 \cup \{j\} \\ \vdots & & & \\ \phi_i(\delta_{(0_j, x_S, k_{T_{n-s-1} \setminus i}, k_i)}) &= \frac{1}{n-s-1} \phi_j(\delta_{(x_S, k_{T_{n-s}})}), & T_{n-s} &= T_{n-s-1} \cup \{j\}. \end{align*} $$

Therefore,

$$ \begin{align*} \phi_i(\delta_{(x_{-i}, k_i)}) &= \frac{(n-s-t)!(t-1)!}{(n-s)!} \\ &= \frac{(n-\sigma(x))!(\kappa(x)-1)!}{(n+\kappa(x)-\sigma(x))!} \\ &= \frac{(n-\sigma(x_{-i}, k_i))!(\kappa(x_{-i}, k_i)-1)!}{(n+\kappa(x_{-i}, k_i)-\sigma(x_{-i}, k_i))!} \\ &= \frac{(n-\sigma(x_{-i})-1)!(\kappa(x_{-i})!}{(n+\kappa(x_{-i})-\sigma(x_{-i}))!} \end{align*} $$

The result is proved. □

The importance index given in Theorem 1 measures the impact of attribute *i* on *v* as the weighted average of the difference between the highest and lowest value of attribute *i*, when $x_{-i}$ is varying over the domain $L_{-i}$. It can be also seen as the average variation along the axis of the attribute.

# 6 Axiomatization of absolute importance indices

We turn now to the axiomatization of the family defined by (9), and of particular instances of it. The major difficulty in axiomatizing (9) is that $\phi$ does not satisfy linearity, and therefore it is not possible to start from the decomposition of games in some basis. Instead, the idea is the following: the expression of $v$ in the basis of unanimity games (5) becomes a conic combination when $v$ is in $G_+(L)$. As any game can be written as the difference of two games in $G_+(L)$ (see (6)), it is then possible to extend this expression to monotone games, for which the absolute value in $|v(x+1_i) - v(x)|$ is no more necessary. Hence, we need that $\phi$ commutes with conic combination and differences of games in $G_+(L)$.

**Conic Combination axiom (CC)**: For every $v, w \in G_+(L)$, for every $\alpha \in \mathbb{R}_+$,

$$ \phi(v + \alpha w) = \phi(v) + \alpha \phi(w). $$
**Decomposition axiom (D):** If $v, v' \in \mathcal{G}_+(L)$ and $v - v'$ is monotone, then $\phi(v - v') = \phi(v) - \phi(v')$.

These two axioms permit to obtain the following result.

**Proposition 3.** Under axioms (CC) and (D), for all $i \in N$, there exists constants $a_i^+ \in \mathbb{R}$, for all $x \in L$, such that for all $v \in \mathcal{G}_M(L)$,

$$ \phi_i(v) = \sum_{x \in L} a_x^i v(x). \qquad (27) $$

**Proof.** By using the basis of unanimity games, and by the definition of $\mathcal{G}_+(L)$,

$$ v = \sum_{x \in L} m^v(x) u_x $$

for any $v \in \mathcal{G}_+(L)$, and $m^v(x) \ge 0$. Applying (CC) we find

$$ \phi_i(v) = \sum_{x \in L} m^v(x) \phi_i(u_x) \quad (i \in N). $$

Since $m^v$ is a linear transform over $\mathcal{G}(L)$, $m^v(x)$ is a linear combination of all coefficients $v(y)$, $y \in L$. Rearranging terms, we finally find the desired form $\phi_i(v) = \sum_{y \in L} a_y^i v(y)$, where $a_y^i$ are real constants independent from $v$. Consider now a game $v \in \mathcal{G}_M(L)$. Applying (6), we write $v = v^+ - v^-$, and (D) yields

$$ \phi_i(v) = \phi_i(v^+ - v^-) = \phi_i(v^+) - \phi_i(v^-) = \sum_{x \in L} a_x^i v^+(x) - \sum_{x \in L} a_x^i v^-(x) = \sum_{x \in L} a_x^i v(x). $$

□

Taking two multichoice games $v$ and $w$ for which the marginal contribution of a player $i$ to a game $v$ is the same or the opposite of that to a game $w$, the average importance of attribute $i$ shall be the same for $v$ and $w$. We propose the following axiom.

**Marginal contribution axiom (MC):** Let $i \in N$ and $v, w \in \mathcal{G}(L)$ such that

$$ |\Delta_i v(x)| = |\Delta_i w(x)|, \forall x \in L, x_i < k_i. $$

Then $\phi_i(v) = \phi_i(w)$.

These three axioms suffice to characterize the family of absolute importance indices.

**Lemma 2.** For every $v \in \mathcal{G}(L)$, there exists $w \in \mathcal{G}_M(L)$, such that $|\Delta_i v| = |\Delta_i w|$.

**Proof.** Let $v \in \mathcal{G}(L)$, we set $D(x_i, y_{-i}) = \sum_{\substack{z_i \in L_i \\ z_i < x_i}} |\Delta_i v(z_i, y_{-i})|, \forall x_i \in L_i, y_{-i} \in L_{-i}$. Define the following multichoice game $w$:

$$ w(0, \dots, 0) = 0 \\ w(0_i, x_{-i}) = \max_{\substack{x_i \in L_i \\ y_{-i} \in L_{-i}, y_{-i} < x_{-i}}} (w(0_i, y_{-i}) + D(x_i, y_{-i}) - D(x_i, x_{-i})), \quad x_{-i} \in L_{-i} $$

$$ w(x_i, y_{-i}) = w(0_i, y_{-i}) + D(x_i, y_{-i}), \quad x_i > 0, y_{-i} \in L_{-i}. $$

Then by construction we have $w(x + 1_i) = w(x) + |\Delta_i v(x)|, \forall x \in L, x_i < k_i$. Hence, $w$ is a monotone multichoice game such that $|\Delta_i v| = |\Delta_i w|$ pointwise. □
**Proposition 4.** Under axioms (CC), (D) and (MC), there exist real constants $p_x^i$, $i \in N$, $x \in L$, $x_i < k_i$, such that for every $v \in \mathcal{G}(L)$,

$$ \phi_i(v) = \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i |\Delta_i v(x)|. \qquad (28) $$

**Proof.** Let $v \in \mathcal{G}(L)$, by Lemma 2 there exists a monotone multichoice game $w$, such that $|\Delta_i v(x)| = |\Delta_i w(x)|$ for all $x \in L$, $x_i < k_i$.

Observe that (CC) (or (D)) implies $\phi_i(0) = 0$ for all $i \in N$, where 0 indicates the null game. Suppose now that $i$ is a null player in $w$. It follows that $\Delta_i w(x) = 0 = \Delta_i 0(x)$ for all $x$. Then by (MC), we deduce $\phi_i(w) = 0$. In other words, $\phi$ satisfies the null axiom (N) for any game $w \in \mathcal{G}_M(L)$ (and even for any nonmonotonic game). Then proceeding as in the proof of Proposition 1, we deduce:

$$ \phi_i(w) = \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i (w(x + 1_i) - w(x)), $$

and by axiom (MC) we have, for all $i \in N$,

$$ \begin{aligned} \phi_i(v) &= \phi_i(w) \\ &= \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i (w(x + 1_i) - w(x)) \\ &= \sum_{\substack{x \in L \\ x_i < k_i}} p_x^i |\Delta_i v(x)|. \end{aligned} $$

□

*Remark 3.* As it can be seen from (28), the null axiom (N) is implied by (CC), (D) and (MC) (In fact, only one of (CC), (D) is necessary; see proof of Proposition 4).

We can now further restrict the family by imposing the invariance axiom that was used in Section 5. We obtain:

**Proposition 5.** Under axioms (CC), (D), (I) and (MC), for all $v \in \mathcal{G}(L)$

$$ \phi_i(v) = \sum_{\substack{x \in L \\ x_i < k_i}} p_{x-1}^i |v(x + 1_i) - v(x)|, \forall i \in N. $$

(the proof works as in the case of Proposition 2)

**Absolute Efficiency (AE):** Let $x \in L$ such that $N \setminus \Sigma(x) \neq \emptyset$ and $K(x) \neq \emptyset$.

$$ \sum_{i \in K(x)} \phi_i(\delta_x) = \sum_{i \in N \setminus \Sigma(x)} \phi_i(\delta_x). $$

The absolute efficiency axiom says that the total importance of attributes in the kernel equals the total importance of attributes outside the support. It shows the difference with the signed importance index. Indeed, considering $x \in L$ as above, by (19), we have $\sum_{i \in N} \phi_i^s(\delta_x) = \sum_{i \in K(x)} \phi_i^s(\delta_x) + \sum_{i \in N \setminus \Sigma(x)} \phi_i^s(\delta_x) = 0$, which implies $\sum_{i \in K(x)} \phi_i^s(\delta_x) = - \sum_{i \in N \setminus \Sigma(x)} \phi_i^s(\delta_x)$. If absolute differences are taken instead of signed ones, the previous expression is turned into (AE), as $\phi_i^s(\delta_x) > 0$ for $i \in K(x)$ and $\phi_i^s(\delta_x) < 0$ for $i \in N \setminus \Sigma(x)$.
**Calibration axiom (C):** For every $x \in L$ such that $N \setminus \Sigma(x) = \emptyset$.

$$ \sum_{i \in K(x)} \phi_i(\delta_x) = 1. $$

The calibration axiom addresses the case (uncovered by the two previous axioms) where there is no attribute outside the support of the Dirac game. Then again, all attributes in the kernel have equal importance, whose sum is normalized to 1.

**Theorem 2.** Under axioms (CC), (D), (I), (SD), (AE), (MC) and (C), for all $v \in \mathcal{G}(L)$ and all $i \in N$,

$$ \phi_i(v) = \phi_i^a(v) := \sum_{\substack{x \in L \\ x_i < k_i}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} |v(x + 1_i) - v(x)|. \quad (29) $$

**Proof.** It is easy to check that the formula (29) satisfies the axioms (CC), (D), (I), (SD), (AE) and (MC). Let us show that it satisfies the axiom (C). For every $x \in L$ such that $\sigma(x) = n$, we have,

$$ \begin{aligned} \phi_i^a(\delta_{x_{-i}, k_i}) &= \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} \\ &= \frac{\kappa(x_{-i})!}{(\kappa(x_{-i}) + 1)!} \\ &= \frac{1}{\kappa(x)}. \end{aligned} $$

Conversely, we consider $\phi$ satisfying the axioms (CC), (D), (I), (SD), (AE), (MC) and (C). Let $x \in L$, we write $x = (0_{N \setminus S \cup T}, x_S, k_T)$, with $x_S \in L_S \setminus \Gamma(L_S)$, $S = \Sigma(x) \setminus K(x)$, and $T = K(x)$.

By Proposition 5, we have

$$ \phi_i(\delta_x) = p_{x_{-i}}^i \sum_{\substack{y_i \in L_i \\ y_i < k_i}} |\delta_x(x_{-i}, y_i + 1) - \delta_x(x_{-i}, y_i)|, \forall i \in N. $$

Then we obtain,

$$ \phi_i(\delta_{(x_{-i}, k_i)}) = \phi_i(\delta_{(x_{-i}, 0_i)}) = p_{x_{-i}}^i, \quad (30) $$

and

$$ \phi_i(\delta_{(x_{-i}, x_i)}) = 2\phi_i(\delta_{(x_{-i}, k_i)}), \text{ for } x_i \in L_i \setminus \{0, k_i\}. \quad (31) $$

We only have to determine $\phi_i(\delta_{(x_{-i}, k_i)})$, with $x_{-i} = (0_{N \setminus S \cup T}, x_S, k_{T \setminus i})$.

We distinguish the two following cases:

(i) If $S \cup T = N$, by axiom (C), we have

$$ \phi_i(\delta_{x_{N \setminus T}, k_{T \setminus i}, k_i}) = \frac{1}{t}. $$

(ii) If $S \cup T \neq N$, by axioms (SD) and (AE), we have, $\forall t \in \{1, \dots, n-1\}, \forall s \in \{1, \dots, n-2\}$ with $s+t < n$,

$$ \phi_i(\delta_{(0_{N \setminus S \cup T}, x_S, k_{T \setminus i}, k_i)}) = \frac{n-s-t}{t} \phi_j(\delta_{(0_j, 0_{N \setminus S \cup T \cup j}, x_S, k_T)}), $$
by (30), we obtain,

$$ \phi_i(\delta_{(0_{N\setminus S\cup T}, x_S, k_{T\setminus i}, k_i)}) = \frac{n-s-t}{t} \phi_j(\delta_{(0_{N\setminus S\cup T\cup j}, x_S, k_{T\cup j\setminus i}, k_i)}), $$

and thus, recursively,

$$ \begin{aligned} \phi_i(\delta_{(0_{N\setminus S\cup T}, x_S, k_{T\setminus i}, k_i)}) &= \frac{n-s-t}{t} \phi_j(\delta_{(0_{N\setminus S\cup T\cup j}, x_S, k_{T\cup \{j\}})}) \\ &\vdots \\ &= \frac{(n-s-t)!(t-1)!}{(n-s-1)!} \phi_j(\delta_{(x_S, k_{N\setminus S\cup j}, k_i)}). \end{aligned} $$

By axiom (C), we finally obtain

$$ \begin{aligned} \phi_i(\delta_{(0_{N\setminus S\cup T}, x_S, k_{T\setminus i}, k_i)}) &= \frac{(n-s-t)!(t-1)!}{(n-s)!} \\ &= \frac{(n-s(x))!(k(x)-1)!}{(n+k(x)-s(x))!} \\ &= \frac{(n-s(x_{-i})-1)!(k(x_{-i}))!}{(n+k(x_{-i})-s(x_{-i}))!}. \end{aligned} $$

The result is proved. $\square$

<figure><img src="image_4.png" /></figure>

# 7 Norm-based importance indices

We turn now to the family of norm-based importance indices, which have the general form (10). As explained in Section 3, they appear as a natural generalization of absolute importance indices, which are based on the $L_1$ norm or its weighted version.

A fundamental difficulty is that the use of a norm different from $L_1$ forbids to take an axiomatic approach similar to the one we used in Section 6, because there would exist no class of games where a property similar to linearity would hold. Nevertheless, it is possible to obtain a general form through a number of axioms which are presented below. In the rest of this section $i \in N$ is fixed.

**Nonnegativity (NN) :** The importance index takes nonnegative values, i.e., $\phi_i : \mathcal{G}(L) \to \mathbb{R}_+$.

**Absolute Homogeneity (AH) :** For every $\alpha \in \mathbb{R}$ and every game $v \in \mathcal{G}(L)$,

$$ \phi_i(\alpha v) = |\alpha| \phi_i(v) $$

**Subadditivity (SA) :** For any games $v, w \in \mathcal{G}(L)$,

$$ \phi(v + w) \le \phi(v) + \phi(w) $$

**Strong Null axiom (SN) :** $\phi_i(v) = 0$ if and only if $i$ is null for $v$.
The nonnegativity axiom says that importance indices are nonnegative quantities. Absolute homogeneity says that multiplying a game be a constant just multiplies the importance index by the magnitude of this constant. The subadditivity axiom expresses the fact that summing two games $v, w$ may hinder the importance of an attribute by some hedging effect: the positive variation of $i$ at some point $x$ for $v$ can be cancelled by a negative variation at the same point for $w$. Lastly, the strong null axiom is a strong version of the usual null axiom, in the sense that only games whose attribute $i$ is null can lead to a null importance index for $i$.

We obtain the following.

**Theorem 3.** Under axioms (NN), (AH), (SA) and (SN), there exists a norm $||·||$ on $\mathbb{R}^{k(k+1)^{n-1}}$ and a linear one-to-one mapping $h$ on $\mathbb{R}^{k(k+1)^{n-1}}$ such that

$$ \phi_i(v) = ||h \circ \Delta_i(v)||. $$

**Proof.** To keep notation simple we assume $k_1 = \dots = k_n = k$, but this is not limitative.

1. We first establish the following general result.

**Lemma 3.** Let $g: \mathbb{R}^n \to \mathbb{R}^m$, $m \le n$, be a linear mapping, with $\dim(\ker(g)) = \ell$. Consider $f: \mathbb{R}^n \to \mathbb{R}^m$ a linear mapping. Then

$$ \ker(f) = \ker(g) \Leftrightarrow f = h \circ g, $$

with $h: \mathbb{R}^m \to \mathbb{R}^m$ a linear mapping and $\dim(\ker(h)) \le m - n + \ell$.

*Proof.* (of the Lemma) Consider first that $\ker(g) = \{0\}$, Then $n = m$, $\ell = 0$ and $f, g, h$ are bijections, with $h = f \circ g^{-1}$.

Consider now that $\ker(g) \ne \{0\}$, then $\ell \ge 1$. Choose $B = \{v_1, \dots, v_n\}$ with $v_i \in \mathbb{R}^m$, a basis of $\mathbb{R}^n$ such that $\{v_1, \dots, v_\ell\}$ is a basis of $\ker(g)$. Then $g(v_i) = f(v_i) = 0$ for $i = 1, \dots, \ell$, and $g, f$ are uniquely determined by the images of vectors $v_{\ell+1}, \dots, v_n$ by $g$ and $f$. We claim that $g(v_{\ell+1}), \dots, g(v_n)$ are independent vectors. Indeed, suppose there exist $\lambda_{\ell+1}, \dots, \lambda_n \in \mathbb{R}$, not all equal to 0, such that $\sum_{i=\ell+1}^n \lambda_i g(v_i) = 0$. By linearity of $g$,

$$ 0 = \sum_{i=\ell+1}^{n} \lambda_i g(v_i) = g\left(\sum_{i=\ell+1}^{n} \lambda_i v_i\right), $$

i.e., $\sum_{i=\ell+1}^n \lambda_i v_i \in \ker(g)$, which is impossible since $\{v_1, \dots, v_\ell\}$ is a basis of $\ker(g)$. The same conclusion holds for $f(v_{\ell+1}), \dots, f(v_n)$. Therefore, there exists a mapping $h: \mathbb{R}^m \to \mathbb{R}^m$ transforming the set of independent vectors $g(v_{\ell+1}), \dots, g(v_n)$ into the set of independent vectors $f(v_{\ell+1}), \dots, f(v_n)$, letting, e.g., $f(v_i) = h(g(v_i))$, $i = \ell+1, \dots, n$, and $\dim(\ker(h)) \le m - (n - \ell)$. $\square$

As a particular case, if $\dim(\ker(g)) = n - m$, then $h$ is a bijection.

2. Observe that the derivative of a game can be considered as a linear map $\Delta_i: \mathbb{R}^{(k+1)^{n-1}} \to \mathbb{R}^{k(k+1)^{n-1}}$. Its kernel is the set of games $v$ such that $\Delta_i v = 0$ pointwise (which is equivalent to: $i$ is null for $v$), i.e., $v(z+1)_i = v(z)$ for all $z \in L$ such that $z_i < k$, from which it follows that $v(z_{-i}, 0)_i = v(z_{-i}, 1)_i = \dots = v(z_{-i}, k_i)$. Hence, $\dim(\ker(\Delta_i)) = (k+1)^{n-1} - 1$.

It is easy to see that the basis of the kernel is $\{u_z\}_{z \in L, z_i=0}$, therefore the basis of the supplementary space (let us call it $B$) is $\{u_z\}_{z \in L, z_i>0}$.

3. Define the linear map $g: \mathcal{G}(L) \to \mathcal{G}(L)$ by

$$ g(u_z) = \begin{cases} 0 & \text{if } z \in L, z_i = 0 \\ u_z, & \text{otherwise.} \end{cases} $$
Then $\mathrm{Ker}(g) = \mathrm{Ker}(\Delta_i)$, and by Lemma 3 it follows that $g = h \circ \Delta_i$ and $h$ is a linear bijection on $\mathbb{R}^{k(k+1)^{n-1}}$. Moreover, $\mathrm{Im}(g) = B$.

Now, we can write $\phi_i = \psi \circ g$ with $\psi : B \to \mathbb{R}_+$, which defines $\psi$ unambiguously. We show that $\psi$ is a norm on $B$, which finishes the proof. Consider $v_1, v_2 \in B$, then there exist $w_1, w_2 \in G(L)$ such that $v_1 = g(w_1)$ and $v_2 = g(w_2)$. We have:

* Suppose $\psi(v_1) = 0$. Then $0 = \psi(v_1) = \psi(g(w_1)) = \phi_i(w_1)$, hence by (SN) this is equivalent to $w_1 \in \mathrm{Ker}(\Delta_i) = \mathrm{Ker}(g)$, equivalent to $v_1 = g(w_1) = 0$.

* For all $\alpha \in \mathbb{R}$, $\psi(\alpha v_1) = \psi(\alpha g(w_1)) = \psi(g(\alpha w_1)) = \phi_i(\alpha w_1)$, which by (AH) is equal to $|\alpha|\phi_i(w_1) = |\alpha|\psi(v_1)$.

* $\psi(v_1 + v_2) = \psi(g(w_1 + w_2)) = \phi_i(w_1 + w_2) \le \phi_i(w_1) + \phi_i(w_2) = \psi(v_1) + \psi(v_2)$, where we have used (SA).

Hence, $\psi$ is a norm on $B$.

<figure><img src="image_5.png" /></figure>

Additional axioms may be used to determine a particular norm or class of norms. Note however that the precise determination of $h$ through calibration or efficiency axioms seems to be difficult as $h$ lies inside the norm.

# 8 Discussion and examples

We apply Theorem 1 and Theorem 2 with the following examples.

**Example 2.** We give a practical example of computation of $\phi_i^s$ and $\phi_i^a$ to the Dirac and unanimity games. Let $x \in L \setminus \{0_N\}$ and $i \in N$.

* The computation of $\phi_i$ w.r.t. $\delta_x$ gives

$$ \phi_i^s(\delta_{x_{-i},0}) = -\phi_i^s(\delta_{x_{-i},k_i}) = -\frac{(n - \sigma(x_{-i}) - 1)!\kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!}, \text{and } \phi_i(\delta_{x_{-i},x_i}) = 0, \forall x_i \notin \{0, k_i\}. $$

$$ \phi_i^a(\delta_{x_{-i},0}) = \phi_i^a(\delta_{x_{-i},k_i}) = \phi_i^s(\delta_{x_{-i},k_i}), \text{ and } \phi_i^a(\delta_{x_{-i},x_i}) = 2\phi_i^a(\delta_{x_{-i},k_i}), \forall x_i \notin \{0, k_i\}. $$

* The computation of $\phi_i$ w.r.t. $u_x$ gives,

$$ \phi_i^s(u_x) = \phi_i^a(u_x) = \sum_{\substack{y_{-i} \in L_{-i} \\ y_{-i} \ge x_{-i}}} \frac{(n - \sigma(y_{-i}) - 1)!\kappa(y_{-i})!}{(n + \kappa(y_{-i}) - \sigma(y_{-i}))!}, \forall x_i \ne 0. $$

Let us take for example $N = \{1, 2, 3\}$, $k_N = (2, 2, 3)$, and $x = (0, 1, 3)$, we have,

$$ \phi_1^s(\delta_x) = -\frac{1}{2}, \quad \phi_2^s(\delta_x) = 0, \quad \phi_3^s(\delta_x) = \frac{1}{2}, $$

$$ \phi_1^a(\delta_x) = \frac{1}{2}, \quad \phi_2^a(\delta_x) = 1, \quad \phi_3^a(\delta_x) = \frac{1}{2}, $$

$$ \phi_1^s(u_x) = 0, \quad \phi_2^s(u_x) = 1, \quad \phi_3^s(u_x) = 3. $$
We note that $\phi_i^s(\delta_x)$ can be null when the peak is attained inside the domain ($i = 2$), is strictly negative if the peak is attained at 0 ($i = 1$), and is strictly positive if the peak is attained at the maximal value $k_i$ ($i = 3$). However, $\phi_i^a(\delta_x)$ is always strictly positive.

These results are in accordance with the theory, i.e., they illustrate in particular the respective efficiency and calibration axioms (E), (AE), (C). Also, they are intuitively appealing:

(i) The absolute importance index accounts for the cumulated absolute variations along some axis. For the game $\delta_x$, its value changes along attributes 1 and 3 only once, while it changes twice for attribute 2. This is exactly reflected by $\phi^a$.

(ii) The signed importance index accounts for the average monotonicity of the function along some axis. For attribute 1, $\delta_x(z)$ is decreasing along $z_1$, hence $\phi_1^s(\delta_x) < 0$, while it is increasing along attribute 3. Now, when $z_2$ goes from 0 to 2, $\delta_x(z)$ is first increasing then decreasing of the same amount. Thus, the overall variation is 0. As a conclusion, the signed importance index should be more interpreted as an indicator of overall tendency of monotonicity, rather than an importance index.

(iii) Unanimity games being monotone, the two importance indices coincide. Attribute 1 has a null importance index because $u_x$ is constant w.r.t. attribute 1. Attribute 3 has a greater importance index than attribute 2 because there is a variation of $u_x(z)$ along the second attribute $z_2$ only if $z_3 = 3$, while there is variation along $z_3$ for $z_2 = 1, 2$.

**Example 3 (Thermal comfort).** Thermal comfort is a subjective judgment depending on humidity ($X_1$), temperature ($X_2$) and air velocity ($X_3$) among other parameters. The concept of thermal comfort has been studied from a “thermal” exchange standpoint (Fanger, 1970; Haldi and Robinson, 2011). The Predicted Mean Vote (PMV) is an assessment of the mean thermal feeling of individuals, on a bipolar scale taking 5 levels (from “very cold” to “very hot”). It is based on four thermal ambient condition factors, namely, the ambient air temperature $T_a$ in °C, the ambient air velocity $V_a$ in m/s, the rate of moisture in the air (humidity) $H_y$ in %, and the radiant temperature $T_r$ in °C, and on two personal parameters related to each person, namely, the metabolic coefficient $M_e$, and the clothing insulation $C_i$ (Haldi and Robinson, 2011; Denguir, 2014). For the purpose of this paper, we can focus on the three most influencing variables $T_a$, $H_y$ and $V_a$. The Predicted Percentage Dissatisfied (PPD) is a transformation of the PMV into a [0, 100] scale where 0 means that there is no dissatisfaction and 100 means that there is a complete dissatisfaction (Fanger, 1970). Figures 1 (resp. 2) shows PPD as a function of $T_a$ and $H_y$ (resp. $T_a$ and $V_a$).

<figure><img src="image_6.png" /><figcaption>Figure 1: PPD$_{T_v=23}$ ($T_a$, $V_a$ = 0.2, $H_y$) – source (Denguir, 2014, Fig 10)</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 2: PPD<sub>T<sub>r</sub>=23</sub>(Ta, Va, Hy = 50) – source (Denguir, 2014, Fig 11)</figcaption></figure>

We consider that the function *U* measures the discomfort level over the set *X* = *X*<sub>1</sub>×*X*<sub>2</sub>×*X*<sub>3</sub>
defined by (Figure 3):

<table><tr><td>U(0, 23, 0) = 2</td><td>U(0, 25, 0) = 4</td><td>U(0, 28, 0) = 7</td></tr><tr><td>U(50, 23, 0) = 5</td><td>U(50, 25, 0) = 7</td><td>U(50, 28, 0) = 10</td></tr><tr><td>U(100, 23, 0) = 10</td><td>U(100, 25, 0) = 12</td><td>U(100, 28, 0) = 15</td></tr><tr><td>U(0, 23, 0.2) = 10</td><td>U(0, 25, 0.2) = 7</td><td>U(0, 28, 0.2) = 15</td></tr><tr><td>U(50, 23, 0.2) = 30</td><td>U(50, 25, 0.2) = 35</td><td>U(50, 28, 0.2) = 45</td></tr><tr><td>U(100, 23, 0.2) = 65</td><td>U(100, 25, 0.2) = 70</td><td>U(100, 28, 0.2) = 85</td></tr><tr><td>U(0, 23, 1) = 40</td><td>U(0, 25, 1) = 35</td><td>U(0, 28, 1) = 30</td></tr><tr><td>U(50, 23, 1) = 87</td><td>U(50, 25, 1) = 75</td><td>U(50, 28, 1) = 70</td></tr><tr><td>U(100, 23, 1) = 97</td><td>U(100, 25, 1) = 90</td><td>U(100, 28, 1) = 83</td></tr></table>

We define the multichoice game $v \in \mathcal{G}(L)$ with $L = \{0, 1, 2\}^3$ by

$$v(x) = U(Z(x)) - U(Z(0, 0, 0)), \text{ for any } x \in L,$$

where *Z* is a transformation of the elements of *L* in *X*:

<table><tr><td>Z(0, 0, 0) = (0, 23, 0)</td><td>Z(0, 1, 0) = (0, 25, 0)</td><td>Z(0, 2, 0) = (0, 28, 0)</td></tr><tr><td>Z(1, 0, 0) = (50, 23, 0)</td><td>Z(1, 1, 0) = (50, 25, 0)</td><td>Z(1, 2, 0) = (50, 28, 0)</td></tr><tr><td>Z(2, 0, 0) = (100, 23, 0)</td><td>Z(2, 1, 0) = (100, 25, 0)</td><td>Z(2, 2, 0) = (100, 28, 0)</td></tr><tr><td>Z(0, 0, 1) = (0, 23, 0.2)</td><td>Z(0, 1, 1) = (0, 25, 0.2)</td><td>Z(0, 2, 1) = (0, 28, 0.2)</td></tr><tr><td>Z(1, 0, 1) = (50, 23, 0.2)</td><td>Z(1, 1, 1) = (50, 25, 0.2)</td><td>Z(1, 2, 1) = (50, 28, 0.2)</td></tr><tr><td>Z(2, 0, 1) = (100, 23, 0.2)</td><td>Z(2, 1, 1) = (100, 25, 0.2)</td><td>Z(2, 2, 1) = (100, 28, 0.2)</td></tr><tr><td>Z(0, 0, 2) = (0, 23, 1)</td><td>Z(0, 1, 2) = (0, 25, 1)</td><td>Z(0, 2, 2) = (0, 28, 1)</td></tr><tr><td>Z(1, 0, 2) = (50, 23, 1)</td><td>Z(1, 1, 2) = (50, 25, 1)</td><td>Z(1, 2, 2) = (50, 28, 1)</td></tr><tr><td>Z(2, 0, 2) = (100, 23, 1)</td><td>Z(2, 1, 2) = (100, 25, 1)</td><td>Z(2, 2, 2) = (100, 28, 1)</td></tr></table>

It is clear that the function *v* is not monotone in its three arguments.

The computation of the importance index w.r.t. *v* gives,

$$\phi_1^s(v) \approx 188.17,$$

$$\phi_2^s(v) \approx 17.67,$$

$$\phi_3^s(v) \approx 247.17,$$

$$\phi_1^a(v) \approx 188.17,$$

$$\phi_2^a(v) \approx 50.33,$$

$$\phi_3^a(v) \approx 248.50.$$

The values of the absolute importance index show that air velocity is the most important criterion, then humidity, which both seem to be fairly more important than temperature. This slightly surprising conclusion may be explained by the fact the interval of variation of the temperature is fairly more restricted than the interval of variation of humidity and air velocity.
<figure><img src="image_8.png" /><figcaption>Figure 3: Discomfort level w.r.t. $X_1$, $X_2$ and $X_3$. Blue=comfortable ([0, 10]), orange=fairly comfortable ([10, 60]), red=uncomfortable ([60, 100]).</figcaption></figure>

Hence, the conclusion should be rather than, within the interval [23,28], temperature is relatively less important than humidity and air velocity.

The value of the signed importance index coincides with the absolute importance index for humidity since in the interval of temperature considered, the discomfort is increasing w.r.t. this attribute. The conclusion is almost the same for air velocity: the discomfort is increasing on most of the domain, except for high values, where it slightly decreases. By contrast, it can be checked on the table that the temperature has a more complex behavior. Depending on the values of the two other attributes, the discomfort is sometimes increasing, sometimes decreasing, and sometimes has a bell shape w.r.t. temperature. This is why the signed and the absolute importance indices differ, and the former one indicates that there is a slight overall tendency for the discomfort to augment with temperature.

# 9 The continuous case

In the continuous case of an aggregation function $F$, we have seen that a natural definition of the importance index is given by (7). It has been shown that this formula applied on $[0, 1]^n$ yields the Shapley value when the aggregation function is the Choquet integral (see (Marichal, 1998, proposition 5.3.3 page 141) and also (Grabisch et al., 2009, Definition 10.41 and Proposition 10.43 page 369))). We generalize this result in this section.

## 9.1 Definition of the Choquet integral

We consider first a normalized (1-ary) capacity on $N$, that is, a set function $\mu: 2^N \to \mathbb{R}$ satisfying monotonicity ($\mu(A) \le \mu(B)$ whenever $A \subseteq B$), and the normalization conditions $\mu(\emptyset) = 0$, $\mu(N) = 1$. The set of permutations on $N$ is denoted by $\mathfrak{S}$. For $\sigma \in \mathfrak{S}$, we define $\Omega_\sigma = \{t \in \mathbb{R}^N : t_{\sigma(1)} \le t_{\sigma(2)} \le \dots \le t_{\sigma(n)}\}$. The Choquet integral of $t \in [0, 1]^N$ can be written in terms of the capacity $\mu$ (Choquet, 1953)

$$C_{\mu}(t) = \sum_{i=1}^{n} (t_{\sigma(i)} - t_{\sigma(i-1)}) \mu(\{\sigma(i), \dots, \sigma(n)\}), \quad (32)$$
where $t_{\sigma(0)} := 0$ and $\sigma$ is a permutation on $N$ such that $t \in \Omega_{\sigma}$.

The Choquet integral can also be defined w.r.t. a $k_N$-ary capacity $v: L \to \mathbb{R}$. For $x \in X := \times_{i \in N}[0, k_i]$, we define $q_i(x) \in L_i$ by $q_i(x) \le x_i < q_i(x) + 1$ if $x_i < k_i$, and $q_i(k_i) = k_i - 1$. We set $q(x) = (q_1(x), \dots, q_n(x))$. Point $x$ lies in the $n$-dimensional interval $[q(x), q(x) + 1]$. The Choquet integral at $x$ w.r.t. $v$ is then simply the usual Choquet integral (32) at point $x - q(x) \in [0, 1]^n$ w.r.t. a game $\mu_{q(x)}^v$, defined by

$$ \mu_q^v(S) = v((q+1)_S, q_{N\setminus S}) - v(q). \quad (33) $$

Then the Choquet integral of $x$ w.r.t. $v$ is defined by (Grabisch and Labreuche, 2003a, 2008; Labreuche and Grabisch, to appear)

$$ C_v(x) = C_{\mu_{q(x)}^v}(x - q(x)) + v(q(x)). \quad (34) $$

## 9.2 Computation of the importance indices

For an aggregation function $F$ defined on an interval $[0, k_N] = \times_{i \in N}[0, k_i]$, we can define the signed and absolute importance of criterion $i$ by

$$ \text{Signed importance: } \psi_i^s(F) = \int_{[0, k_N]} \frac{\partial F(x)}{\partial x_i} dx, \quad (35) $$

$$ \text{Absolute importance: } \psi_i^a(F) = \int_{[0, k_N]} \left| \frac{\partial F(x)}{\partial x_i} \right| dx, \quad (36) $$

The next result relates the importance indices on $v$ with the indices defined on aggregation functions.

**Theorem 4.** For every $i \in N$,

$$ \psi_i^s(C_v) = \phi_i^s(v), \text{ for any } k_N\text{-ary capacity } v, \quad (37) $$

$$ \psi_i^a(C_v) = \phi_i^a(v), \text{ for any } v \in \mathcal{G}(L). \quad (38) $$

*Proof.* Let $v$ a $k_N$-ary capacity. For every $i \in N$, we have, by Lemma 1,

$$ \begin{align*} \phi_i^s(v) &= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \phi_i^{Sh}(\mu_x^v) \\ &= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \int_{[0,1]^n} \frac{\partial C_{\mu_x^v}(t)}{\partial t_i} dt \\ &= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \int_{[x, x+1]_N} \frac{\partial C_{\mu_x^v}(z-x)}{\partial z_i} dz \\ &= \sum_{\substack{x \in L \\ \forall j \in N, x_j < k_j}} \int_{[x, x+1]_N} \frac{\partial C_v(z)}{\partial z_i} dz \\ &= \int_{[0, k_N]} \frac{\partial C_v(z)}{\partial z_i} dz. \end{align*} $$
Hence (37) is proved.

Let $v \in \mathcal{G}(L)$, by Lemma 2 there exists $w \in \mathcal{G}_M(L)$ such that $|\Delta_i v(x)| = |\Delta_i w(x)|$ for all $x \in L$, $x_i < k_i$. Then $\left|\frac{\partial \mathcal{C}_v(x)}{\partial x_i}\right| = \left|\frac{\partial \mathcal{C}_w(x)}{\partial x_i}\right|$ for all $x \in [0, k_N]$. Hence, $\psi_i^a(\mathcal{C}_v) = \psi_i^a(\mathcal{C}_w) = \psi_i^s(\mathcal{C}_w)$, and by (37), we have

$$ \psi_i^a(\mathcal{C}_v) = \phi_i^s(\mathcal{C}_w). $$

By Theorem 1 we have

$$ \begin{aligned} \phi_i^s(\mathcal{C}_w) &= \sum_{x_{-i} \in L_{-i}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} (w(x_{-i}, k_i) - w(x_{-i}, 0_i)) \\ &= \sum_{x_{-i} \in L_{-i}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} \sum_{\substack{x_i \in L_i \\ x_i < k_i}} |w(x + 1_i) - w(x)| \\ &= \sum_{x_i \in L} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} |v(x + 1_i) - v(x)| \\ &= \phi_i^a(\mathcal{C}_v). \end{aligned} $$

Hence (38) is proved. $\square$

# 10 Another axiomatization

As it was mentioned early in the Section 5, the signed importance index (see Lemma 1) takes the form of the total over the domain $L$ of the classical Shapley value. Here in this section, we propose another axiomatisation of the importance index based on the decomposition of the index for each elementary cell in the grid $L$. The decomposition is apparent from the integral expression provided in the previous section.

For any $a = (a_1, \dots, a_n)$ and $b = (b_1, \dots, b_n) \in \mathbb{N}_0^n$, with $\mathbb{N}_0 = \mathbb{N} \cup \{0\}$, such that $a_i < b_i$ for all $i \in N$, define $L_i^{a_i, b_i} = \{a_i, a_i + 1, \dots, b_i\}$ and $L^{a,b} = \times_{i \in N} L_i^{a_i, b_i}$.

In this section, we consider a multichoice game as a mapping $v$ on $L^{m,M}$ such that $v(m) = 0$, with $m = (m_1, \dots, m_n)$ and $M = (M_1, \dots, M_n) \in \mathbb{N}_0^n$ indicating the lower bound and upper bound on each attribute. We denote by $\mathcal{G}(L^{m,M})$ the set of multichoice games on $L^{m,M}$. For $v \in \mathcal{G}(L^{m,M})$ and any $a, b \in \mathbb{N}_0^n$ such that $m_i \le a_i < b_i \le M_i$ for all $i$, we define the restriction $v' := v|_{L^{a,b}}$ in $\mathcal{G}(L^{a,b})$ as $v'(x) = v(x)$ for all $x \in L^{a,b}$.

The aim of this section is to define and axiomatize an importance index $\phi_i^{m,M}(v)$ for any attribute $i \in N$ and game $v \in \mathcal{G}(L^{m,M})$. Note that $L = L^{0_N, k_N}$, and $\phi = \phi^{0_N, k_N}$, while letting $0_N = (0, \dots, 0)$, $1_N = (1, \dots, 1)$, a classical game is an element of $\mathcal{G}(L^{0_N, 1_N})$.

The first axiom says that $\phi$ can be additively decomposed on the lattice $L^{m,M}$.

**Decomposition over Domains (DD):** Let $j \in N$ and $\ell \in \mathbb{N}$ such that $m_j < \ell < M_j$.

Let $v \in \mathcal{G}(L^{m,M})$. We define $v_1 = v|_{L^{m,(\ell_j,M_{-j})}}$ and $v_2 = v|_{L^{(\ell_j,M_{-j}),M}}$. Then for every $i \in N$

$$ \phi_i^{m,M}(v) = \phi_i^{m,(\ell_j,M_{-j})}(v_1) + \phi_i^{(\ell_j,M_{-j}),M}(v_2). \quad (39) $$

**Remark 4.** From DD applied $\prod_{i \in N} (M_i - m_i - 1)$ times, we obtain

$$ \phi_i^{m,M}(v) = \sum_{y \in L^{m,M-1,N}} \phi_i^{y,y+1,N}(v|_{L^{y,y+1,N}}). \quad (40) $$
**Linearity axiom (L<sub>01</sub>)**: $\phi^{0_N, 1_N}$ is linear on $\mathcal{G}(L^{0_N, 1_N})$, i.e., $\forall v, w \in \mathcal{G}(L^{0_N, 1_N})$, $\forall \alpha \in \mathbb{R}$,

$$ \phi^{0_N, 1_N}(v + \alpha w) = \phi^{0_N, 1_N}(v) + \alpha \phi^{0_N, 1_N}(w). $$

**Definition 2.** An attribute $i \in N$ is said to be null for $v \in \mathcal{G}(L^{0_N, 1_N})$ if

$$ v(x + 1_i) = v(x), \text{ for every } x \in L^{0_N, 1_N} \text{ such that } x_i = 0. $$

**Null axiom (N<sub>01</sub>):** If an attribute $i$ is null for $v \in \mathcal{G}(L^{0_N, 1_N})$, then

$$ \phi_i^{0_N, 1_N}(v) = 0. $$

**Equal Treatment (ET<sub>01</sub>):** Let $v \in \mathcal{G}(L^{0_N, 1_N})$ and $i, j \in N$.

If $v(x + 1_i) = v(x + 1_j)$ for all $x \in L^{0_N, 1_N}$ with $x_i = 0$ and $x_j = 0$, then

$$ \phi_i^{0_N, 1_N}(v) = \phi_j^{0_N, 1_N}(v). $$

**Efficiency (E<sub>01</sub>)**: $\forall v \in \mathcal{G}(L^{0_N, 1_N})$, $\sum_{i \in N} \phi_i^{0_N, 1_N}(v) = v(N)$.

The next axiom is a translation property. It says that the computation of the importance index in one elementary cell $L^{y, y+1_N}$ does not depend on the origin point $y$: it can be obtained from the computation of the index in $L^{0_N, 1_N}$ cell by a shift operation on the game.

**Translation axiom (T):** For all $y \in L^{m,M}$, such that $y + 1_N \le M$, and for any $i \in N$,

$$ \phi_i^{y, y+1_N}(v) = \phi_i^{0_N, 1_N}(T_y \circ v), $$

where $v \in \mathcal{G}(L^{y, y+1_N})$ and $T_y \circ v$ is a game in $\mathcal{G}(L^{0_N, 1_N})$ defined by $T_y \circ v(z) = v(y+z) - v(y), \forall z \in L^{0_N, 1_N}$.

**Theorem 5.** Under axioms (DD), (L<sub>01</sub>), (N<sub>01</sub>), (T), (ET<sub>01</sub>) and (E<sub>01</sub>), $\forall v \in \mathcal{G}(L^{m,M})$

$$ \phi_i^{m,M}(v) = \phi_i^{s;m,M}(v) = \sum_{x_{-i} \in L_{-i}^{m,M}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} (v(x_{-i}, M_i) - v(x_{-i}, m_i)), \forall i \in N. $$

**Proof.** It is easy to check that the formula satisfies all axioms.

Conversely, assume that $\phi^{m,M}$ satisfies all axioms, we have $\phi^{0_N, 1_N} = \phi^{Sh}$.
From (40) and (T), we have

$$
\begin{align*}
\phi_i^{m,M}(v) &= \sum_{y \in L^{m,M-1,N}} \phi_i^{0,N,1,N}(T_y \circ v_{|L^{y,y+1,N}}) \\
&= \sum_{y \in L^{m,M-1,N}} \phi_i^{Sh}(T_y \circ v_{|L^{y,y+1,N}}) \\
&= \sum_{y \in L^{m,M-1,N}} \sum_{x_{-i} \in L_{-i}^{0,N,1,N}} \frac{(n - \sigma(x_{-i}) - 1)! \sigma(x_{-i})!}{n} (T_y \circ v_{|L^{y,y+1,N}}(x+1_i) - T_y \circ v_{|L^{y,y+1,N}}(x)) \\
&= \sum_{y \in L^{m,M-1,N}} \sum_{x_{-i} \in L_{-i}^{0,N,1,N}} \frac{(n - \sigma(x_{-i}) - 1)! \sigma(x_{-i})!}{n} (v(x+y+1_i) - v(x+y)) \\
&= \sum_{y \in L^{m,M-1,N}} \sum_{S \subseteq N \setminus i} \frac{(n-s-1)!s!}{n} (v(y+1_S \cup i) - v(y+1_S)) \\
&= \sum_{\substack{z \in L^{m,M} \\ z_i < M_i}} (v(z+1_i) - v(z)) \sum_{\substack{S \subseteq N \setminus i \\ \forall j \in S, z_j > m_j \\ \forall j \in N \setminus S, z_j < M_j}} \frac{(n-s-1)!s!}{n!} \\
&= \sum_{\substack{z \in L^{m,M} \\ z_i < M_i}} (v(z+1_i) - v(z)) \sum_{\substack{S \subseteq (N \setminus i) \cap \sum(z_{-i}) \\ S \supseteq K(z_{-i})}} \frac{(n-s-1)!s!}{n!} \\
&= \sum_{\substack{z \in L^{m,M} \\ z_i < M_i}} (v(z+1_i) - v(z)) \sum_{\substack{S \subseteq \sum(z_{-i}) \\ S \supseteq K(z_{-i})}} \frac{(n-s-1)!s!}{n!} \\
&= \sum_{\substack{z \in L^{m,M} \\ z_i < M_i}} \frac{(n - \sigma(z_{-i}) - 1)! \kappa(z_{-i})!}{(n + \kappa(z_{-i}) - \sigma(z_{-i}))!} (v(z+1_i) - v(z)).
\end{align*}
$$

□

We turn now to the axiomatization of the family defined by the absolute value.

**Decomposition over Domains for Monotone games (DDM) :** $\phi^{m,M}$ satisfies the (DD) axiom on $\mathcal{G}_M(L^{m,M})$.

**Linearity for Monotone games (LM<sub>01</sub>):** $\phi^{0,N,1,N}$ is linear on $\mathcal{G}_M(L^{0,N,1,N})$.

**Efficiency for Monotone games (EM<sub>01</sub>) :** $\phi^{0,N,1,N}$ satisfies the (E<sub>01</sub>) axiom on $\mathcal{G}_M(L^{0,N,1,N})$.

We also use the marginal contribution axiom (MC) introduced in Section 6, but here for games in $\mathcal{G}(L^{m,M})$.

**Proposition 6.** Under axioms (DDM), (LM<sub>01</sub>), (N), (T), (ET), (EM<sub>01</sub>) and (MC), $\forall v \in \mathcal{G}(L^{m,M}), \forall i \in N$,

$$
\phi_i^{m,M}(v) = \phi_i^{a;m,M}(v) = \sum_{x_i \in L^{m,M} \setminus M_i} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} |v(x + 1_i) - v(x)|.
$$
**Proof.** It is easy to check that the formula satisfies all axioms.

Conversely, assume that $\phi^{m,M}$ satisfies all axioms. Then by Proposition 5 and Lemma 2 we have, for all $i \in N$,

$$ 
\begin{align*}
\phi_i^{m,M}(v) &= \phi_i^{m,M}(w) \\
&= \sum_{x_{-i} \in L_{-i}^{m,M}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} (w(x_{-i}, M_i) - w(x_{-i}, m_i)) \\
&= \sum_{\substack{x \in L^{m,M} \\ x_i < M_i}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} (w(x + 1_i) - w(x)) \\
&= \sum_{\substack{x \in L^{m,M} \\ x_i < M_i}} \frac{(n - \sigma(x_{-i}) - 1)! \kappa(x_{-i})!}{(n + \kappa(x_{-i}) - \sigma(x_{-i}))!} |\Delta_i v(x)|.
\end{align*}
$$

□

# 11 Concluding remarks

We have presented in this paper two families of importance indices, the signed and the absolute importance indices, together with two remarkable representatives $\phi^a, \phi^s$. They are valid for any kind of numerical model of representation of preferences (monotonic or not, discrete or continuous), and we have characterized them through natural and interpretable axioms. Further generalization is possible through the use of norms, but still further investigation is necessary.

The proposed indices are close in spirit to the Shapley value, and a similar approach could also be undertaken using as starting point the Banzhaf value.

Both indices have their usefulness, and their combination may shed light on the model under analysis. The signed index indicates the overall trend of the model w.r.t. an attribute: increasing or decreasing. The absolute index measures the amount of variation of the model induced by an attribute. An attribute having a high value of $\phi^a$ and a low value of $\phi^s$ indicates an attribute which has a strong contribution in the model, but the model is not monotone w.r.t. this attribute: it could be bell-shaped or could have a more complex behavior. We have illustrated the usage of these indices on examples, among which a modelling of discomfort based on real data. Lastly, we mention that these indices contribute to the theory of multichoice games by providing a new type of Shapley value.

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