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Well-formed decompositions of generalized additive independence models

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Abstract

Generalized additive independence (GAI) models permit to represent interacting variables in decision making. A fundamental problem is that the expression of a GAI model is not unique as it has several equivalent different decompositions involving multivariate terms. Considering for simplicity 2-additive GAI models (i.e., with multivariate terms of at most 2 variables), the paper examines the different questions (definition, monotonicity, interpretation, etc.) around the decomposition of a 2-additive GAI model and proposes as a basis the notion of well-formed decomposition. We show that the presence of a bi-variate term in a well-formed decomposition implies that the variables are dependent in a preferential sense. Restricting to the case of discrete variables, and based on a previous result showing the existence of a monotone decomposition, we give a practical procedure to obtain a monotone and well-formed decomposition and give an explicit expression of it in a particular case.

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Notes

  1. An ANOVA decomposition of a function \(f:X\subseteq \mathbb {R}^n\rightarrow \mathbb {R}\) can be defined as \(f(x)=\displaystyle \sum \nolimits _{A\subseteq N}f_A(x_A)\), where the decomposition is orthogonal in the sense that, for all \(A\subseteq N\), \(A\not = \emptyset \), the expected value of \(f_A\) w.r.t. any of its coordinate is zero. This ensures that a term \(f_A\) does not contain a function of a strict subset of variables \(B\subsetneq A\) that could be put in \(f_B\).

  2. This is not the only way to proceed. The substitution with \(\mu _{2_i1_j}^{-2i-j}\) can be done as well, yielding a branching with the condition \(m(1_{ij})+m(2_i1_j)\ge 0\) or \(<0\) and a different expression for \( [v_{\downarrow \! 2_i1_j}]\). This is illustrated in Example 1.

References

  • Bacchus, F., & Grove, A. (1995). Graphical models for preference and utility. In Proceedings of the eleventh conference on uncertainty in artificial intelligence (pp. 3–10). Morgan Kaufmann Publishers Inc.

  • Bigot, D., Fargier, H., Mengin, J., & Zanuttini, B. (2012). Using and learning GAI-decompositions for representing ordinal rankings. Montpellier, France, August: In Eur. Conf. on Artificial Intelligence.

  • Boutilier, C., Bacchus, F., & Brafman, R. (2001). UCP-networks: A directed graphical representation of conditional utilities. In Proceedings of the seventeenth conference on uncertainty in artificial intelligence (UAI-01) (pp. 56–64), Seattle.

  • Braziunas, D. (2012). Decision-theoretic elicitation of generalized additive utilities. PhD thesis.

  • Braziunas, D. & Boutilier. (2005) Local utility elicitation in GAI models. In Proceedings of the 9th international conference on the principles of knowledge (pp. 42–49).

  • Dubus, J.-P., Gonzales, Ch., & Perny, P. (2009) Fast recommendations using GAI models. In International joint conference on artificial intelligence (pp. 1896–1901).

  • Fishburn, P. C. (1967). Interdependence and additivity in multivariate, unidimensional expected utility theory. International Economic Review, 8(3), 335–342.

    Article  Google Scholar 

  • Gonzales, Ch., & Perny, P. (2004) GAI networks for utility elicitation. In Proceedings of the 9th international conference on the principles of knowledge (pp. 224–234).

  • Gonzales, Ch., Perny, P., & Dubus, J. P. (2011). Decision making with multiple objectives using gai networks. Artificial Intelligence Journal, 175, 1153–1179.

    Article  Google Scholar 

  • Grabisch, M. & Labreuche, Ch. (2003). Capacities on lattices and k-ary capacities. In International conference of the euro society for fuzzy logic and technology (EUSFLAT), Zittau, Germany, September 10–12.

  • Grabisch, M., & Labreuche, Ch. (2018). Monotone decomposition of 2-additive generalized additive independence models. Mathematical Social Sciences, 92, 64–73.

    Article  Google Scholar 

  • Gradshteyn, I. S. & Ryzhik, I. M. (2007) Table of integrals, series and products.

  • Hsiao, C. R., & Raghavan, T. E. S. (1990). Shapley value for multi-choice cooperative games. I. Discussion paper of the University of Illinois at Chicago.

  • Keeney, R. L., & Raiffa, H. (1976). Decision with multiple objectives. New York: Wiley.

    Google Scholar 

  • Krantz, D., Luce, R., Suppes, P., & Tversky, A. (1971). Additive and polynomial representations: foundations of measurement.

  • Rota, G. C. (1964). On the foundations of combinatorial theory I. Theory of Möbius functions. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 2, 340–368.

    Article  Google Scholar 

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Correspondence to Mustapha Ridaoui.

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Appendix: Monotone decompositions of \(v_{\downarrow \!x}\), for x of the form \(1_{ij}\), \(2_i1_j\), and \(2_{ij}\)

Appendix: Monotone decompositions of \(v_{\downarrow \!x}\), for x of the form \(1_{ij}\), \(2_i1_j\), and \(2_{ij}\)

  • Case 1 Suppose \(m(1_{ij})<0\). We make the substitution

    $$\begin{aligned} m(1_{ij})\mu _{1_{ij}} = -m(1_{ij})(\mu _{1_{ij}}^{-i-j}-\mu _{1_i}-\mu _{1_j}) \end{aligned}$$
    (33)

    which yields

    $$\begin{aligned}{}[v_{\downarrow \! 1_{ij}}] = |m(1_{ij})|\mu _{1_{ij}}^{-i-j} + (m(1_{ij}) + m(1_i))\mu _{1_i} + (m(1_{ij}) + m(1_j))\mu _{1_j}. \end{aligned}$$
    (34)
  • Case 2 Suppose \(m(2_i1_j)<0\) and \(m(2_j1_i)\ge 0\).

    • Case 2a \(m(1_{ij})<0\). We make the substitution

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j} = -m(2_i1_j)\big (\mu _{2_i1_j}^{-2i-j}-\mu _{2_i}-\mu _{1_j}\big ) \end{aligned}$$

      which yields, combined with (33):

      $$\begin{aligned}{}[v_{\downarrow \! 2_i1_j}]&= |m(2_i1_j)|\mu _{2_i1_j}^{-2i-j} + |m(1_{ij})|\mu _{1_{ij}}^{-i-j} +(m(2_i1_j)+m(2_i))\mu _{2_i} \nonumber \\&\quad + (m(1_{ij}) + m(1_i))\mu _{1_i} + (m(2_i1_j) + m(1_{ij}) + m(1_j))\mu _{1_j}. \end{aligned}$$
      (35)
    • Case 2b \(m(1_{ij})+m(2_i1_j)\ge 0\). We make the substitutionFootnote 2

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j} = -m(2_i1_j)\big (\mu _{2_i1_j}^{-i-j} - \mu _{1_{ij}} - \mu _{2_i}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned}{}[v_{\downarrow \! 2_i1_j}]&=|m(2_i1_j)|\mu _{2_i1_j}^{-i-j} + (m(2_i1_j) + m(1_{ij})) \mu _{1_{ij}}\nonumber \\&\quad + (m(2_i1_j) + m(2_i)) \mu _{2_i} + m(1_i)\mu _{1_i} + m(1_j)\mu _{1_j}. \end{aligned}$$
      (36)
    • Case 2c \(m(1_{ij})\ge 0\) and \(m(1_{ij})+m(2_i1_j)< 0\). We make the substitution

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j}&= m(1_{ij})\big (\mu _{2_i1_j}^{-i-j} - \mu _{1_{ij}} - \mu _{2_i}\big ) \\&\quad + (-m(2_i1_j)-m(1_{ij}))\big (\mu _{2_i1_j}^{-2i-j}-\mu _{2_i} - \mu _{1_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned}{}[v_{\downarrow \! 2_i1_j}]&= m(1_{ij})\mu _{2_i1_j}^{-i-j} + (-m(2_i1_j)-m(1_{ij}))\mu _{2_i1_j}^{-2i-j}\nonumber \\&\quad + (m(2_i1_j)+m(2_i))\mu _{2_i} + (m(2_i1_j)+m(1_{ij}) \nonumber \\&\quad + m(1_j)) \mu _{1_j} + m(1_i)\mu _{1_i}. \end{aligned}$$
      (37)
  • Case 3 Suppose \(m(2_i1_j)<0\) and \(m(2_j1_i)<0\). Then the intersection \(\downarrow \! 2_i1_j\cap \downarrow \! 2_j1_i\) contains \(1_{ij}\), so that we need a monotone decomposition for \(v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}\).

    • Case 3a \(m(1_{ij})<0\). We make the substitution

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j} + m(2_j1_i)\mu _{2_j1_i}&= -m(2_i1_j)\big (\mu _{2_i1_j}^{-2i-j}-\mu _{2_i}-\mu _{1_j}\big ) \\&\quad +(-m(2_j1_i))\big (\mu _{2_j1_i}^{-2j-i}-\mu _{2_j}-\mu _{1_i}\big ) \end{aligned}$$

      which yields, together with (33)

      $$\begin{aligned}{}[v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}]&= |m(2_i1_j)|\mu _{2_i1_j}^{-2i-j} + |m(2_j1_i)|\mu _{2_j1_i}^{-2j-i} + |m(1_{ij})|\mu _{1_{ij}}^{-i-j} \nonumber \\&\quad + (m(2_i1_j) + m(2_i))\mu _{2_i} + (m(2_j1_i) + m(2_j))\mu _{2_j} \nonumber \\&\quad + (m(2_j1_i)+m(1_{ij}) + m(1_i))\mu _{1_i} + (m(2_i1_j)+m(1_{ij}) \nonumber \\&\quad + m(1_j))\mu _{1_j}. \end{aligned}$$
      (38)
    • Case 3b \(m(2_i1_j)+m(1_{ij})\ge 0\) and \(m(2_j1_i)+m(1_{ij})\ge 0\). Then we write, assuming \(m(2_i1_j)\le m(2_j1_i)\)

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j} + m(2_j1_i)\mu _{2_j1_i}&= -m(2_j1_i)\big (\mu _{ij}^{\wedge \wedge } - \mu _{2_i} - \mu _{2_j} - \mu _{1_{ij}}\big ) \\&\quad + (m(2_j1_i) - m(2_i1_j))\big (\mu _{2_i1_j}^{-i-j}-\mu _{1_{ij}} - \mu _{2_i}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned}{}[v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}]&= |m(2_j1_i)|\mu _{ij}^{\wedge \wedge } + (m(2_j1_i) - m(2_i1_j))\mu _{2_i1_j}^{-i-j} \nonumber \\&\quad +(m(1_{ij}) + m(2_i1_j))\mu _{1_{ij}} \nonumber \\&\quad + (m(2_i1_j) + m(2_i))\mu _{2_i} + (m(2_j1_i) + m(2_j))\mu _{2_j} \nonumber \\&\quad + m(1_i)\mu _{1_i} + m(1_j)\mu _{1_j}. \end{aligned}$$
      (39)
    • Case 3c \(m(1_{ij})\ge 0\), \(m(2_i1_j)+m(1_{ij})<0\) and \(m(2_j1_i) + m(1_{ij})\ge 0\). Then we write, assuming \(m(2_i1_j)\le m(2_j1_i)\)

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j} + m(2_j1_i)\mu _{2_j1_i}&= -m(2_j1_i)\big (\mu _{ij}^{\wedge \wedge } - \mu _{2_i} - \mu _{2_j} - \mu _{1_{ij}}\big ) \\&\quad + (m(2_j1_i) + m(1_{ij}))\big (\mu _{2_i1_j}^{-i-j} - \mu _{2_i} - \mu _{1_{ij}}\big ) \\&\quad + (-m(2_i1_j) - m(1_{ij}))\big (\mu _{2_i1_j}^{-2i-j} - \mu _{2_i} - \mu _{1_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned}{}[v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}]&= |m(2_j1_i)|\mu _{ij}^{\wedge \wedge } + (-m(2_i1_j) - m(1_{ij}))\mu _{2_i1_j}^{-2i-j} + (m(2_j1_i) \nonumber \\&\quad + m(1_{ij}))\mu _{2_i1_j}^{-i-j} + (m(2_i1_j)+m(2_i))\mu _{2_i} + (m(2_j1_i)+m(2_j))\mu _{2_j} \nonumber \\&\quad +(m(2_i1_j)+m(1_{ij})+m(1_j))\mu _{1_j}+m(1_i)\mu _{1_i}. \end{aligned}$$
      (40)
    • Case 3d \(m(1_{ij})\ge 0\), \(m(2_i1_j)+m(1_{ij})<0\) and \(m(2_j1_i) + m(1_{ij})< 0\). Then we write, assuming \(m(2_i1_j)\le m(2_j1_i)\)

      $$\begin{aligned} m(2_i1_j)\mu _{2_i1_j} + m(2_j1_i)\mu _{2_j1_i}&+ m(1_{ij})\mu _{1_{ij}} = -m(2_j1_i)\big (\mu _{ij}^{\wedge \wedge }-\mu _{2_i}-\mu _{2_j}\big )\\&\quad + (-m(2_j1_i) -m(1_{ij}))\big (\mu _{1_{ij}}^{-i-j} -\mu _{1_i} - \mu _{1_j} \big )\\&\quad +(m(2_j1_i)-m(2_i1_j))\big (\mu _{2_i1_j}^{-2i-j} - \mu _{2_i} - \mu _{1_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned}{}[v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}]&= |m(2_j1_i)|\mu _{ij}^{\wedge \wedge } + (-m(2_j1_i) - m(1_{ij}))\mu _{1_{ij}}^{-i-j} \nonumber \\&\quad + (m(2_j1_i)-m(2_i1_j))\mu _{2_i1_j}^{-2i-j} \nonumber \\&\quad +(m(2_i1_j) + m(2_i))\mu _{2_i} + (m(2_j1_i) + m(2_j))\mu _{2_j} \nonumber \\&\quad + (m(2_j1_i) + m(1_{ij}) + m(1_i))\mu _{1_i} + (m(2_i1_j) \nonumber \\&\quad +m(1_{ij}) + m(1_j))\mu _{1_j}. \end{aligned}$$
      (41)
  • Case 4 Suppose \(m(2_{ij})<0\).

    • Case 4a \(m(2_i1_j)<0\) and \(m(2_j1_i)<0\). We make the substitution

      $$\begin{aligned} m(2_{ij})\mu _{2_{ij}} = -m(2_{ij})\big (\mu _{2_{ij}}^{-2i-2j} - \mu _{2_i} - \mu _{2_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned} v_{\downarrow \!2_{ij}}= & {} m(2_{ij})\mu _{2_{ij}} + v_{\downarrow \!2_i1_j \cup \downarrow \!2_j1_i} = -m(2_{ij})\big (\mu _{2_{ij}}^{-2i-2j} - \mu _{2_i} - \mu _{2_j}\big ) \nonumber \\&\quad +v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}. \end{aligned}$$
      (42)

      It remains to substitute in the above the expression of \(v_{\downarrow \!2_i1_j\cup \downarrow \!2_j1_i}\) by (38) to (41) successively. Observe that only the coefficients of \(\mu _{2_i},\mu _{2_j}\) are affected, and they are changed \(m(2_{ij})+m(2_i1_j)+m(2_i)\) and \(m(2_{ij})+m(2_j1_i)+m(2_j)\) respectively, which are nonnegative.

    • Case 4b \(m(2_i1_j)<0\) and \(m(2_{ij}) + m(2_j1_i)\ge 0\). We make the substitution

      $$\begin{aligned} m(2_{ij})\mu _{2_{ij}} = -m(2_{ij})\big (\mu _{2_{ij}}^{-2j-i} - \mu _{2_i} - \mu _{2_j1_i}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned} v_{\downarrow \!2_{ij}}&= -m(2_{ij})\big (\mu _{2_{ij}}^{-2j-i} - \mu _{2_i} - \mu _{2_j1_i}\big ) + v_{\downarrow 2_i1_j} + m(2_j1_i)\mu _{2_j1_i} + m(2_j)\mu _{2_j}\nonumber \\&= v_{\downarrow 2_i1_j} +|m(2_{ij})|\mu _{2_{ij}}^{-2j-i} + (m(2_j1_i) + m(2_{ij}))\mu _{2_j1_i} \nonumber \\&\quad + m(2_j)\mu _{2_j} + m(2_{ij})\mu _{2_i}. \end{aligned}$$
      (43)

      It remains to substitute in the above the expression of \(v_{\downarrow 2_i1_j}\) by (35) to (37) successively. Observe that only the coefficient of \(\mu _{2_i}\) is affected.

    • Case 4c \(m(2_i1_j)<0\), \(m(2_j1_i)\ge 0\) and \(m(2_{ij}) + m(2_j1_i)< 0\). We make the substitution

      $$\begin{aligned} m(2_{ij})\mu _{2_{ij}}&= m(2_j1_i)\big (\mu _{2_{ij}}^{-2j-i} - \mu _{2_i} - \mu _{2_j1_i}\big ) + (-m(2_{ij}) \nonumber \\&\quad - m(2_j1_i))\big (\mu _{2_{ij}}^{-2i-2j} - \mu _{2_i}- \mu _{2_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned} v_{\downarrow \!2_{ij}}&= m(2_j1_i)\big (\mu _{2_{ij}}^{-2j-i} - \mu _{2_i} - \mu _{2_j1_i}\big ) + (-m(2_{ij}) - m(2_j1_i))\nonumber \\&\quad \big (\mu _{2_{ij}}^{-2i-2j} - \mu _{2_i}- \mu _{2_j}\big ) + v_{\downarrow 2_i1_j} + m(2_j1_i)\mu _{2_j1_i} + m(2_j)\mu _{2_j}\nonumber \\&= v_{\downarrow 2_i1_j} + m(2_j1_i)\mu _{2_{ij}}^{-2j-i} + (-m(2_{ij})-m(2_j1_i))\mu _{2_{ij}}^{-2i-2j}\nonumber \\&\quad + m(2_{ij})\mu _{2_i} + (m(2_{ij})+m(2_j1_i) + m(2_j))\mu _{2_j}. \end{aligned}$$
      (44)

      It remains to substitute in the above the expression of \(v_{\downarrow 2_i1_j}\) by (35) to (37) successively. Observe that only the coefficient of \(\mu _{2_i}\) is affected.

    • Case 4d \(m(2_{ij}) + m(2_i1_j)\ge 0\) and \(m(2_{ij}) + m(2_j1_i)\ge 0\). We make the substitution

      $$\begin{aligned} m(2_{ij})\mu _{2_{ij}} = -m(2_{ij})\big (\mu _{2_{ij}}^{-i-j} - \mu _{2_i1_j} - \mu _{2_j1_i}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned} v_{\downarrow \!2_{ij}}&= -m(2_{ij})\big (\mu _{2_{ij}}^{-i-j} - \mu _{2_i1_j} - \mu _{2_j1_i}\big ) + v_{\downarrow 2_i1_j} + m(2_j1_i)\mu _{2_j1_i} + m(2_j)\mu _{2_j}\nonumber \\&= v_{\downarrow 2_i1_j} + |m(2_{ij})|\mu _{2_{ij}}^{-i-j} + m(2_{ij})\mu _{2_i1_j} + (m(2_j1_i)+m(2_{ij}))\mu _{2_j1_i} + m_{2j}\mu _{2j}. \end{aligned}$$

      We have two subcases:

      \(*\):

      If \(m(1_{ij})<0\), then using (34), we have

      $$\begin{aligned} v_{\downarrow \!2_i1_j}&=m(2_i1_j)\mu _{2_i1_j} + v_{\downarrow \!1_{ij}} +m(2_i)\mu _{2_i}\\&= m(2_i1_j)\mu _{2_i1_j} + |m(1_{ij})|\mu _{1_{ij}}^{-i-j} + m(2_i)\mu _{2_i} \\&\quad + (m(1_{ij}) + m(1_i))\mu _{1_i} + (m(1_{ij}) + m(1_j))\mu _{1_j} \end{aligned}$$

      which inserted in the expression of \(v_{\downarrow \!2_{ij}}\) yields

      $$\begin{aligned}{}[v_{\downarrow \!2_{ij}}]&= |m(2_{ij})|\mu _{2_{ij}}^{-i-j} + |m(1_{ij})|\mu _{1_{ij}}^{-i-j} + (m(2_{ij})+m(2_i1_j))\mu _{2_i1_j} \nonumber \\&\quad + (m(2_j1_i)+m(2_{ij}))\mu _{2_j1_i} + m_{2j}\mu _{2j} + + m(2_i)\mu _{2_i} \nonumber \\&\quad + (m(1_{ij}) + m(1_i))\mu _{1_i} + (m(1_{ij}) + m(1_j))\mu _{1_j}. \end{aligned}$$
      (45)
      \(*\):

      If \(m(1_{ij})\ge 0\), then we get

      $$\begin{aligned}{}[v_{\downarrow \!2_{ij}}]&= |m(2_{ij})|\mu _{2_{ij}}^{-i-j} + (m(2_{ij})+m(2_i1_j))\mu _{2_i1_j} + (m(2_j1_i)+m(2_{ij}))\mu _{2_j1_i} \nonumber \\&\quad +m(1_{ij})\mu _{1_{ij}} + m(2_i)\mu _{2_i} +m_{2j}\mu _{2j} + m(1_i)\mu _{1_i} + m(1_j)\mu _{1_j}. \end{aligned}$$
      (46)
    • Case 4e \(m(2_{ij}) + m(2_i1_j)\ge 0\), \(m(2_j1_i)\ge 0\) and \(m(2_{ij}) + m(2_j1_i)< 0\). We make the substitution

      $$\begin{aligned} m(2_{ij})\mu _{2_{ij}}&= m(2_j1_i)\big (\mu _{2_{ij}}^{-i-j} - \mu _{2_i1_j} - \mu _{2_j1_i}\big ) + (-m(2_{ij}) - m(2_j1_i))\\&\quad \times \big (\mu _{2_{ij}}^{-2i-j}-\mu _{2_j} - \mu _{2_i1_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned} v_{\downarrow \!2_{ij}}&= m(2_j1_i)\big (\mu _{2_{ij}}^{-i-j} - \mu _{2_i1_j} - \mu _{2_j1_i}\big ) + (-m(2_{ij}) - m(2_j1_i))\big (\mu _{2_{ij}}^{-2i-j} -\mu _{2_j}-\mu _{2_i1_j}\big )\\&\quad + v_{\downarrow 2_i1_j} + m(2_j1_i)\mu _{2_j1_i} + m(2_j)\mu _{2_j}\\&= v_{\downarrow 2_i1_j} + (-m(2_{ij}) - m(2_j1_i))\mu _{2_{ij}}^{2i-j} + m(2_j1_i)\mu _{2_{ij}}^{-i-j} \\&\quad + m(2_{ij})\mu _{2_i1_j} + (m(2_{ij})+m(2_j1_i)+m(2_j))\mu _{2_j} \end{aligned}$$

      As above, we have two cases, which yield:

      \(*\):

      If \(m(1_{ij})<0\):

      $$\begin{aligned}{}[v_{\downarrow \!2_{ij}}]&=(-m(2_{ij}) - m(2_j1_i))\mu _{2_{ij}}^{2i-j} + m(2_j1_i)\mu _{2_{ij}}^{-i-j} + (m(2_{ij})+m(2_i1_j))\mu _{2_i1_j} \nonumber \\&\quad + |m(1_{ij})|\mu _{1_{ij}}^{-i-j} + (m(2_{ij})+m(2_j1_i)+m(2_j))\mu _{2_j} + m(2_i)\mu _{2_i} \nonumber \\&\quad + (m(1_{ij}) +m(1_i))\mu _{1_i} + (m(1_{ij}) + m(1_j))\mu _{1_j}. \end{aligned}$$
      (47)
      \(*\):

      If \(m(1_{ij})\ge 0\):

      $$\begin{aligned}{}[v_{\downarrow \!2_{ij}}]&= (-m(2_{ij}) - m(2_j1_i))\mu _{2_{ij}}^{2i-j} + m(2_j1_i)\mu _{2_{ij}}^{-i-j} + (m(2_{ij})+m(2_i1_j))\mu _{2_i1_j} \nonumber \\&\quad + m(1_{ij})\mu _{1_{ij}} + (m(2_{ij})+m(2_j1_i)+m(2_j))\mu _{2_j} \nonumber \\&\quad + m(2_i)\mu _{2_i} + m(1_i)\mu _{1_i} + m(1_j)\mu _{1_j}. \end{aligned}$$
      (48)
    • Case 4f \(m(2_i1_j)\ge 0\), \(m(2_j1_i)\ge 0\), \(m(2_{ij}) + m(2_i1_j)< 0\) and \(m(2_{ij}) + m(2_j1_i)< 0\). Then we write, assuming \(m(2_i1_j)\le m(2_j1_i)\),

      $$\begin{aligned} m(2_{ij})\mu _{2_{ij}}&= m(2_i1_j)\big (\mu _{2_{ij}}^{-i-j}- \mu _{2_i1_j}-\mu _{2_j1_i} \big ) + (m(2_j1_i)-m(2_i1_j))\\&\quad \times \big (\mu _{2_{ij}}^{-i-2j}-\mu _{2_j1_i}-\mu _{2_i}\big ) \\&\quad +(-m(2_{ij})-m(2_j1_i))\big (\mu _{2_{ij}}^{-2i-2j}- \mu _{2_i} - \mu _{2_j}\big ) \end{aligned}$$

      which yields

      $$\begin{aligned} v_{\downarrow \!2_{ij}}&= m(2_i1_j)\big (\mu _{2_{ij}}^{-i-j} - \mu _{2_i1_j} - \mu _{2_j1_i}\big ) + (m(2_j1_i)-m(2_i1_j))\big (\mu _{2_{ij}}^{-i-2j} - \mu _{2_j1_i} - \mu _{2_i}\big ) \\&\quad +(-m(2_{ij})-m(2_j1_i))\big (\mu _{2_{ij}}^{-2i-2j} - \mu _{2_i} - \mu _{2_j}\big )\\&\quad + v_{\downarrow 2_i1_j} + m(2_j1_i)\mu _{2_j1_i} + m(2_j)\mu _{2_j}\\&= v_{\downarrow 2_i1_j} +m(2_i1_j)\mu _{2_{ij}}^{-i-j} + (m(2_j1_i)-m(2_i1_j)) \mu _{2_{ij}}^{-i-2j} \\&\quad +(-m(2_{ij})-m(2_j1_i))\mu _{2_{ij}}^{-2i-2j} + (-m(2_i1_j))\mu _{2_i1_j} \\&\quad +(m(2_{ij}) + m(2_i1_j))\mu _{2_i} +(m(2_{ij}) + m(2_j1_i) + m(2_j))\mu _{2_j} . \end{aligned}$$

      Distinguishing the two cases yields, assuming \(m(2_i1_j)\le m(2_j1_i)\)

      \(*\):

      If \(m(1_{ij})<0\):

      $$\begin{aligned}{}[v_{\downarrow \!2_{ij}}]&=m(2_i1_j)\mu _{2_{ij}}^{-i-j} + (m(2_j1_i)-m(2_i1_j))\mu _{2_{ij}}^{-i-2j} \nonumber \\&\quad +(-m(2_{ij})-m(2_j1_i))\mu _{2_{ij}}^{-2i-2j} + (m(2_{ij}) + m(2_i1_j) + m(2_i))\mu _{2_i}\nonumber \\&\quad + (m(2_{ij}) + m(2_j1_i) + m(2_j))\mu _{2_j} + |m(1_{ij})|\mu _{1_{ij}}^{-i-j} \nonumber \\&\quad +(m(1_{ij})+m(1_i))\mu _{1_i} + (m(1_{ij})+m(1_j))\mu _{1_j}. \end{aligned}$$
      (49)
      \(*\):

      If \(m(1_{ij})\ge 0\):

      $$\begin{aligned}{}[v_{\downarrow \!2_{ij}}]&=m(2_i1_j)\mu _{2_{ij}}^{-i-j} + (m(2_j1_i)-m(2_i1_j))\mu _{2_{ij}}^{-i-2j} \nonumber \\&\quad +(-m(2_{ij})-m(2_j1_i))\mu _{2_{ij}}^{-2i-2j} + (m(2_{ij}) + m(2_i1_j) + m(2_i))\mu _{2_i}\nonumber \\&\quad + (m(2_{ij}) + m(2_j1_i) + m(2_j))\mu _{2_j} + m(1_{ij})\mu _{1_{ij}} \nonumber \\&\quad +m(1_i)\mu _{1_i} + m(1_j)\mu _{1_j}. \end{aligned}$$
      (50)

In all formulas (34) to (50), it can be checked using (20) to (22) that all coefficients are nonnegative, hence they are indeed monotone decompositions.

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Grabisch, M., Labreuche, C. & Ridaoui, M. Well-formed decompositions of generalized additive independence models. Ann Oper Res 312, 827–852 (2022). https://doi.org/10.1007/s10479-020-03844-w

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