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A generalization of the Harsanyi NTU value to games with incomplete information

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Abstract

In this paper, we introduce a solution concept generalizing the Harsanyi non-transferable utility (NTU) value to cooperative games with incomplete information. The so-defined S-solution is characterized by virtual utility scales that extend the Harsanyi-Shapley fictitious weighted-utility transfer procedure. We construct a three-player cooperative game in which Myerson’s (Int J Game Theory 13(2):69–96, 1984a) generalization of the Shapley NTU value does not capture some “negative” externality generated by the adverse selection. However, when we explicitly compute the S-solution in this game, it turns out that it prescribes a more intuitive outcome which takes into account the above mentioned informational externality.

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Notes

  1. The Shapley NTU value is sometimes referred as the “\(\lambda \)-transfer value”. The Harsanyi NTU value, being less tractable, has received less attention. Indeed, the Shapley NTU value was introduced as a simplification of the Harsanyi NTU value. Both values are compared in Hart (1985a) in terms of their axiomatic characterizations and in Hart (2004) by means of a simple example. The reader is referred to Peleg and Sudhölter (2007, Ch. 13) and Myerson (1991, Ch. 9) for further details and formal definitions of these two solution concepts.

  2. Myerson (1992) provides a detailed explanation of the fictitious transfer procedure.

  3. De Clippel’s example is an incomplete information version of a NTU game introduced by Owen (1971).

  4. A characteristic function game V is comprehensive if, for every (nonempty) coalition S, whenever \(V(S)\subseteq {\mathbb {R}}^S\) contains an allocation u, it also contains all allocations v satisfying \(v\le u\). Further assumptions are also required for the existence of the Harsanyi NTU value: (i) if \(u,v\in \partial V(N)\) (i.e., u and v are efficient for the grand coalition) and \(u\ge v\), then \(u=v\) (non-levelness); (ii) \(V(N)=K+C\), where \(K\subseteq {\mathbb {R}}^N\) is a compact set and \(C\subseteq {\mathbb {R}}^N\) is a convex cone [see Peleg and Sudhölter (2007, Theorem 13.3.5)]. Assumption (i) excludes vanishing utility weights, while (ii) is a technical assumption guaranteeing that the set of utility weights \(\lambda \in {\mathbb {R}}^N_{++}\) for which the “primal problem” \(\max _{v\in V(N)}\lambda \cdot v\) has a finite optimum is compact and convex. It is worth noticing that these assumptions are not necessary for the definition of the Harsanyi NTU value. They express restrictions on the space of games for which a Harsanyi NTU value can be computed. Similar hypothesis are also required for the axiomatic characterization of the Harsanyi NTU value (see Hart (1985a)).

  5. See Forges and Serrano (2013) for a discussion about this issue.

  6. For any two sets A and B, \(A\subseteq B\) denotes weak inclusion (i.e., possibly \(A=B\)), and \(A\subset B\) denotes strict inclusion.

  7. For simplicity we write \(S\setminus i\), \(S\cup i\) and \(D_i\) instead of the more cumbersome \(S\setminus \{i\}\), \(S\cup \{i\}\) and \(D_{\{i\}}\).

  8. The common prior assumption is made without loss of generality, since the solution concept developed in this paper satisfies the probability-invariance axiom described by Myerson (1984a), and so for any game with inconsistent beliefs, conditional probabilities and utilities can be jointly modified in a way that the new game satisfies the common prior assumption and both games impute probability and utility functions that are decision-theoretically equivalent [see also Myerson (1991, pp. 72–73)].

  9. We have departed slightly from the formal definition of Holmström and Myerson (1983) in using strict inequalities rather than weak inequalities and one strict inequality.

  10. This property is specially useful for practical applications, in particular when computing value allocations.

  11. A strong solution may not exist, but if so it is unique up to equivalence in utility.

  12. This allocation is implemented by the mechanism \(\mu _{\{1,3\}}(d^1_{13}\mid L)=1-\mu _{\{1,3\}}(d^3_{13}\mid L)=3/4\), \(\mu _{\{1,3\}}(d^1_{13}\mid H)=\mu _{\{1,3\}}(d^3_{13}\mid H)=1/2\).

  13. Although we have tried to make our arguments as compelling as possible, this sort of discussion may leave room for disagreement.

  14. When incentive constraints matter, a safe mechanism for player 3 is one which would be incentive compatible and individually rational if player 1 knew 3’s type.

  15. This allocation is implemented by the mechanism \(\mu _{\{1,3\}}(d^1_{13}\mid L)=\mu _{\{1,3\}}([d_1,d_3]\mid L)=1/2\), \(\mu _{\{1,3\}}(d^1_{13}\mid H)=\mu _{\{1,3\}}(d^3_{13}\mid H)=1/2\).

  16. The incentive efficient mechanism \(\mu _N([d_{12},d_3]\mid t)=\tfrac{2}{3}\), \(\mu _N([d^2_{23},d_1]\mid t)=\mu _N([d^1_{13},d_2]\mid t)=\tfrac{1}{6}\) for all \(t\in T_3\) is an M-solution. The value is supported by the utility weights \((\lambda _1,\lambda _2,\lambda ^H_3,\lambda ^L_3)=(1,1,9/10,1/5)\) and the Lagrange multipliers \((\alpha _1(L\mid H),\alpha _1(H\mid L))=(0,0)\). We focus only on non-degenerated values, i.e., those which are supported by strictly positive utility weights \(\lambda \). Utility weights are determined up to a positive scalar multiplication. We then normalize utility weights so that virtual utilities of the uninformed players coincide with their real utilities. This is possible since 1 and 2 are symmetric. Explicit computations are given in the Online supplementary material.

  17. It can be shown that when player 3 drops out of the game and coalition \(\{1,2\}\) forms, the constraint asserting that type \(1_H\) has no incentive to report to be type \(1_L\) is binding in any incentive efficient mechanism for this coalition.

  18. When a coalition S forms, it cannot rely on the information possessed by the players outside S. In other words, a communication mechanism for a coalition must be measurable with respect to the private information of its members. This is equivalent to define a mechanism as \(\mu _S:T\rightarrow {\varDelta }(D_S)\) with \(\mu _S(t)=\mu _S(t')\) for every \(t,t'\in T\) such that \(t_S=t'_S\).

  19. Strictly speaking, the component \(\mu _N\in {\mathcal {M}}_N\) of \(\eta \) is not a threat, since there is no coalition to threaten. However, we keep this terminology in order to simplify the exposition.

  20. When information is complete, so that \(T_i\) is a singleton for every \(i\in N\), (11) reduces to the first condition in Proposition 6 of Imai (1983).

  21. It also extends the “preservation of average differences” principle introduced by Hart and Mas-Colell (1996).

  22. A definition like that would be consistent with Imai’s (1983) characterization of the Harsanyi NTU value.

  23. Clearly, when \(S=N\) this definition coincides with the one introduced in Sect. 2.

  24. Egalitarian solutions generalize the monotonic solutions introduced by Kalai and Samet (1985) to games with incomplete information.

  25. Singleton coalitions are not constrained by the egalitarian restrictions in (12)

  26. A two-person bargaining problem is a cooperative game satisfying: \(n=2\), \(D_i=\{d_i\}\) for all \(i\in N\) and \(u_i(d^*,t)=0\) for all \(i\in N\) and \(t\in T\), where \(d^*:=[d_i,d_j]\) is the disagreement outcome.

  27. A TU game (NW) is weakly superadditive if and only if for each player \(i\in N\), \(W(S\setminus i)+W(\{i\})\le W(S)\) for all coalitions \(S\subseteq N\) containing i. Clearly, by definition of the Shapley TU value, weak superadditivity implies that \(\phi _i(N,W)\ge W(\{i\})\) for every \(i\in N\).

  28. The same utility weights support the unique M-value (see Sect. 3).

  29. Detailed computations are provided in the Online supplementary material.

  30. The game \({\varGamma }_C\) can be used to construct a game with incomplete information for which there is no S-solution. Let N and \((D_S)_{S\subseteq N}\) be defined as in \({\varGamma }_C\). For each \(i=1,2,3\), let \(T_i\) be a singleton. Player 4 has private information in the form of two possible types \(T_4=\{A,B\}\) with prior probabilities \(q(A)=1-q(B)>0\). Utility functions are defined as follows: \(w_i(d_N,A)=u_i(d_N)\) and \(w_i(d_N,B)=\beta u_i(d_N)\) (with \(\beta >0\)), where \((u_i)_{i\in N}\) is defined as in \({\varGamma }_C\). Then, the game \({\varGamma }_I=\{N,(D_S)_{S\subseteq N},(w_i,T_i)_{i\in N},q\}\) has no S-solution. Indeed, because the incentives of player 4 are fully aligned in both states, incentive constraints are not essential. Thus, we can set the Lagrange multipliers to be \(\alpha _4(A\mid B)=\alpha _4(B\mid A)=0\). Virtual utilities then reduce to \(\lambda \)-weighted utilities. The rest of the analysis follows, mutatis mutandis, the same reasoning as for \({\varGamma }_C\).

  31. Clearly, this issue is not present in \({\varGamma }_C\), this being a game with complete information. Neither is it in \({\varGamma }_I\), as incentive constraints are not essential in this game. Nevertheless, in more general games in which incentives constraints are binding, the same difficulties are also encountered. In those cases, in addition to the utility weights \(\lambda \), also the dual variables \(\alpha \) have to be taken into consideration. Exemplify such situations is, however, more difficult due to the endogenous nature of the dual variables.

  32. In this game, only type H has incentives to impersonate type L. In an effort to distinguish himself from type H, type L may agree on a mechanism that discards an appropriate amount of utility in state H. Clearly, this “commitment strategy” does not affect type L, but is harmful for type H. Therefore, type H will never accept any such mechanism. In this way, types become essentially verifiable.

  33. It is worth noticing that in two-person games the S-solution always exists. This follows from the existence of the M-solution, since by Theorem 1 both solutions coincide whenever \(n=2\). The issue illustrated in the previous example does not bring any difficulty for the existence of the S-solution in two-person games. The reason is that, equity imposes no restrictions for singleton coalitions. Hence, in this case, allowing for free disposal is no longer necessary for guaranteeing existence and (upper-hemi)continuity of the optimal egalitarian threats.

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Acknowledgements

This paper makes up part of my Ph.D. dissertation written at Toulouse School of Economics. I am very much indebted to Françoise Forges for her insights, her continuous guidance and for innumerable discussions. I am also grateful to Thomas Mariotti, François Salanié, Peter Sudhölter and two anonymous referees of this journal for their comments and remarks that helped me to improve the paper. I acknowledge valuable feedback and suggestions from participants at “Dynamic Approach to Game and Economic Theory: Conference Celebrating the 65th Birthday of Sergiu Hart” and “10th BiGSEM Doctoral Workshop on Economic Theory”.

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Appendix

Appendix

1.1 Proof of Proposition 4

Let \(\mu _N\) be an S-solution of \({\varGamma }_C\) supported by \(\lambda \) and \(\eta =(\mu _S)_{S\subseteq N}\). We verify recursively conditions (i)-(iv). Because \({\varGamma }_C\) has complete information, there are no incentive constraints, which is equivalent to set \(\alpha =0\), so that virtual utility reduces to \(\lambda \)-weighted utility and the egalitarian criterion in (12) becomes

$$\begin{aligned} \lambda _i\left( u_i(\mu _S)-u_i(\mu _{S\setminus j})\right) =\lambda _j\left( u_j(\mu _S)-u_j(\mu _{S\setminus i})\right) ,\quad \forall i,j\in S. \end{aligned}$$
(26)

For coalitions consisting of a single player i, it is clear that \(u_i(\mu _i)=0\). For all two-person coalitions containing player 4, \(D_{\{i,4\}}=\{[d_i,d_4]\}\). Then, it follows immediately that \(u_i(\mu _{\{i,4\}})=u_4(\mu _{\{i,4\}})=0\) for all \(i\in N\setminus 4\). Similarly, \(D_{\{1,3\}}=\{[d_1,d_3]\}\), thus \(u_1(\mu _{\{1,3\}})=u_3(\mu _{\{1,3\}})=0\). Consider now coalition \(\{1,2\}\). It can be easily verified that an optimal egalitarian threat for this coalition satisfies:

$$\begin{aligned} \left( u_1(\mu _{\{1,2\}}),u_2(\mu _{\{1,2\}})\right) =\left\{ \begin{array}{ll} (2,2), &{} \quad \text {if}\;\lambda _1=\lambda _2>0 \\ (y,y),\; y\in [0,2], &{} \quad \text {if}\; \lambda _1=\lambda _2=0 \\ (0,0), &{} \quad \text {if}\;\lambda _1\ne \lambda _2 \end{array} \right. \end{aligned}$$

Similarly,

$$\begin{aligned} \left( u_2(\mu _{\{2,3\}}),u_3(\mu _{\{2,3\}})\right) =\left\{ \begin{array}{ll} (1,1), &{} \quad \text {if}\;\lambda _2=\lambda _3>0 \\ (y,y),\; y\in [0,1], &{} \quad \text {if}\; \lambda _2=\lambda _3=0 \\ (0,0), &{} \quad \text {if}\;\lambda _2\ne \lambda _3 \end{array} \right. \end{aligned}$$

We proceed now by cases.

Case 1:\(\lambda _1=\lambda _2=\lambda _3>0\) Condition (26) applied to \(S=\{1,2,3\}\) leads to Eqs.  (24) and (25). We have already shown in Sect. 7 that these two equations are incompatible.

Case 2:\(\lambda _1=\lambda _2>0\), \(\lambda _2\ne \lambda _3\) Without loss of generality, we can set \(\lambda _1=\lambda _2=1\). It can be easily verified that, (26) implies: \(u_1(\mu _{\{1,2,3\}})=u_2(\mu _{\{1,2,3\}})=u_1(\mu _{\{1,2,4\}})=u_2(\mu _{\{1,2,4\}})=2\) and \(u_3(\mu _{\{1,2,3\}})=u_4(\mu _{\{1,2,4\}})=u_2(\mu _{\{2,3,4\}})=u_3(\mu _{\{2,3,4\}})=u_4(\mu _{\{2,3,4\}})=0\). Then, condition (26) applied to N reduces to

$$\begin{aligned} u_1(\mu _N)= & {} u_2(\mu _N) \end{aligned}$$
(27a)
$$\begin{aligned} u_1(\mu _N)= & {} \lambda _3u_3(\mu _N)+2 \end{aligned}$$
(27b)
$$\begin{aligned} u_1(\mu _N)= & {} \lambda _4u_4(\mu _N)+2 \end{aligned}$$
(27c)

On the other hand, we have that

$$\begin{aligned}&u_1(d_N)+u_2(d_N)+\lambda _3u_3(d_N)+\lambda _4u_4(d_N) \nonumber \\&\quad =\left\{ \begin{array}{ll} 2+3(\lambda _3+\lambda _4), &{} \quad \text {if}\; d_N=d^1_N,\,d^2_N\\ 6-\lambda _3+3\lambda _4, &{} \quad \text {if}\; d_N=d^3_N\\ 6+3\lambda _3-\lambda _4, &{} \quad \text {if}\; d_N=d^4_N \end{array} \right. \end{aligned}$$
(27d)

Subcase 2.1:\(\lambda _3>1\), \(\lambda _4>1\) Condition (i) implies that \(\mu _N(d^3_N)=\mu _N(d^4_N)=0\). But then, (27a) requires that \(\mu _N(d^1_N)=\mu _N(d^2_N)=1/2\). Hence, \(u_1(\mu _N)=u_2(\mu _N)=1\) and \(u_3(\mu _N)=u_4(\mu _N)=3\). With this, (27b) reduces to \(\lambda _3=-1/3\), which is a contradiction.

Subcase 2.2:\(\lambda _3>1\), \(\lambda _4=1\) Condition (i) implies that \(\mu _N(d^3_N)=0\). But then, \(u_1(\mu _N)<\lambda _3u_3(\mu _N)+2\), which contradicts (27b).

Subcase 2.3:\(\lambda _3>1\), \(\lambda _4<1\) Condition (i) implies that \(\mu _N(d^4_N)=1\). But then, (27c) reduces to \(\lambda _4=-1\).

Subcase 2.4:\(\lambda _3<1\), \(\lambda _4\ge 1\) Condition (i) implies that \(\mu _N(d^3_N)=1\). But then, \(u_1(\mu _N)>\lambda _3u_3(\mu _N)+2\), which contradicts (27b).

Subcase 2.5:\(\lambda _4<\lambda _3<1\) Condition (i) implies that \(\mu _N(d^4_N)=1\). The same conclusion as in the case 2.3 is obtained.

Subcase 2.6:\(\lambda _3<\lambda _4<1\) Condition (i) implies that \(\mu _N(d^3_N)=1\). The same conclusion as in the case 2.4 is obtained.

Subcase 2.7:\(0<\lambda _3=\lambda _4<1\) Condition (i) implies that \(\mu _N(d^3_N)=1-\mu _N(d^4_N)=\beta \) with \(\beta \in [0,1]\). Condition (27b) implies

$$\begin{aligned} \beta [1+\lambda _3]+(1-\beta )[1-3\lambda _3]=0\;\Rightarrow \; \beta =\frac{3\lambda _3-1}{4\lambda _3} \end{aligned}$$

Similarly, (27c) implies

$$\begin{aligned} \beta [1-3\lambda _3]+(1-\beta )[1+\lambda _3]=0\;\Rightarrow \; \beta =\frac{1+\lambda _3}{4\lambda _3} \end{aligned}$$

Therefore, \(3\lambda _3-1=1+\lambda _3\), or equivalently, \(\lambda _3=1\), which is a contradiction.

Subcase 2.8:\(\lambda _3=\lambda _4=0\) As in the previous case, condition (i) implies that \(\mu _N(d^3_N)=1-\mu _N(d^4_N)=\beta \) with \(\beta \in [0,1]\). However, (27a) and (27b) imply that \(u_1(\mu _N)=u_2(\mu _N)=2\), which cannot be satisfied by any such mechanism.

Case 3:\(\lambda _1=\lambda _2=\lambda _3=0\) From (26) with \(S=N\), \(i=2\) and \(j=4\), we get that \(\lambda _4u_4(\mu _N)=0\) (since \(u_4(\mu _{\{1,3,4\}})=0\)). Hence, \(\sum _{i\in N}\lambda _iu_i(\mu _N)=0\). However, condition (i) implies that \(\sum _{i\in N}\lambda _iu_i(\mu _N)=3\), which is a contradiction.

Case 4:\(\lambda _1=\lambda _2=0\), \(\lambda _2\ne \lambda _3\) Condition (26) applied to \(S=N\) with \(i=2\) and \(j=3,4\) gives \(\lambda _3u_3(\mu _N)=\lambda _4u_4(\mu _N)=0\) (since \(u_3(\mu _{\{1,3,4\}})=u_4(\mu _{\{1,3,4\}})=0\)). Hence, \(\sum _{i\in N}\lambda _iu_i(\mu _N)=0\). However, condition (i) implies that \(\sum _{i\in N}\lambda _iu_i(\mu _N)>0\), which is a contradiction.

Case 5:\(\lambda _1\ne \lambda _2\), \(\lambda _2=\lambda _3>0\) Without loss of generality, we can set \(\lambda _2=\lambda _3=1\). It can be easily verified that, (26) implies: \(u_2(\mu _{\{1,2,3\}})=u_3(\mu _{\{1,2,3\}})=u_2(\mu _{\{2,3,4\}})=u_3(\mu _{\{2,3,4\}})=1\) and \(u_1(\mu _{\{1,2,3\}})=u_1(\mu _{\{1,2,4\}})=u_2(\mu _{\{1,2,4\}})=u_4(\mu _{\{1,2,4\}})=u_4(\mu _{\{2,3,4\}})=0\). Then, condition (26) applied to N reduces to

$$\begin{aligned} \lambda _1u_1(\mu _N)= & {} u_2(\mu _N)-1 \end{aligned}$$
(28a)
$$\begin{aligned} \lambda _1u_1(\mu _N)= & {} u_3(\mu _N)-1 \end{aligned}$$
(28b)
$$\begin{aligned} \lambda _1u_1(\mu _N)= & {} \lambda _4u_4(\mu _N) \end{aligned}$$
(28c)

On the other hand, we have that

$$\begin{aligned}&\lambda _1u_1(d_N)+u_2(d_N)+u_3(d_N)+\lambda _4u_4(d_N) \nonumber \\&\quad =\left\{ \begin{array}{ll} 2+3(\lambda _1+\lambda _4), &{} \text {if}\; d_N=d^2_N,\,d^3_N\\ 6-\lambda _1+3\lambda _4, &{} \text {if}\; d_N=d^1_N\\ 6+3\lambda _1-\lambda _4, &{} \text {if}\; d_N=d^4_N \end{array} \right. \end{aligned}$$
(28d)

Subcase 5.1:\(\lambda _1>1\), \(\lambda _4>1\) Condition (i) implies that \(\mu _N(d^2_N)=1-\mu _N(d^3_N)=\beta \) with \(\beta \in [0,1]\). But then, since \(u_2(\mu _N)=u_3(\mu _N)\) (by (28a) and (28b)), we must necessarily have that \(\beta =1/2\). Therefore, \(u_2(\mu _N)=u_3(\mu _N)=1\) and \(u_1(\mu _N)=u_4(\mu _N)=3\). However, this together with (28a) imply that \(\lambda _1=0\), which is a contradiction.

Subcase 5.2:\(\lambda _1>1\), \(\lambda _4=1\) Condition (i) implies that \(\mu _N(d^1_N)=0\). Hence, (since \(\lambda _1>2/3\)) \(\lambda _1u_1(\mu _N)>u_2(\mu _N)-1\), which contradicts (28a).

Subcase 5.3:\(\lambda _1>1\), \(\lambda _4<1\) Condition (i) implies that \(\mu _N(d^4_N)=1\). With this, (28a) implies that \(\lambda _1=2/3<1\), which is a contradiction.

Subcase 5.4:\(\lambda _1<1\), \(\lambda _4\ge 1\) Condition (i) implies that \(\mu _N(d^1_N)=1\). This together with (28a) imply that \(\lambda _1=-2\).

Subcase 5.5:\(\lambda _1<\lambda _4<1\) Condition (i) implies that \(\mu _N(d^1_N)=1\). The same conclusion as in case 5.4 is obtained.

Subcase 5.6:\(\lambda _4<\lambda _1<1\) Condition (i) implies that \(\mu _N(d^4_N)=1\). Hence, (28c) implies that \(3\lambda _1=-\lambda _4\). Therefore, (since \(\lambda _1,\lambda _2\ge 0\)) \(\lambda _1=\lambda _2=0\), which is a contradiction.

Subcase 5.7:\(0<\lambda _4=\lambda _1<1\) Condition (i) implies that \(\mu _N(d^1_N)=1-\mu _N(d^4_N)=\beta \), with \(\beta \in [0,1]\). On the other hand, (28c) implies that \(u_1(\mu _N)=u_4(\mu _N)\). Hence, we must have that \(\beta =1/2\). But then, (28a) implies that \(\lambda _1=2\), which is a contradiction.

Subcase 5.8:\(0=\lambda _4=\lambda _1\) As in the previous case, condition (i) implies that \(\mu _N(d^1_N)=1-\mu _N(d^4_N)=\beta \), with \(\beta \in [0,1]\). Hence, \(u_2(\mu _N)=u_3(\mu _N)=3\). However, by (28a) and (28b), \(u_2(\mu _N)=u_3(\mu _N)=1\), which contradicts the previous fact.

Case 6:\(\lambda _1\ne \lambda _2\), \(\lambda _2=\lambda _3=0\) Condition (26) applied to \(S=N\) with \(i=2\) and \(j=1,4\) gives \(\lambda _1u_1(\mu _N)=\lambda _4u_4(\mu _N)=0\) (since \(u_1(\mu _{\{1,3,4\}})=u_4(\mu _{\{1,3,4\}})=0\)). Hence, \(\sum _{i\in N}\lambda _iu_i(\mu _N)=0\). However, condition (i) implies that \(\sum _{i\in N}\lambda _iu_i(\mu _N)=3(1+\lambda _4)>0\), which is a contradiction.

Case 7:\(0=\lambda _2\ne \lambda _1\), \(\lambda _2\ne \lambda _3\) Condition (26) applied to \(S=N\) with \(i=2\) and \(j=1,3,4\) gives \(\lambda _1u_1(\mu _N)=\lambda _3u_3(\mu _N)=\lambda _4u_4(\mu _N)=0\) (since \(u_1(\mu _{\{1,3,4\}})=u_3(\mu _{\{1,3,4\}})=u_4(\mu _{\{1,3,4\}})=0\)). Hence, \(\sum _{i\in N}\lambda _iu_i(\mu _N)=0\). However, condition (i) implies that \(\sum _{i\in N}\lambda _iu_i(\mu _N)=3(\lambda _1+\lambda _3+\lambda _4)>0\) (since we must have \(\lambda _1>0\) and \(\lambda _3>0\)). But this is a contradiction.

Case 8:\(\lambda _2>\lambda _1\), \(\lambda _2\ne \lambda _3\) Condition (26) applied to \(S=N\) with \(i=1\) and \(j=2\) gives \(\lambda _1u_1(\mu _N)=\lambda _2u_2(\mu _N)\) (since \(u_1(\mu _{\{1,3,4\}})=u_2(\mu _{\{2,3,4\}})=0\)). On the other hand, condition (i) implies that \(\mu _N(d^2_N)=0\). But then, this implies that \(\lambda _1u_1(\mu _N)<\lambda _2u_2(\mu _N)\), which is a contradiction.

Case 9:\(\lambda _2<\lambda _1\), \(\lambda _2\ne \lambda _3\) As in the previous case, we have that \(\lambda _1u_1(\mu _N)=\lambda _2u_2(\mu _N)\). We also have that condition (i) implies \(\mu _N(d^1_N)=0\). But then, this implies that \(\lambda _1u_1(\mu _N)>\lambda _2u_2(\mu _N)\), which is a contradiction.

We conclude that \({\varGamma }_C\) has no S-solution. This completes the proof.

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Salamanca, A. A generalization of the Harsanyi NTU value to games with incomplete information. Int J Game Theory 49, 195–225 (2020). https://doi.org/10.1007/s00182-019-00686-0

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