Research Article | Open Access
Convex Interval Games
Convex interval games are introduced and characterizations are given. Some economic situations leading to convex interval games are discussed. The Weber set and the Shapley value are defined for a suitable class of interval games and their relations with the interval core for convex interval games are established. The notion of population monotonic interval allocation scheme (pmias) in the interval setting is introduced and it is proved that each element of the Weber set of a convex interval game is extendable to such a pmias. A square operator is introduced which allows us to obtain interval solutions starting from the corresponding classical cooperative game theory solutions. It turns out that on the class of convex interval games the square Weber set coincides with the interval core.
In classical cooperative game theory payoffs to coalitions of players are known with certainty. A classical cooperative game is a pair where is a set of players and is a map, assigning to each coalition a real number, such that . Often, we also refer to such a game as a (transferable utility) TU game. We denote by the family of all classical cooperative games with player set . The class of convex games  is one of the most interesting classes of cooperative games from a theoretical point of view as well as regarding its applications in real-life situations. A game is convex (or supermodular) if and only if the supermodularity condition for each holds true. Many characterizations of classical convex games are available in literature (, Biswas et al. , Brânzei et al. , Martinez-Legaz [5, 6]). On the class of classical convex games solution concepts have nice properties; for details we refer the reader to Brânzei et al. . Classical convex games have many applications in economic and real-life situations. It is well-known that classical public good situations , sequencing situations (Curiel et al. ), and bankruptcy situations (, Aumann and Maschler , Curiel et al. ) lead to convex games.
However, there are many real-life situations in which people or businesses are uncertain about their coalition payoffs. Situations with uncertain payoffs in which the agents cannot await the realizations of their coalition payoffs cannot be modelled according to classical game theory. Several models that are useful to handle uncertain payoffs exist in the game theory literature. We refer here to chance-constrained games (Charnes and Granot ), cooperative games with stochastic payoffs (Suijs et al. ), cooperative games with random payoffs (Timmer et al. . In all these models probability and stochastic theory plays an important role.
This paper deals with a model of cooperative games where only bounds for payoffs of coalitions are known with certainty. Such games are called cooperative interval games. Formally, a cooperative interval game in coalitional form (Alparslan Gök et al. ) is an ordered pair where is the set of players, and is the characteristic function such that , where is the set of all nonempty, compact intervals in . For each , the worth set (or worth interval) of the coalition in the interval game is of the form . We denote by the family of all interval games with player set . Note that if all the worth intervals are degenerate intervals, that is, for each , then the interval game corresponds in a natural way to the classical cooperative game where for all .
Cooperative interval games are very suitable to describe real-life situations in which people or firms that consider cooperation have to sign a contract when they cannot pin down the attainable coalition payoffs, knowing with certainty only their lower and upper bounds. Such contracts should specify how the interval uncertainty with regard to the coalition values will be incorporated in the allocation of the worth before its uncertainty is resolved, and how the realization of the payoff for the grand coalition will be finally distributed among the players. Interval solution concepts for cooperative interval games are a useful tool to settle cooperation within the grand coalition via such (binding) contracts.
An interval solution concept on is a map assigning to each interval game a set of -dimensional vectors whose components belong to . We denote by the set of all such interval payoff vectors. An interval allocation obtained by interval solution concept commonly chosen by the players before the interval uncertainty with regard to the coalition values is removed offers at this ex-ante stage an estimation of what individual players may receive, between two bounds, when the uncertainty on the reward of the grand coalition is removed in the ex post stage. We notice that the agreement on a particular interval allocation based on an interval solution concept merely says that the payoff that player will receive in the interim or ex post stage is in the interval . This is a very weak contract to settle cooperation. Therefore, writing down in the contact how to transform the interval allocation of into an allocation in of the realization of , , in a consistent way with , that is, for each , is mandatory (see Brânzei et al. ).
In this paper, we introduce the class of convex interval games and extend classical results regarding characterizations of convex games and properties of solution concepts to the interval setting. Some classical -games associated with an interval game will play a key role, namely, the border games , and the length game , where for each . Note that .
The paper is organized as follows. In Section 2 we recall basic notions and facts from the theory of cooperative interval games. In Section 3 we introduce supermodular and convex interval games and give basic characterizations of convex interval games. In Section 4 we introduce for size monotonic interval games the notions of interval marginal operators, the interval Shapley value and the interval Weber set and study their properties for convex interval games. Moreover, we introduce the notion of population monotonic interval allocation scheme (pmias) and prove that each element of the interval Weber set of a convex interval game is extendable to such a pmias. In Section 5 we introduce the square operator and describe some interval solutions for interval games that have close relations with existing solutions from the classical cooperative game theory. It turns out that on the class of convex interval games the interval core and the square interval Weber set coincide. Finally, in Section 6 we conclude with some remarks on further research.
2. Preliminaries on Interval Calculus and Interval Games
In this section some preliminaries from interval calculus and some useful results from the theory of cooperative interval games are given (Alparslan Gök et al. ).
Let with , , and . Then,(i);(ii).
By (i) and (ii) we see that has a cone structure.
In this paper we also need a partial substraction operator. We define , only if , by . Note that . We recall that is weakly better than , which we denote by , if and only if and . We also use the reverse notation , if and only if and . We say that is better than , which we denote by , if and only if and .
For we say that if , for each . For and we define and by and for each . So, we conclude that endowed with is a partially ordered set and has a cone structure with respect to addition and multiplication with nonnegative scalars described above. For with for each , is defined by .
Now, we recall that the interval imputation set of the interval game , is defined by and the interval core of the interval game , is defined by
Here, is the efficiency condition and , , are the stability conditions of the interval payoff vectors.
A game is called -balanced if for each balanced map we have . We recall that a map is called a balanced map  if . Here, , and for each , if and otherwise. It is easy to prove that if is -balanced then the border games and are balanced. A game is -balanced if and only if (in Alparslan Gök et al. [17, Theorem 3.1]). We denote by the class of -balanced interval games with player set .
Let , and . We say that dominates via coalition , denoted by , if(i) for all ;(ii).
For we denote by the set of those elements of which are dominated via . is called undominated if there does not exist and a coalition such that . The interval dominance core of consists of all undominated elements in . For a subset of is an interval stable set if the following conditions hold.(i)(Internal stability ) there does not exist such that dom or dom .(ii)(External stability ) for each there exist such that dom .
It holds for all and a stable set of .
3. Supermodular and Convex Interval Games
We say that a game is supermodular if
From formula (3.1) it follows that a game is supermodular if and only if its border games and are supermodular (convex). We introduce the notion of convex interval game and denote by the class of convex interval games with player set . We call a game convex if is supermodular and its length game is also supermodular. We straightforwardly obtain characterizations of games in terms of , and .
Proposition 3.1. Let and its related games . Then the following assertions hold. (i)A game is convex if and only if its length game and its border games , are convex.(ii)A game is convex if and only if its border game and the game are convex.
We notice that the nonempty set is a subcone of and traditional convex games can be embedded in a natural way in the class of convex interval games because if is convex then the corresponding game which is defined by for each is also convex. The next example shows that a supermodular interval game is not necessarily convex.
Example 3.2. Let be the two-person interval game with , and . Here, is supermodular, but . Hence, is not convex.
The next example shows that an interval game whose length game is supermodular is not necessarily convex.
Example 3.3. Let be the three-person interval game with for each , , and . Here, is not convex, but is supermodular, since , for each .
Interesting examples of convex interval games are unanimity interval games. First, we recall the definition of such games. Let with and let . The unanimity interval game based on and is defined by for each .
Clearly, is supermodular. The supermodularity of can be checked by considering the following case study:
For convex TU-games various characterizations are known. In the next theorem we give some characterizations of convex interval games inspired by Shapley .
Theorem 3.4. Let be such that is supermodular. Then, the following three assertions are equivalent: (i) is convex;(ii)for all with one has (iii)for all and such that one has
Proof. We show (i)(ii), (ii)(iii), (iii)(i).
Suppose that () holds. To prove () take with . From (3.1) with in the role of and in the role of we obtain (3.4) by noting that , . Hence, (i) implies (ii).
That (ii) implies (iii) is straightforward (take ).
Now, suppose that (iii) holds. To prove (i) take . Clearly, (3.1) holds if . In case , suppose that consists of the elements and let . Then, where the inequality follows from (iii).
Next we give as a motivating example a situation with an economic flavour leading to a convex interval game.
Example 3.5. Let and let be such that for each and . Suppose that , and are convex monotonic increasing functions. Then, we can construct a corresponding interval game such that for each . It is easy to show that is a convex interval game with the symmetry property for each with .
We can see as a production game if we interpret for as the interval reward which players in can produce by working together.
Before closing this section we indicate some other economic and OR situations related to supermodular and convex interval games. In case the parameters determining sequencing situations are not numbers but intervals, under certain conditions also convex interval games appear (Alparslan Gök et al. [17, 19]). Bankruptcy situations when the estate of the bankrupt firm and the claims are intervals, under restricting conditions, give rise in a natural way to supermodular interval games which are not necessarily convex .
4. The Shapley Value, the Weber Set and Population Monotonic Allocation Schemes
We call a game size monotonic if is monotonic, that is, for all with . For further use we denote by the class of size monotonic interval games with player set . We notice that size monotonic games may have an empty interval core. In this section we introduce interval marginal operators on the class of size monotonic interval games, define the Shapley value and the Weber set on this class of games, and study their properties on the class of convex interval games.
Denote by the set of permutations . Let . We introduce the notions of interval marginal operator corresponding to , denoted by , and of interval marginal vector of with respect to , denoted by . The marginal vector corresponds to a situation, where the players enter a room one by one in the order , and each player is given the marginal contribution he/she creates by entering. If we denote the set of predecessors of in by , where denotes the entrance number of player , then or . We notice that is an efficient interval payoff vector for each . For size monotonic games , is well defined for all with since . Now, we notice that for each the interval marginal vectors are defined for each , because the monotonicity of implies , which can be rewritten as . So, is defined for each and .
The following example illustrates that for interval games which are not size monotonic it might happen that some interval marginal vectors do not exist.
Example 4.1. Let be the interval game with , and . This game is not size monotonic. Note that is not defined because is undefined since .
A characterization of convex interval games with the aid of interval marginal vectors is given in the following theorem.
Theorem 4.2. Let . Then, the following assertions are equivalent: (i) is convex;(ii) is supermodular and for all .
Proof. (i)(ii) Let , let and take . Clearly, we have . To prove that we have to show that for , . Let with . Then,
where the inequality follows from Theorem 3.4. Further, by convexity of , is supermodular.
(ii)(i) From for all follows that and for all . Now, by the well-known characterization of classical convex games with the aid of marginal vectors we obtain that and are convex games. Since is convex by hypothesis, we obtain by Proposition 3.1(i) that is convex.
Now, we straightforwardly extend for size monotonic interval games two important solution concepts in cooperative game theory which are based on marginal worth vectors: the Weber set  and the Shapley value .
The interval Weber set on the class of size monotonic interval games is defined by for each . We notice that for traditional TU-games we have for all , while for interval games it might happen that (in case none of the interval marginal vectors is defined). Clearly, for all . Further, it is well-known that if and only if is convex. However, this result cannot be extended to convex interval games as we prove in the following proposition.
Proposition 4.3. Let . Then, .
Proof. By Theorem 4.2 we have for each . Now, we use the convexity of .
The following example shows that the inclusion in Proposition 4.3 might be strict.
Example 4.4. Let and let be defined by and . This game is convex. Further, and , belong to the interval core and . Notice that and there is no such that Hence, and .
In Section 5 we introduce a new notion of Weber set and show that the equality between the interval core and that Weber set still holds on the class of convex interval games.
The interval Shapley value is defined by
Since for each , by Proposition 4.3 we have for each . Without going into details we note here that the Shapley value on the class of size monotonic interval games, and consequently on , satisfies the properties of additivity, efficiency, symmetry and dummy player, which are straightforward generalizations of the corresponding properties for classical TU-games.
Proposition 4.5. Let . If is convex, then it is size monotonic.
Proof. Let . This assures that is supermodular which implies that is monotonic because for each with we have and from this inequality follows since . So, .
In the next two propositions we provide explicit expressions of the interval marginal vectors and of the interval Shapley value on .
Proposition 4.6. Let and let . Then, for all .
Proof. By definition, Now, we prove that . Since is a classical convex game we have for each where the inequality follows from the monotonicity of . So, for all , and
Since we obtain from Proposition 4.6 that for each , and for all .
Proposition 4.7. Let and let . Then, for all .
From Proposition 4.7 we obtain that for each we have for all .
In the sequel we introduce the notion of population monotonic interval allocation scheme (pmias) for totally -balanced interval games, which is a direct extension of pmas for classical cooperative games . A game is called totally -balanced if the game itself and all its subgames are -balanced.
We say that for a game a scheme with is a pmias of if(i) for all ;(ii) for all with and for each .
Notice that the total -balancedness of an interval game is a necessary condition for the existence of a pmias for that game. A sufficient condition is the convexity of the interval game. We notice that all subgames of a convex interval game are also convex. In what follows we focus on pmias on the class of convex interval games.
We say that for a game an imputation is pmias extendable if there exist a pmias such that for each .
Theorem 4.8. Let . Then, each element of is extendable to a pmias of .
Proof. Let . First, we show that for each , is extendable to a pmias. We know that the interval marginal operator is efficient for each . Then, for each , holds, where is the corresponding (convex) subgame.
Further, by convexity, for each , where and are the corresponding subgames.
Second, each is a convex combination of , , that is, with and . Now, since all are pmias extandable, we obtain that is pmias extendable as well.
From Theorem 4.8 we obtain that the total interval Shapley value, that is, the interval Shapley value applied to the game itself and all its subgames, generates a pmias for each convex interval game. We illustrate this in Example 4.9, where the calculations are based on Proposition 4.7.
Example 4.9. Let with , , and . It is easy to check that the interval Shapley value generates for this game the pmias depicted as
5. Interval Solutions Obtained with the Square Operator
Let and with . Then, we denote by the vector generated by the pair . Let . Then, we denote by the subset of defined by .
With the use of the operator, we give a procedure to extend classical multisolutions on to interval multisolutions on .
For a multisolution we define by for each .
Now, we focus on this procedure for multisolutions such as the core and the Weber set on interval games. We define the square interval core by for each . We notice that a necessary condition for the non-emptiness of the square interval core is the balancedness of the border games.
Proposition 5.1. Let . Then, .
Proof. if and only if and if and only if .
Since we obtain that for each .
We define the square Weber set by for each . Note that if .
The next two theorems are very interesting because they extend for interval games, with the square interval Weber set in the role of the Weber set, the well-known results in classical cooperative game theory that for each  and if and only if is convex .
Theorem 5.2. Let . Then, .
Proof. If the inclusion holds true. Suppose and let . Then, by Proposition 5.4, and , and, because for each , we obtain and . Hence, we obtain .
Theorem 5.3. Let . Then, the following assertions are equivalent: (i) is convex;(ii) is supermodular and .
Proof. By Proposition 3.1(i), is convex if and only if and are convex. Clearly, the convexity of is equivalent with its supermodularity. Further, and are convex if and only if and . These equalities are equivalent with . By Proposition 5.1 this is equivalent to .
Proposition 5.4. The interval core is an additive map.
Proof. The interval core is a superadditive solution concept for all interval games (Alparslan Gök et al. ). We need to show the subadditivity of the interval core. We have to prove that Note that for each . By definition of the square interval Weber set we have . By Theorem 5.3 we obtain
Finally, we define for each and notice that for convex interval games we have , where the second equality follows from the well-known result in the theory of TU-games that for convex games the core and the dominance core coincide, and the last equality follows from Proposition 5.1. From for each and for each we obtain for each . We notice that this inclusion might be strict (Alparslan Gök et al. , Example 4.1).
6. Concluding Remarks
In this paper we define and study convex interval games. We note that the combination of Theorems 3.4, 4.2 and 5.3 can be seen as an interval version in (Brânzei et al. , Theorem 96). In fact these theorems imply (Brânzei et al. , Theorem 96) for the embedded class of classical TU-games. Extensions to convex interval games of the characterizations of classical convex games where exactness of subgames and superadditivity of marginal (or remainder) games play a role (Biswas et al. , Brânzei et al.  and Martinez-Legaz [5, 6]) can be found in (Brânzei et al. ).
There are still many interesting open questions. For further research it is interesting to study whether one can extend to interval games the well-known result in the traditional cooperative game theory that the core of a convex game is the unique stable set . It is also interesting to find an axiomatization of the interval Shapley value on the class of convex interval games. An axiomatic characterization of the interval Shapley value on a special subclass of convex interval games can be found in Alparslan Gök et al. . Other topics for further research could be related to introducing new models in cooperative game theory by generalizing cooperative interval games. For example, the concepts and results on (convex) cooperative interval games could be extended to cooperative games in which the coalition values are ordered intervals of the form of an (infinite dimensional) ordered vector space. Such generalization could give more applications to the interval game theory. Also to establish relations between convex interval games and convex games in other existing models of cooperative games could be interesting. One candidate for such study could be convex games in cooperative set game theory .
Alparslan Gök acknowledges the support of TUBITAK (Turkish Scientific and Technical Research Council) and hospitality of Department of Mathematics, University of Genoa, Italy. Financial support from the Government of Spain and FEDER under project MTM2008-06778-C02-01 is gratefully acknowledged by R. Branzei and S. Tijs. The authors thank Elena Yanovskaya for her valuable comments. The authors greatfully acknowledge two anonymous referees.
- L. S. Shapley, “Cores of convex games,” International Journal of Game Theory, vol. 1, no. 1, pp. 11–26, 1971.
- T. Driessen, Cooperative Games, Solutions and Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1988.
- A. K. Biswas, T. Parthasarathy, J. A. M. Potters, and M. Voorneveld, “Large cores and exactness,” Games and Economic Behavior, vol. 28, no. 1, pp. 1–12, 1999.
- R. Brânzei, D. Dimitrov, and S. Tijs, Models in Cooperative Game Theory, Game Theory and Mathematical Methods, Springer, Berlin, Germany, 2008.
- J. E. Martinez-Legaz, “Two remarks on totally balanced games,” Tech. Rep. TR#317, Department of Mathematics, The University of Texas, Arlington, Tex, USA, 1997.
- J. E. Martínez-Legaz, “Some characterizations of convex games,” in Recent Advances in Optimization, A. Seeger, Ed., vol. 563 of Lecture Notes in Economics and Mathematical Systems, pp. 293–303, Springer, Berlin, Germany, 2006.
- H. Moulin, Axioms of Cooperative Decision Making, vol. 15 of Econometric Society Monographs, Cambridge University Press, Cambridge, UK, 1988.
- I. Curiel, G. Pederzoli, and S. Tijs, “Sequencing games,” European Journal of Operational Research, vol. 40, no. 3, pp. 344–351, 1989.
- B. O'Neill, “A problem of rights arbitration from the Talmud,” Mathematical Social Sciences, vol. 2, no. 4, pp. 345–371, 1982.
- R. J. Aumann and M. Maschler, “Game theoretic analysis of a bankruptcy problem from the Talmud,” Journal of Economic Theory, vol. 36, no. 2, pp. 195–213, 1985.
- I. J. Curiel, M. Maschler, and S. Tijs, “Bankruptcy games,” Zeitschrift für Operations Research. Serie A. Serie B, vol. 31, no. 5, pp. A143–A159, 1987.
- A. Charnes and D. Granot, “Prior solutions: extensions of convex nucleus solutions to chance-constrained games,” in Proceedings of the Computer Science and Statistics Seventh Symposium at Iowa State University, pp. 323–332, Ames, Iowa, USA, October 1973.
- J. Suijs, P. Borm, A. De Waegenaere, and S. Tijs, “Cooperative games with stochastic payoffs,” European Journal of Operational Research, vol. 113, no. 1, pp. 193–205, 1999.
- J. Timmer, P. Borm, and S. Tijs, “Convexity in stochastic cooperative situations,” International Game Theory Review, vol. 7, no. 1, pp. 25–42, 2005.
- S. Z. Alparslan Gök, R. Brânzei, and S. Tijs, “The interval Shapley value: an axiomatization,” no. 130, Institute of Applied Mathematics, METU, Ankara, Turkey, 2009, to appear in Central European Journal of Operations Research (CEJOR).
- R. Brânzei, S. Tijs, and S. Z. Alparslan Gök, “How to handle interval solutions for cooperative interval games,” preprint no. 110, Institute of Applied Mathematics, METU, 2008.
- S. Z. Alparslan Gök, R. Brânzei, and S. Tijs, “Cores and stable sets for interval-valued games,” Tech. Rep. 2008-17, Center for Economic Research, Tilburg University, Tilburg, The Netherlands, 2008, preprint no. 90.
- S. Tijs, Introduction to Game Theory, vol. 23 of Texts and Readings in Mathematics, Hindustan Book Agency, New Delhi, India, 2003.
- S. Z. Alparslan Gök, R. Brânzei, V. Fragnelli, and S. Tijs, “Sequencing interval situations and related games,” Tech. Rep. 2008-63, Center for Economic Research, Tilburg University, Tilburg, The Netherlands, 2008, preprint no. 113.
- R. Brânzei and S. Z. Alparslan Gök, “Bankruptcy problems with interval uncertainty,” Economics Bulletin, vol. 3, no. 56, pp. 1–10, 2008.
- R. J. Weber, “Probabilistic values for games,” in The Shapley Value: Essays in Honour of Lloyd S. Shapley, A. E. Roth, Ed., pp. 101–119, Cambridge University Press, Cambridge, UK, 1988.
- L. S. Shapley, “A value for -person games,” in Contributions to the Theory of Games, Vol. 2, Annals of Mathematics Studies, no. 28, pp. 307–317, Princeton University Press, Princeton, NJ, USA, 1953.
- Y. Sprumont, “Population monotonic allocation schemes for cooperative games with transferable utility,” Games and Economic Behavior, vol. 2, no. 4, pp. 378–394, 1990.
- T. Ichiishi, “Super-modularity: applications to convex games and to the greedy algorithm for LP,” Journal of Economic Theory, vol. 25, no. 2, pp. 283–286, 1981.
- I. Drăgan, J. Potters, and S. Tijs, “Superadditivity for solutions of coalitional games,” Libertas Mathematica, vol. 9, pp. 101–110, 1989.
- R. Brânzei, D. Dimitrov, and S. Tijs, “A new characterization of convex games,” Tech. Rep. 109, Center for Economic Research, Tilburg University, Tilburg, The Netherlands, 2004.
- R. Brânzei, S. Tijs, and S. Z. Alparslan Gök, “Some characterizations of convex interval games,” AUCO Czech Economic Review, vol. 2, no. 3, pp. 219–226, 2008.
- H. Sun, Contributions to set game theory, Ph.D. thesis, University of Twente, Twente University Press, Enschede, The Netherlands, 2003.
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