We apply ideas from abstract argumentation theory to study cooperative game theory. Building on Dung's results in his seminal paper, we further the correspondence between Dung's four argumentation semantics and solution concepts in cooperative game theory by showing that complete extensions (the grounded extension) correspond to Roth's subsolutions (respectively, the supercore). We then investigate the relationship between well-founded argumentation frameworks and convex games, where in each case the semantics (respectively, solution concepts) coincide; we prove that three-player convex games do not in general have well-founded argumentation frameworks.
Argumentation theory is the branch of artificial intelligence (AI) that is concerned with the rational and transparent resolution of disagreements between arguments (e.g. [18]). Abstract argumentation theory, as articulated in Dung’s seminal paper [7], abstracts away from the contents of the arguments and the nature of their disagreements. The resulting directed graph (digraph) representation of arguments (nodes) and their disagreements (directed edges), called an abstract argumentation framework (AF), is simple yet powerful enough to resolve these disagreements and determine the sets of winning arguments.
Dung demonstrated the “correctness” of abstract argumentation by showing how abstract argumentation “can be used to investigate the logical structure of the solutions to many practical problems” [7, Section 3]. Specifically, he investigated two examples of problems from microeconomics (e.g. [14]): cooperative game theory and matching theory. In each case, Dung showed how an appropriate AF can represent a given cooperative game or a given instance of the stable marriage problem, and that the sets of winning arguments in such AFs correspond to meaningful solutions in both of these domains.
In this paper, we further demonstrate the “correctness” of abstract argumentation theory by investigating its relationship with cooperative game theory. Cooperative game theory (e.g. [6]) is the branch of game theory (e.g. [27]) Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0). that studies how normatively rational agents may cooperate in order to possibly earn more payo↵ than they would do as individuals. Cooperative game theory abstracts away from individual agents’ strategies in such games for a simpler and “high level” view of their interactions.1 Each cooperative game consists of finitely many agents that can cooperate as coalitions, and each coalition earns some payo↵ as measured by its value. Given certain standard assumptions on the values of coalitions, all agents should cooperate as a single coalition, called the grand coalition. However, this still leaves open the question of how the payo↵ obtained by the grand coalition (which is already maximised) should be distributed among the individual agents, such that no agent should want to defect from the grand coalition. Historically, the first solution concept for cooperative games that captures this idea is the Von Neumann-Morgenstern (vNM) stable set, where each such set consists of such payo↵ distributions interpreted as an “acceptable standard of behaviour” [27]. Subsequently, Dung showed that each possible payo↵ distribution can be interpreted as an argument in an AF. Payo↵ distributions “disagree” when agents can defect because they can earn strictly more. This argumentative interpretation of cooperative games allowed Dung to demonstrate that the stable extensions of the AF of each cooperative game correspond exactly to the game’s vNM stable sets [7, Theorem 37].
However, just like that stable extensions of an AF may not exist, vNM stable sets for cooperative games also may not exist [11,12]. As a result, alternative solution concepts have been proposed. For example, Dung proposed that sets of payo↵ distributions that form preferred extensions could serve as an alternative solution concept, because preferred extensions of AFs always exist, and therefore this is well-defined for all cooperative games. Other possible alternative solution concepts from cooperative game theory include the core [8], the subsolution and the supercore [21]. Dung showed that the core corresponds to the set of unattacked arguments of the game’s AF [7, Theorem 38]. This paper’s first contribution is to finish the correspondence between Dung’s four argumentation semantics and the various solution concepts from cooperative game theory by proving that the complete extensions (respectively, the grounded extension) of the AF correspond(s) to the subsolutions (respectively, the supercore) of the cooperative game. These correspondences allow us to characterise when the supercore of a cooperative game is non-empty via the Bondareva-Shapley theorem [3,24]: exactly when the game is balanced (see Section 3.2).
Shapley investigated a special class of cooperative games called convex games, which capture the intuition that agents have more incentive to join larger coalitions; the key property of each such game is that its core is its unique stable set [25, Theorem 8]. In abstract argumentation theory, a similar result by Dung states that if an AF is well-founded, its grounded extension is its unique stable set [7, Theorem 30]. Given these similar results, we would like to know whether convex games always give rise to well-founded AFs. Our second contribution in 1 The nature of this cooperation is exogenous to the theory, but can be interpreted as groups of agents forming binding contracts. See, e.g. [6, page 7]. this paper is a negative answer to this question through a three-player counterexample.
The rest of this paper is structured as follows. In Section 2, we review abstract argumentation theory and cooperative game theory. In Section 3, we complete the correspondences between Dung’s four argumentation semantics with solution concepts in cooperative games and study the properties using well-known results from argumentation. In Section 4, we recap convex games and well-founded AFs, and give a counter-example of a three-player convex game that gives rise to a non-well-founded AF. In Section 5, we compare our results with related work in argumentation theory and game theory, and conclude with future work. 2
Notation: Let X, Y, Z be sets. P (X) is the power set of X and |X| is the cardinality of X. N is the set of natural numbers (including 0) and R is the set of + real numbers. Further, R+ (respectively, R0 ) is the set of positive (respectively, non-negative) real numbers. For n 2N, the n-fold Cartesian power of X is Xn. If f : X ! Y and g : Y ! Z are appropriate functions, then g f : X ! Z is the composition of g then f , and functional composition is denoted with . If X is a set, then for a function f : X ! R, f 0 abbreviates (8 x 2X) f (x) 0. 2.1 An (abstract) argumentation framework2 (AF) is a directed graph (digraph) hA, Ri where A is the set of arguments and R ✓ A2 denotes the attack relation, where for a, b 2A, (a, b) 2R, abbreviated by R(a, b), denotes that a disagrees with b. Let S ✓ A be a set of arguments for the remainder of this subsection. Define S+ := {b 2A (9 a 2S) R(a, b)} to be the set of all arguments attacked by S. The neutrality function is n : P (A) ! P (A) where n(S) := A S+ denotes the set of arguments not attacked by S, i.e. the set of arguments that S is neutral towards. We say S is conflict-free (cf) i↵ S ✓ n(S). Similar to S+, let S := {b 2A (9 a 2S) R(b, a)}, and for a 2A, a± := {a}±. The defence function is d : P (A) ! P (A) where a 2 d(S) i↵ a ✓ S+.3 It can be shown that d = n2 := n n [7, Lemma 45]. Let U ✓ A denote the set of unattacked arguments. It can be shown that U = d (? ). We say S is self-defending (sd) i↵ S ✓ d(S). Further, S is admissible i↵ it is cf and sd.
To determine which sets of arguments are justifiable we say S is a complete extension i↵ S is admissible and d(S) ✓ S.4 Further, S is a preferred extension i↵ it is a ✓ -maximal complete extension [28, Theorem 7.11], S is a stable extension i↵ n(S) = S and S is the grounded extension i↵ it is 2 When defining our terms in this paper, any words in between brackets may be omitted when using the term, e.g. in this case, the terms “argumentation framework” and “abstract argumentation framework” are interchangeable. 3 In [7, Section 2.2] this is called the characteristic function. 4 i.e. if one can defend a proposition then one is obliged to accept it as justifiable. the ✓ -least complete extension. These four semantics are collectively called the Dung semantics, and each defines sets of winning arguments given hA, Ri. 2.2
We review the basics of cooperative game theory (see, e.g. [6]). Let m 2N and N = {1, 2, 3, . . . , m} be our set of players or agents.5 We assume m 1. A coalition C is any subset of N . The empty coalition is ? and the grand coalition is N .
Example 1. Consider the set of players N = {1, 2, 3}, where m = 3. In our examples, agents 1, 2 and 3 are respectively named Josh, David and Peter. If all three decide to work together, then they form the grand coalition N . If only Josh and David work together and Peter works alone, then the resulting coalitions are, respectively, {1, 2} and {3}.
A valuation function is a function v : P (N ) ! R such that v (? ) = 0. The number v(C) can be thought of as the coalition’s payo↵ as a result of the agents’ coordination of strategies; this payo↵ is in arbitrary units. Example 2. (Example 1 continued) If Josh, David and Peter work together and earn 10 units of payo↵, then v(N ) = 10. If Josh and Peter will lose 50 units of payo↵ if they work together, then v({1, 3}) = 50.
Given N and v, with m := |N |, a (cooperative) (m-player) game (in normal form) is the pair G := hN, vi. The following five properties are standard for v. We say v is non-negative i↵ v 0; this excludes valuation functions such as the one in Example 2. We say v is monotonic i↵ for all C, C0 ✓ N , if C ✓ C0, then v(C) v(C0). We say v is constant-sum i↵ ( 8 C ✓ N ) v(C)+v(N C) = v(N ). We say v is super-additive i↵ for all C, C0 ✓ N , if C and C0 are disjoint then v (C [ C0) v (C) + v (C0). We say v is inessential i↵ Pkm=1 v ({k}) = v (N ); inessential means there is no incentive to cooperate.
It is easy to show that if v is non-negative and super-additive, then v is monotonic, while the converse is not true (e.g. [5, Example 1]). For the rest of the games in this paper, we will assume v is non-negative, super-additive and essential (i.e. not inessential).6 Example 3. (Example 1 continued) Suppose if Josh, David or Peter earn no payo↵ if they work as individuals, but if any two of them work together they earn 10 units of payo↵, and if all three work together they earn 20 units of payo↵. This v is non-negative, super-additive, not constant-sum, and essential. 5 We use “m” instead of the more traditional “n” for the number of players to avoid confusion with the neutrality function n : P (A) ! P (A) in argumentation. 6 When combined with super-additivity, it follows that there are two disjoint coalitions C and C0 such that v(C [ C0) > v(C) + v(C0).
An outcome of a game is the pair (CS, x), where CS is a partition of N called a coalition structure, and x 2Rm is a payo↵ vector that distributes the value of each coalition to the players in that coalition. As we have assumed that v is non-negative, super-additive and essential, then v(N ) has the (strictly) largest payo↵ among all coalitions by monotonicity. Agents are rational and want to maximise their payo↵, and so they should seek to form the grand coalition. Therefore, we restrict our attention to outcomes where CS = {N }.
How should the amount v(N ) be distributed among the m players? In this
paper, we consider transferable utility games, which allow for the distribution
of v(N ) arbitrarily to the m players, e.g. by interpreting v(N ) as money, which
all players should desire. This leads to the following properties of payo↵ vectors
x. We say x := (x1, x2, . . . , xm) is feasible i↵ Pk2 N xk v(N ), ecient i↵
Pk2 N xk = v(N ) and individually rational i↵ ( 8 k 2N ) v ({k}) xk. We
call a payo↵ vector x an imputation i↵ x is ecient and individually rational;
intuitively, imputations distribute all the money to every agent without waste,
such that every agent earns at least as much as when they work alone. Following
[7], we denote the set of imputations for a game G with IM P (G), or just
IM P if it is clear which cooperative game G we are referring to.
Example 4. (Example 3 continued) As v(N ) = 20 (which we now measure in
dollars ($), as an example of transferable utility), a feasible payo↵ vector is (
The solution concepts of cooperative games that we will consider are
concerned with whether coalitions of agents are incentivised to defect from the grand
coalition.7 Given a game G = hN, vi, let C be a coalition and x, y 2IM P . We
say x dominates y via C, denoted x ! C y, i↵ (
It is easy to see from the definition of ! C that it is irreflexive, acyclic (and hence asymmetric), and transitive. Further, some important special cases include ! N = ? and (8 k 2N ) ! {k}= ? , i.e. it does not make sense for the grand coalition to defect to the grand coalition, and individual players cannot defect from N (the latter due to individual rationality). Also, ! ? = IM P 2, the total binary relation on IM P . We say imputation x dominates imputation y, denoted 7 See Section 5 for a brief mention of other solution concepts. x ! y, i↵ ( 9 C ✓ N ) [C 6= ? , x ! C y]. This is a well-defined irreflexive binary relation on IM P . However, it can be shown that this is not generally transitive, complete or acyclic (e.g. [26, Chapter 4]). Therefore, each cooperative game gives rise to an associated directed graph hIM P, !i , called an abstract game [21].
Let I ✓ IM P be a set of imputations. Following [27], we say I is internally stable i↵ no imputation in I dominates another in I. Further, I is externally stable i↵ every imputation not belonging to I is dominated by an imputation from I. A (von Neumann-Morgenstern) stable set is a subset of IM P that is both internally and externally stable. Taken together, a stable set of imputations contains the distributions of the amount of money v(N ) to the set of m players that are socially acceptable [27].
We now recapitulate a simplification to coalitional games that does not lose
generality (e.g. [26, Chapter 4]). Let G := hN, vi and G0 := hN, v0i be two
games on the same set of players. We say G and G0 are strategically
equivalent i↵ ( 9 K 2R+) (9 c 2Rm) (8 C ✓ N ) v0(C) = Kv(C) + Pk2 C ck, for c :=
(c1, . . . , cm), and we denote this with G ⇠= G0; this is an equivalence relation
between games. Further, the function f : IM P (G) ! IM P (G0) with rule f (x) :=
Kx + c is a digraph isomorphism from hIM P (G), !i to hIM P (G0), ! 0i, where
! 0 is the corresponding domination relation on IM P (G0). It follows that if S ✓
IM P (G) is a stable set of G, then its image set f (S) ✓ IM P (G0) is also a stable
set of G0. By setting K1 = v(N ) Pk2 N v ({k}) and (8 k 2N ) ck = Kv ({k})
for this K, then we can transform G = hN, vi to its (
This means IM P G(
Proof. Let ⇠= denote bijection between sets and ,! denote an injective embedding
between sets, then we have R ⇠= [0, 1] ,! IM P G(
k=3 xk normalises the output to be on the simplex. By the
CantorSchr¨oder-Bernstein theorem [10, Theorem 3.2], IM P G(
In this section we recap [7, Section 3.1]. We now understand how an abstract game hIM P, !i , which is also an uncountably infinite digraph, arises from a game G. Dung interprets hIM P, !i as an abstract AF, where each argument is an argument for a given payo↵ distribution among the agents, and each attack denotes the possibility for a subset of agents to defect from the grand coalition. Corollary 1 states that such an AF has uncountably infinite arguments, but this is not a problem because Dung’s argumentation semantics and their properties hold for AFs of arbitrary cardinalities [2,7,28]. Dung then proved that various methods of resolving conflicts in hIM P, !i as an AF correspond to meaningful solution concepts of G. The following result is straightforward to show.
As stable sets may not exist for AFs, and in particular there are games without
stable sets [11,12], Dung proposed that preferred extensions, as they always exist
[7, Theorem 11(
Further, another important solution concept in cooperative game theory is the core [8]. Formally, the core of a cooperative game G is the set of imputations x satisfying the system of inequalities (8 C ✓ N ) Pk2 C xk v(C). Intuitively, the core is the set of imputations where each agent is receiving at least as much payo↵ even if a subset of such agents were to defect to a new coalition (regardless of how the payo↵ is shared within that coalition). Therefore, no agent has an incentive to defect. It can be shown that the core is the subset of imputations that are not dominated by any other imputation. It follows that:
game. If we view hIM P, !i as an AF, then its set of unattacked arguments corresponds exactly to the core.
From argumentation theory, we thus conclude the well-known result from cooperative game theory that stable sets, if they exist, always contain the core. Example 7. (Example 6 continued) We have N = {1, 2, 3} and v(C) = 0 if |C| 1 else v(C) = 12 , and v(N ) = 1. The eight possible coalitions C ✓ N give rise to the three inequalities x1 +x2 12 , x2 +x3 12 and x3 +x1 21 . Therefore, the core consists of all imputations x = (x1, x2, x3) whose components satisfy these three inequalities, for example, 13 , 13 , 31 and 12 , 12 , 0 . 3
Given that stable extensions correspond to stable sets, and the set of unattacked arguments corresponds to the core, do the complete extensions (including the preferred extensions) and the grounded extension also correspond to solution concepts in cooperative games? We now show that the answer is yes. 3.1
As preferred extensions are a subset of complete extensions, it is natural to ask whether complete extensions more generally correspond to solution concepts in cooperative games. In [21], motivated by the general lack of existence of stable sets [11,12], Roth considered abstract games arising from cooperative games (in his notation) hX, >i, where X is the set of the game’s outcomes and >✓ X2 is an abstract domination relation. Let u : P (X) ! P (X) be the function u(S) := X S+,8 where in this case S+ := {y 2X (9 x 2S) x > y}. Roth then defined the subsolution of such an abstract game as follows.
is a set S ✓ X such that S ✓ u(S) and S = u2(S) := u u(S).
Immediately we can see that by interpreting hX, >i as an abstract argumentation framework, subsolutions are precisely the complete extensions. Theorem 3. Let G = hN, vi be a game and hIM P, !i be its abstract game.
are precisely the subsolutions.
Proof. S ✓ IM P is a complete extension of hIM P, !i i↵ S ✓ n(S) and S = d(S), where n is the neutrality function n(S) = IM P S+, but as n2 = d [7, Lemma 45], this is equivalent to saying that S is a subsolution, by identifying X = IM P , > = ! and u = n.
Roth has shown that every abstract game, and hence every cooperative game, has a subsolution [21, Theorem 1].9 The ✓ -maximal subsolutions of hIM P, !i are exactly the preferred extensions of hIM P, !i when seen as an abstract argumentation framework. Roth also showed that stable sets are subsolutions, and hence subsolutions generalise stable sets. This is well known in argumentation theory as stable extensions are also complete extensions. We can also apply further results from abstract argumentation theory to infer more properties of subsolutions. For instance, the core is contained in all subsolutions. Corollary 2. Every subsolution is a superset of the core.
Proof. Interpreting hIM P, !i as an argumentation framework, the core corresponds to the set of unattacked arguments, which is d (? ), where d is the defence function (Section 2.1). As a subsolution S is a complete extension, we know that d(S) = S. As d is ✓ -monotonic and ? ✓ S, we have d (? ) ✓ S.
Further, subsolutions have a specific lattice-theoretic structure: Theorem 4. The family of subsolutions of an abstract game form a complete semilattice that is also directed-complete.
Proof. This follows from [7, Theorem 25(
In [21, Example 5.1], Roth showed that subsolutions are in general not unique for abstract games; the supercore is one “natural” way of selecting a unique subsolution.
Definition 2. [21, Section 3] The supercore of an abstract game is the intersection of all its subsolutions.
Immediately we can conclude the following.
Theorem 5. The supercore of an abstract game is its grounded extension when viewed as an argumentation framework.
Proof. This follows from e.g. [7, Theorem 25(
Therefore, for arbitrary cooperative games, if stable sets exist, they can serve as the possible sets of recommended payo↵ distributions, i.e. the “acceptable standards of behaviour” [27]. If they do not exist, then we may use subsolutions instead. If we desire a unique subsolution, we can use the supercore.
However, Roth noted that the supercore is empty i↵ the core is empty. This corresponds to the well-known result in argumentation theory that the set of unattacked arguments is empty i↵ the grounded extension is empty (e.g. [28, Corollary 6.9]). We can therefore completely characterise cooperative games with non-empty supercores using the Bondareva-Shapley theorem [3,24]. Let G = hN, vi be a cooperative game. A function f : P (N ) ! [0, 1] is balanced i↵ (8 k 2N ) PS✓ N{ k} f (S [ { k}) = 1, i.e. if for every player the value under f of all coalitions containing that player sum to one. We call G balanced i↵ for every balanced function f , PS✓ N f (S) v(S) v(N ). Intuitively, each player k allocates a fraction of his or her time f (S [ { k}) to the coalition v(S), and that coalition receives a value proportional to that agent’s time spent there. Theorem 6. (Bondareva-Shapley) A game has a non-empty core i↵ it is balanced.
It follows that: Corollary 3. A game has a non-empty supercore i↵ it is balanced. Proof. The core of a game is non-empty i↵ its supercore is non-empty, i↵ it is balanced, by the Bondareva-Shapley theorem (Theorem 6).
From both argumentation theory and abstract games in cooperative game theory, we have the following well-known containment relations between the solution concepts in cooperative games.
lutions, which are subsolutions, and the supercore is also a subsolution. Proof. Immediate, because in argumentation theory, stable extensions are preferred, which are complete. Further, the grounded extension is also complete. We summarise the correspondences between Dung’s argumentation semantics and the solution concepts of cooperative games in the Table 1, including the results presented in this paper. Further, we have shown that the supercore exists, and is unique and non-empty, i↵ the game is balanced (Corollary 3). Finally, the family of subsolutions form a complete semilattice that is also directed-complete (Theorem 4). These correspondences allow us to apply ideas from argumentation to cooperative game theory, as we will in the next section. 4
Having shown how abstract argumentation can be used in cooperative game theory, we now investigate the relationship between well-founded AFs and convex games, because in both cases the semantics (respectively, solution concepts) coincide. 4.1
An important type of cooperative game is that of a convex game [25]. Formally, G = hN, vi is convex i↵ ( 8 C, C0 ✓ N ) v (C [ C0) + v (C \ C0) v(C) + v(C0). Clearly, convex games are super-additive. This property is equivalent to [6, Proposition 2.8]: for all S, T ✓ N , if T ✓ S, then (8 k 2N S) v (T [ { k}) v(T ) v (S [ { k}) v(S). Intuitively, this means as a coalition grows in size, there is more incentive for agents not already in the coalition to join. Shapley calls this a “band-wagon” e↵ect [25]. Further, if G ⇠= G0 and G is convex, then G0 is also convex.10 The key property of convex games that we focus on is: Theorem 8. [25, Theorem 8] The core of a convex game is stable. From our results in Section 3, we can immediately show the following. Corollary 4. If G is convex, then the set of unattacked arguments of its AF hIM P, !i is the only stable extension.
Proof. Immediate from [7, Theorems 37 and 38].
Convex games exhibit a coincidence of the solution concepts so far considered. Corollary 5. If G is convex, then its core is also its supercore, which is also its unique subsolution.
Proof. If G is convex, then viewing its abstract game hIM P, !i as an AF, its set U of unattacked arguments is the unique stable extension [7, Theorem 37]. But if U is stable, then U is also the grounded extension - else the grounded extension, as a superset of U , is not conflict-free. Thus U is also the supercore of G (Theorem 5). As U is unique, it is the unique subsolution of G. 4.2
Now recall from abstract argumentation theory we have a sucient condition for an AF to have all four of Dung’s argumentation semantics coincide.
sequence11 ai i2 N such that (8 i 2N) R(ai+1, ai) [7, Definition 29], then its grounded extension is the unique stable, complete and preferred extension. Given that the consequences of Theorem 9 and Corollaries 4 and 5 are the same, one may ask whether the AFs arising from convex games is in some sense “stronger” than well-founded AFs. Indeed, convex games refer to unattacked arguments U , while well-founded AFs refer to the grounded extension, a superset of U . Could it be that convex games always give rise to well-founded AFs? We answer this question in the following section. 10 This can be shown by writing out the definition of convex for the coalitions in G0 given the definition of strategic equivalence in Section 2.2, and then applying the inclusion-exclusion principle for sets. 11 Let X be a set, an X-sequence is a function f : N ! X written as xi i2 N, so (8 i 2 N) xi 2 X. 4.3
To make this problem more tractable, we specialise to three-player convex games.
We will assume the following canonical form without loss of generality:
Theorem 10. [13, Slide 19] Every essential three-player game that is not
necessarily constant-sum is strategically equivalent to the (
Corollary 6. Every essential three-player convex game G that is not necessarily
constant-sum is strategically equivalent to the (
Proof. (Sketch) () ) If G is convex and G ⇠= ⌦N, v(
The next result shows that three-player convex games do not always give rise to well-founded AFs.
Theorem 11. The game in Examples 1 to 8 is an essential, non-constant-sum three-player convex game whose AF is not well-founded.
Proof. Example 1 states this game is three-player, Example 8 states that this game is convex, and Example 3 states that this game is essential and not constant-sum. Let hIM P, !i denote the abstract game from our examples, now seen as an AF. Consider the following IM P -sequence xi i2 N where xi := xi1, xi2, xi3 =
Clearly, this is a well-defined imputation for all i 2N, because the three components sum to 1 and each component is non-negative.
We now show that (8 i 2N) xi+1 ! xi, and hence hIM P, !i is not a
wellfounded AF. We only need domination with respect to the coalition {1, 2}. For
any i 2N, we have xi+1 ! {1,2} xi because (
2
(
Therefore, not all convex games give rise to well-founded AFs. This result clarifies that the coincidence of solution concepts due to convexity and the coincidence of argumentation semantics due to well-foundedness are of di↵erent natures. 5
In this paper, we have proved that for the uncountably infinite AFs that arise from cooperative games, the complete extensions (respectively the grounded extension) correspond to that game’s subsolutions (respectively, supercore), by Theorem 3 (respectively, Theorem 5). This allows for more results from argumentation theory to be applied to cooperative game theory, for example, the lattice-theoretic structure of the complete extensions (Theorem 4) or when the supercore is empty (Corollary 3). Both convex games and well-founded AFs result in a coincidence of, respectively, solution concepts and argumentation semantics, but convex games do not necessarily give rise to well-founded AFs (Theorem 11). To the best of our knowledge, these contributions are original.12 These e↵orts strengthen the “correctness” of abstract argumentation by demonstrating its ability to reason about problems of societal or strategic concern.
Our first contribution completes the correspondence between Dung’s original four argumentation semantics with solution concepts in cooperative games. We can use this correspondence in future work to investigate the relationship between further abstract argumentation semantics not mentioned in [7] (e.g. those mentioned in [2]) and solution concepts in cooperative game theory. We can also investigate continuum AFs more generally, where the set of arguments A ⇠= R, as cooperative game theory provides a natural motivation for them.
Our second contribution can be developed further. Intuitively, convex games do not have to be well-founded due to the continuum nature of the simplex and the order-theoretic underpinnings of domination, and hence one may divide 12 The authors have checked all papers citing [21], which is the first paper defining the supercore and subsolutions from the abstract game of a cooperative game, and found no papers on argumentation theory among them. extra payo↵s into smaller and smaller units. But then one might justifiably ask whether it still makes sense for the first two agents to be be sensitive to infinitesimal improvements in their payo↵s when i is very large, and thus still maintain their desire to defect. We can attempt to answer this in future work.
There has been much work investigating the relationship between argumentation and game theory more generally. For example, Rahwan and Larson have used argumentation theory to analyse non-cooperative games [17] and study mechanism design [16]. Matt and Toni, and Baroni et al. have applied von Neumann’s minimax theorem [27] to measure argument strength [1,15]. Riveret et al. investigate a dialogical setting of argumentation by representing the dialogue in game-theoretic terms, allowing them to determine optimal strategies for the participants [19]. Roth et al. articulate a prescriptive model of strategic dialogue by using concepts from game theory [22]. Our paper is distinct from these as it applies ideas from argumentation theory to investigate cooperative games.
This paper builds on results from Dung’s seminal paper [7, Section 3.1]. There have been works applying ideas from cooperative game theory to non-monotonic reasoning and argumentation, specifically the Shapley value [23]. For example, Hunter and Konieczny have used the Shapley value to measure inconsistency of a knowledge base [9]. Bonzon et al. have used the Shapley value to measure the relevance of arguments in the context of multiagent debate [4]. The Shapley value, as a solution concept, is concerned with measuring the payo↵ to each agent given their marginal contribution in each coalition, averaged over all coalitions; we do not consider it here in this paper as we are concerned with solution concepts to do with defection rather than marginal contributions. Future work can build on the correspondences in this paper by considering which further solution concepts from cooperative games may be relevant for argumentation.