pith. sign in

arxiv: 1907.09113 · v1 · pith:DE4YU5VGnew · submitted 2019-07-22 · 💻 cs.MA · cs.AI· cs.LO

Aggregation in Value-Based Argumentation Frameworks

Grzegorz Lisowski (University of Warwick) , Sylvie Doutre (University of Toulouse) , Umberto Grandi (University of Toulouse) This is my paper

Pith reviewed 2026-05-24 18:00 UTC · model grok-4.3

classification 💻 cs.MA cs.AIcs.LO
keywords value-based argumentation frameworksargument aggregationpreference aggregationgraph aggregationmulti-agent systemscollective decision makingattack relations
0
0 comments X

The pith

When agents disagree on value orderings in argumentation frameworks, they face a choice between aggregating the orderings, the attack graphs, or rankings extracted from the graphs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Value-based argumentation adds value labels to arguments and audience orderings on those values to justify attacks. Multiple agents can hold different value orderings over the same arguments, creating a need for a shared collective view. The paper compares two direct aggregation routes: merging the value orderings first or merging the resulting attack relations. It also introduces extracting numerical or preference rankings from each agent's attack graph and then aggregating those rankings. The comparison uses tools from preference aggregation and graph aggregation to map out what each route preserves or loses.

Core claim

The paper establishes that value-level aggregation and graph-level aggregation are two equally justifiable but distinct routes to collective views, and that a third route—extracting rankings from the attack relations and aggregating those—offers a meaningfully separate alternative with its own strengths and limitations.

What carries the argument

The three aggregation procedures: value-order aggregation via preference aggregation techniques, attack-relation aggregation via graph aggregation techniques, and extraction of rankings from attack relations followed by aggregation of those rankings.

If this is right

  • Value-level aggregation keeps the justification structure tied to shared values but may alter perceived attacks.
  • Graph-level aggregation directly resolves conflicts in attacks but discards the value-based justifications.
  • Ranking extraction allows aggregation without committing to either full value merging or full graph merging.
  • Different methods can produce different collective acceptance sets from the same set of individual inputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The ranking-extraction method could serve as a computationally lighter proxy when full value or graph aggregation is expensive.
  • These procedures might be applied to other structured argumentation systems that separate justification from attack relations.
  • Empirical tests on debate transcripts could show which method better matches human judgments of fair collective outcomes.
  • Hybrid procedures that switch between the three routes depending on the profile might reduce the occurrence of unstable collective views.

Load-bearing premise

The premise that value-level and graph-level aggregation are equally justifiable alternatives and that deriving rankings from attack relations yields a meaningfully distinct and viable third strategy.

What would settle it

A set of agent profiles in which value-level aggregation, graph-level aggregation, and ranking-extraction aggregation all produce identical collective attack relations and acceptance sets for every possible input.

Figures

Figures reproduced from arXiv: 1907.09113 by Grzegorz Lisowski (University of Warwick), Sylvie Doutre (University of Toulouse), Umberto Grandi (University of Toulouse).

Figure 1
Figure 1. Figure 1: Value-based argumentation framework VAF of Examp [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Defeat graphs based on (a) Expert 1’s (EV [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Preferences over values are aggregated into a coll [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Collective defeat graph for the panel P, under the Borda rule. 3We refer to ∑ Pi∈P rankPi (vi) as to the score of vi [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Individual graphs are aggregated into a collectiv [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The individual attack graphs AFi are provided a justification order over values ≻i , which are then aggregated using a preference aggregation rule F, and the collective ordering ≻coll so obtained induces a collective attack graph AFcoll justified by it. Let us define the proposed method formally. Note that to ensure the functionality of the proposed mechanism, it is required that the selected choice of pre… view at source ↗
read the original abstract

Value-based argumentation enhances a classical abstract argumentation graph - in which arguments are modelled as nodes connected by directed arrows called attacks - with labels on arguments, called values, and an ordering on values, called audience, to provide a more fine-grained justification of the attack relation. With more than one agent facing such an argumentation problem, agents may differ in their ranking of values. When needing to reach a collective view, such agents face a dilemma between two equally justifiable approaches: aggregating their views at the level of values, or aggregating their attack relations, remaining therefore at the level of the graphs. We explore the strenghts and limitations of both approaches, employing techniques from preference aggregation and graph aggregation, and propose a third possibility aggregating rankings extracted from given attack relations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript addresses aggregation of value-based argumentation frameworks across multiple agents who differ in their value rankings. It identifies a dilemma between value-level aggregation and attack-relation (graph-level) aggregation, draws on preference and graph aggregation techniques to examine their strengths and limitations, and proposes a third strategy of first extracting rankings from attack relations and then aggregating those rankings.

Significance. If the promised exploration were carried out with concrete comparisons, examples, and analysis of the three strategies, the work could usefully extend multi-agent argumentation by clarifying trade-offs between established aggregation methods and a derived-ranking alternative. The manuscript's reliance on existing preference and graph aggregation literature is a potential strength, but the absence of any theorems, worked examples, or formal characterizations prevents assessment of whether the three approaches are shown to be meaningfully distinct or viable.

major comments (1)
  1. [Abstract] Abstract: the central claim that the authors 'explore the strengths and limitations of both approaches' and 'propose a third possibility' is unsupported; the manuscript contains no theorems, examples, or detailed analysis that would allow evaluation of the claimed strengths, limitations, or distinctiveness of the three aggregation strategies.
minor comments (1)
  1. [Abstract] Typo: 'strenghts' should read 'strengths'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comments. We agree that the current manuscript is largely conceptual and would benefit from concrete examples, comparisons, and formal analysis to substantiate the claims regarding the three aggregation strategies. We address the major comment below and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the authors 'explore the strengths and limitations of both approaches' and 'propose a third possibility' is unsupported; the manuscript contains no theorems, examples, or detailed analysis that would allow evaluation of the claimed strengths, limitations, or distinctiveness of the three aggregation strategies.

    Authors: We acknowledge the validity of this observation. The manuscript identifies the value-level versus graph-level aggregation dilemma and outlines a third approach of aggregating extracted rankings, drawing on preference and graph aggregation literature at a conceptual level. However, it does not provide theorems, worked examples, or detailed comparative analysis to demonstrate distinctiveness or viability. We will revise the manuscript to include concrete examples for each strategy, a side-by-side comparison of their properties (e.g., respecting unanimity or handling cycles), and a formal characterization of when the third approach differs from the first two. revision: yes

Circularity Check

0 steps flagged

No significant circularity; exploratory comparison with no derivations

full rationale

The paper is an exploratory comparison of three aggregation strategies (value-level, graph-level, and derived-ranking-level) in value-based argumentation frameworks. It employs established techniques from preference aggregation and graph aggregation without presenting equations, formal derivations, or load-bearing self-citations. The abstract and description frame the work as exploring strengths and limitations of equally justifiable approaches plus a third possibility, with no reduction of claims to fitted inputs or self-referential definitions. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is conceptual and rests on standard domain assumptions from argumentation theory and social choice without introducing new free parameters or invented entities.

axioms (1)
  • domain assumption Multiple agents may hold differing rankings of values when facing the same argumentation problem.
    This premise is stated directly in the abstract as the source of the aggregation dilemma.

pith-pipeline@v0.9.0 · 5665 in / 1151 out tokens · 31001 ms · 2026-05-24T18:00:17.882788+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages

  1. [1]

    Journal of Artificial Intel- ligence Research 60, pp

    St´ ephane Airiau, Elise Bonzon, Ulle Endriss, Nicolas M audet & Julien Rossit (2017): Rationalisation of profiles of abstract argumentation frameworks: Characteri sation and complexity. Journal of Artificial Intel- ligence Research 60, pp. 149–177, doi:10.1613/jair.5436

    work page doi:10.1613/jair.5436 2017

  2. [2]

    Kenneth J Arrow (1951): Social Choice and Individual Values . Wiley

    work page 1951

  3. [3]

    Journal of Logic and Computation 27(1), pp

    Edmond A wad, Richard Booth, Fernando Tohm´ e & Iyad Rahwan (2015): Judgement Aggregation in Multi- Agent Argumentation. Journal of Logic and Computation 27(1), pp. 227–259, doi:10.1093/logcom/exv055

    work page doi:10.1093/logcom/exv055 2015

  4. [4]

    Journal of Logic and Computation 13(3), pp

    Trevor Bench-Capon (2003): Persuasion in Practical Argument Using Value-Based Argume ntation Frame- works. Journal of Logic and Computation 13(3), pp. 429–448, doi:10.1093/logcom/13.3.429

    work page doi:10.1093/logcom/13.3.429 2003

  5. [5]

    Arti- ficial Intelligence 171(1), pp

    Trevor Bench-Capon, Sylvie Doutre & Paul E Dunne (2007): Audiences in Argumentatio Frameworks. Arti- ficial Intelligence 171(1), pp. 42–71, doi:10.1016/j.artint.2006.10.013

    work page doi:10.1016/j.artint.2006.10.013 2007

  6. [6]

    Cambridge University Press, doi:10.1017/CBO9781107446 984

    Felix Brandt, Vincent Conitzer, Ulle Endriss, J´ erˆ omeLang & Ariel D Procaccia (2016): Handbook of Com- putational Social Choice . Cambridge University Press, doi:10.1017/CBO9781107446 984. 330 Aggregation in V alue-Based Argumentation Frameworks

    work page doi:10.1017/cbo9781107446 2016

  7. [7]

    Artificial Intelligence 269, pp

    Weiwei Chen & Ulle Endriss (2019): Preservation of Semantic Properties in Collective Argumen tation: The Case of Aggregating Abstract Argumentation Frameworks . Artificial Intelligence 269, pp. 27–48, doi:10.1016/j.artint.2018.10.003

    work page doi:10.1016/j.artint.2018.10.003 2019

  8. [8]

    Artificial Intelligence 171(10-15), pp

    Sylvie Coste-Marquis, Caroline Devred, S´ ebastien Kon ieczny, Marie-Christine Lagasquie-Schiex & Pierre Marquis (2007): On The Merging of Dung’s Argumentation Systems . Artificial Intelligence 171(10-15), pp. 730–753, doi:10.1016/j.artint.2007.04.012

    work page doi:10.1016/j.artint.2007.04.012 2007

  9. [9]

    Proceedings of the 2016 Knowledge Representa- tion (KR), pp

    J´ erˆ ome Delobelle, Adrian Haret, S´ ebastien Konieczny, Jean-Guy Mailly, Julien Rossit & Stefan Woltran (2016): Merging of Abstract Argumentation Frameworks . Proceedings of the 2016 Knowledge Representa- tion (KR), pp. 33–42

    work page 2016

  10. [10]

    Journal of Logic and Computation 28(7), pp

    J´ erˆ ome Delobelle, S´ ebastien Konieczny & Srdjan V es ic (2018): On The Aggregation of Argumenta- tion Frameworks: Operators and Postulates . Journal of Logic and Computation 28(7), pp. 1671–1699, doi:10.1093/logcom/exy023

    work page doi:10.1093/logcom/exy023 2018

  11. [11]

    Artificial Intelligence 77(2), pp

    Phan Minh Dung (1995): On the Acceptability of Arguments and its Fundamental Role i n Nonmono- tonic Reasoning, Logic Programming and n-Person Games . Artificial Intelligence 77(2), pp. 321–357, doi:10.1016/0004-3702(94)00041-x

    work page doi:10.1016/0004-3702(94)00041-x 1995

  12. [12]

    Artificial Intelligence 175(2), pp

    Paul E Dunne, Anthony Hunter, Peter McBurney, Simon Par sons & Michael Wooldridge (2011): Weighted Argument Systems: Basic Definitions, Algorithms, and Compl exity Results. Artificial Intelligence 175(2), pp. 457–486, doi:10.1016/j.artint.2010.09.005

    work page doi:10.1016/j.artint.2010.09.005 2011

  13. [13]

    Proceedings of the Coference on Computational Models of Arg ument (COMMA), pp

    Paul E Dunne, Pierre Marquis & Michael Wooldridge (2012 ): Argument Aggregation: Basic Axioms and Complexity Results. Proceedings of the Coference on Computational Models of Arg ument (COMMA), pp. 129–140

    work page 2012

  14. [14]

    Dunne, Pierre Marquis & Michael Wooldridge (201 2): Argument Aggregation: Basic Axioms and Complexity Results

    Paul E. Dunne, Pierre Marquis & Michael Wooldridge (201 2): Argument Aggregation: Basic Axioms and Complexity Results. In: Computational Models of Argument , pp. 129–140

    work page

  15. [15]

    Endriss (2016): Judgment Aggregation

    Ulle Endriss (2016): Judgment aggregation . In F Brandt, V Conitzer, U Endriss, J Lang & A. D Procaccia, editors: Handbook of Computational Social Choice , Cambridge University Press, doi:10.1017/cbo9781107446984.018

    work page doi:10.1017/cbo9781107446984.018 2016

  16. [16]

    In: Proceedings of the AAAI Conference on Artificial Intelligen ce (AAAI), pp

    Ulle Endriss & Umberto Grandi (2014): Binary Aggregation by Selection of the Most Representative V oter. In: Proceedings of the AAAI Conference on Artificial Intelligen ce (AAAI), pp. 668–674

    work page 2014

  17. [17]

    In: 21st European Conference on Artificial Intelligence (ECAI) , pp

    Ulle Endriss & Umberto Grandi (2014): Collective Rationality in Graph Aggregation. In: 21st European Conference on Artificial Intelligence (ECAI) , pp. 291–296

    work page 2014

  18. [18]

    Endriss & U

    Ulle Endriss & Umberto Grandi (2017): Graph Aggregation . Artificial Intelligence 245, pp. 86–114, doi:10.1016/j.artint.2017.01.001

    work page doi:10.1016/j.artint.2017.01.001 2017

  19. [19]

    In Ulle Endriss, editor: Trends in Computational Social Choice, chapter 7, AI Access, pp

    Patricia Everaere, S´ ebastien Konieczny & Pierre Marq uis (2017): An Introduction to Belief Merging and its Links with Judgment Aggregation. In Ulle Endriss, editor: Trends in Computational Social Choice, chapter 7, AI Access, pp. 123–143

    work page 2017

  20. [20]

    In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , pp

    Xiuyi Fan & Francesca Toni (2015): On Computing Explanations in Argumentation . In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , pp. 1496–1502

    work page 2015

  21. [21]

    Econometrica 41(4), pp

    Allan Gibbard (1973): Manipulation of Voting Schemes: a General Result . Econometrica: Journal of the Econometric Society, pp. 587–601, doi:10.2307/1914083

    work page doi:10.2307/1914083 1973

  22. [22]

    Grossi & G

    Davide Grossi & Gabriella Pigozzi (2014): Judgment Aggregation: a Primer. Synthesis Lectures on Artificial Intelligence and Machine Learning 8(2), pp. 1–151, doi:10.2200/S00559ED1V01Y201312AIM027

    work page doi:10.2200/s00559ed1v01y201312aim027 2014

  23. [23]

    International Journal of Approximate Reasoning 48(3), pp

    Souhila Kaci & Leendert van der Torre (2008): Preference-based argumentation: Arguments sup- porting multiple values . International Journal of Approximate Reasoning 48(3), pp. 730–751, doi:10.1016/j.ijar.2007.07.005

    work page doi:10.1016/j.ijar.2007.07.005 2008

  24. [24]

    Master’s thesis, Universiteit V an Amsterdam

    Grzegorz Lisowski (2018): Preventing Manipulation in Aggregating V alue-Based Argum entation Frame- works. Master’s thesis, Universiteit V an Amsterdam. G. Lisowski, S. Doutre & U. Grandi 331

    work page 2018

  25. [25]

    In: Proceedings of International Workshop on Systems and Algorithms for Formal Argumentation (SAFA 2018) , pp

    Grzegorz Lisowski, Sylvie Doutre & Umberto Grandi (201 8): Preventing Manipulation in Aggregating Au- diences in V alue-Based Argumentation Frameworks. In: Proceedings of International Workshop on Systems and Algorithms for Formal Argumentation (SAFA 2018) , pp. 48–59

    work page 2018

  26. [26]

    In: Logics in Artificial Intelligence , pp

    Paul-Amaury Matt & Francesca Toni (2008): A Game-Theoretic Measure of Argument Strength for Abstract Argumentation. In: Logics in Artificial Intelligence , pp. 285–297, doi:10.1007/BF01448847

    work page doi:10.1007/bf01448847 2008

  27. [27]

    Artificial Intelligence 267, pp

    Tim Miller (2019): Explanation in Artificial Intelligence: Insights From the S ocial Sciences . Artificial Intelligence 267, pp. 1 – 38, doi:10.1016/j.artint.2018.07.007

    work page doi:10.1016/j.artint.2018.07.007 2019

  28. [28]

    Artificial Intelligence 173(9-10), pp

    Sanjay Modgil (2009): Reasoning About Preferences in Argumentation Frameworks . Artificial Intelligence 173(9-10), pp. 901–934, doi:10.1016/j.artint.2009.02.0 01

    work page doi:10.1016/j.artint.2009.02.0 2009

  29. [29]

    In: Pragmatics of Natural Languages , Springer, pp

    Chaim Perelman (1971): The New Rhetoric . In: Pragmatics of Natural Languages , Springer, pp. 145–149, doi:10.1007/978-94-010-1713-8 8

    work page doi:10.1007/978-94-010-1713-8 1971

  30. [30]

    In: Proceedings of International Conference on Knowledge, Sci ence, Engineering and Management, Springer, pp

    Fuan Pu, Jian Luo, Y ulai Zhang & Guiming Luo (2013): Social W elfare Semantics for Value-Based Argu- mentation Framework. In: Proceedings of International Conference on Knowledge, Sci ence, Engineering and Management, Springer, pp. 76–88

    work page 2013

  31. [31]

    In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems , pp

    Iyad Rahwan & Fernando Tohm´ e (2010): Collective Argument Evaluation as Judgement Aggregation . In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems , pp. 417–424

    work page 2010

  32. [32]

    Journal of Economic Theory 10(2), pp

    Mark Allen Satterthwaite (1975): Strategy-Proofness and Arrow’s Conditions: Existence and Correspon- dence Theorems for V oting Procedures and Social W elfare Fun ctions. Journal of Economic Theory 10(2), pp. 187–217, doi:10.1016/0022-0531(75)90050-2

    work page doi:10.1016/0022-0531(75)90050-2 1975