# Justified Preference Aggregation in Agri-Food Systems: An Approach, Argumentation Methods, and Tools

Pierre Bisquert, Abdelraouf Hecham, Patrice Buche, Maksim Koptelov

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Pierre Bisquert, Abdelraouf Hecham, Patrice Buche, Maksim Koptelov. Justified Preference Aggregation in Agri-Food Systems: An Approach, Argumentation Methods, and Tools. International Journal of Agricultural and Environmental Information Systems, 2025, 16 (1), pp.1-26. 10.4018/IJAEIS.393045. http://hal-05404803

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# Justified Preference Aggregation in Agri-Food Systems: An approach, Argumentation Methods and Tools

Pierre Bisquert¹,², Abdelraouf Hecham², Patrice Buche¹, Maksim Koptelov¹

1. IATE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
{pierre.bisquert, patrice.buche, maksim.koptelov}@inrae.fr

2. LIRMM, Inria, Univ Montpellier, CNRS, Montpellier, France
hecham@lirmm.fr

## Abstract

In recent years, there has been a growing recognition of the need to ensure sustainability of agri-food systems, covering a variety of stakeholders and activities from production to waste management. Multi-Criteria Decision Assessment (MCDA) have emerged as crucial tools for sustainability assessment, but many do not consider stakeholder perspectives, which prevents their adoption and use. Our projects, NoAW (No Agricultural Waste) and AgriLoop (High-value products from agricultural residues through sustainable chains), address this issue by focusing on innovative valorization routes for agricultural waste by assessing stakeholder impact categories through participatory decision-making. This article proposes a novel methodology integrating computational social choice and argumentation techniques for achieving justified collective decision-making in agri-food systems. This methodology includes a theoretical framework and related tools, which facilitate the identification, analysis, and aggregation of preferences based on justifications.

**Keywords:** argumentation theory, computational social choice, Life Cycle Analysis

## 1 Introduction

In the past decade, a growing agreement has emerged on the need to address sustainability of agri-food systems (Eakin et al., 2017). These systems, spanning from production to waste disposal, along with resource flows and outcomes, constitute networks of actors and activities (Meynard et al., 2017). While many studies overlook mentioning stakeholders or specify their contributions, stakeholder involvement remains a critical issue in sustainability and operational research (Gésan-Guisiou et al., 2020). Engaging stakeholders, including public authorities and end-users, in Multi-Criteria Decision Assessment (MCDA) studies can enhance acceptance and utilization of the knowledge generated by these studies (Macharis & Bernardini, 2015; Triste et al., 2014).
Collective decision-making and preference aggregation are widespread in modern society, from formal processes such as political elections to informal situations such as selecting a restaurant among friends. In all these cases, individuals (also called agents) express their preferences over a set of alternatives, and the goal is to fairly aggregate these preferences (or rankings) into a collective ranking to arrive at a decision satisfying the group as a whole. This task often employs classical social choice theory, where the aggregation of preferences is computed using a voting rule (Brandt et al., 2016; Jedwabny et al., 2019).

Collective decision making is particularly useful when preferences vary among agents, as it becomes trivial if all agents agree on the same preferences. Consequently, a fair aggregation method is necessary to conform these differences. While aggregating preferences offers a direct solution to the collective decision-making problem, it only provides a relatively superficial solution with various shortcomings such as the inability to explain the underlying reasons for differences in agents' preferences. To achieve more effective and flexible collective decision-making, it is necessary to delve deeper and consider the justifications for each agent's preferences.

A justification is a set of arguments that an agent provides to describe the reasoning behind his ranking of alternatives. These arguments can be seen as a set of “reasoning steps” leading to a conclusion. Agents may agree or contradict one another on one or many of these reasoning steps. We believe that incorporating these justifications when aggregating preferences would yield a better result. Therefore, the problem we address is obtaining justified collective decision-making on alternatives through automatic justification analysis, achieved by combining computational social choice (for preference aggregation using voting methods) and argumentation techniques (Karanikolas et al, 2018).

Our approach entails soliciting justifications from agents for each of their preferences, automatically detecting agreements and contradictions between the provided reasoning steps, facilitating discussions on points of disagreement, and potentially revising rankings or justifications. Subsequently, argumentation techniques are applied to establish accepted and rejected justifications, which are then used to filter and update the initial preferences. Finally, a voting method from social choice theory is employed to aggregate these updated preferences.

The application-independent outcomes of this study comprise a theoretical methodology on justification analysis and how justifications can be incorporated in preference aggregation, alongside a set of tools:

- a web-based tool (DAMN) that visually depicts points of agreement and disagreement among participants;

- a logic-based tool (ELDR) for reasoning with contradictory knowledge;

- a tool enabling preference changes based on accepted and rejected justifications (Rank sliding approach).

To our knowledge, no other methodology or tool facilitates the use of justifications to modulate preference aggregation.

In addition to the original methodological contribution and tool implementations presented in this article, we demonstrate our approach by applying it to data collected within the NoAW project. In this project, different agents provided preferences on a set of LCA impact
categories, such as water consumption, land use, and global warming, along with their justifications. To facilitate preference representation, we develop a specific ontology for representing justifications about preferences on LCA impact categories. Furthermore, we conduct an analysis of aggregated preferences with and without considering justifications.

In summary, this article offers two main original contributions: (1) a collective decision-making methodology that exclusively considers preferences justified by arguments; (2) a set of tools implementing this methodology, which, to our knowledge, is unique in providing support for multi-agent collaboration and visualization. We also demonstrate the value of our methodology in the context of LCA impact categories by showing how considering preferences' justifications affects the final ranking of the categories.

## 2 Problem setting

In the context of the H2020 NoAW (No Agricultural Waste) project (https://noaw2020.eu), participants expressed the need to be supported in determining the relative importance of different criteria for an Life Cycle Analysis (LCA) of innovative bio-based products which can be obtained from agro wastes.

Starting from this expression of need, a generic problem expression has been identified: how to deal with collective decision-making over a set of options called *alternatives* with the aim of obtaining a *justified collective ranking* of these alternatives from best to worst or from most to least important. These alternatives can represent for example the impact categories of life cycle analysis (e.g. land use, water consumption, etc.).

Formally, the problem can be defined as follows:

* **Input:** A set of options *A* called *alternatives with* $|A|=m>1$ and a set of agents *N* such that $|N|=n>1$. Each agent has a *knowledge base* where he expresses his *preferences* (a ranking on the set of alternatives belonging to the set of all total preorders $\Gamma(A)$) along with a *justification* (a set of facts and rules that justify these preferences, see Section 3.1).

* **Output:** A justified collective ranking of alternatives obtained by the aggregation of all the preferences of the agents by a function $F: \Gamma^n \to A$ while taking into account their justifications.

This overall objective can be split into two main problems (Figure 1): **preference aggregation** and **justification analysis**. Preference aggregation relies generally on the classical social choice theory where the aggregation of the preferences is computed by a *voting rule*. The input in this case is the set of alternatives and the agents' rankings. The output is a collective aggregated ranking of the alternatives.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Overall architecture for justified collective decision-making on alternatives</figcaption></figure>

In this paper, we tackle the problem of justification analysis which comes with its own set of sub-problems: automatic detection of conflicts in the justifications of the agents, establishing accepted justifications by solving these conflicts, and reflecting these accepted justifications by updating the agents' rankings to produce justified preferences. The input in this case is the set of alternatives and the knowledge base of each agent containing his or her ranking along with its justifications. The output is an updated justified ranking for each agent.

## 3 Related work

The model that we use for representing justifications and computing contradictions between them has been introduced in (Hecham et al., 2018a; Hecham et al., 2020). In this model, the link between argumentation theory and defeasible reasoning is established via grounded semantics (Dung, 1995; Prakken & Sartor, 1997) and Dialectical Trees (García & Simari, 2004). Such approaches only allow for ambiguity propagation without team defeat (Governatori et al., 2004; Prakken, 2010). Statement Graphs, however, allow for both ambiguity propagation and blocking with and without team defeat, and have been extended to some repair semantics (Hecham et al., 2020). Technically, Statement Graphs can be seen as a generalization of Abstract Dialectical Frameworks (ADF) (Brewka & Woltran, 2010) that enrich ADF acceptance conditions. While defeasible reasoning is a highly topical area of research (see for instance, Longo et al., 2021; Coetzer & Britz, 2022; Cristani et al., 2022b; Everett et al., 2022; Alviano et al., 2023), as far as we know, there is no other implemented
model that joins Defeasible reasoning and Repairs semantics and relates on the same level semantics from these two different domains.

The first version of the decision support software based on our model for representing justifications and computing contradictions has also been presented (Karanikolas et al., 2018). The social choice system, architecture of which is described in that work, is composed of the following three subsystems: the data collecting, the voting and the decision subsystems. Based on this system a practical methodology has been developed in (Sohn et al., 2020), which provides a relatively simple method for integrating stakeholder preference and relative impact for deriving a weighting for use in applying MCDA. In this article, we go further and present an extended version of this methodology, which includes an ontology for representing justifications about preferences on LCA impact categories and a use case based on real data from the NoAW project. Additionally, we develop graphical tools which allow launching and visualizing each step of the process. To the best of our knowledge, there is no other alternative methodology for getting justifications and automatically compute contradictions between them, especially in the context of assessing the importance of LCA impact categories.

As for our tools' alternatives, there are relatively few existing works due to the limited amount of recent research in this specific area. This can be explained by the fact that defeasible reasoning is rather a theoretical discipline, and applying such tools in a concrete, practical context, such as ours, is itself a novel contribution. Nevertheless, a few solutions for defeasible (conflict-tolerant) reasoning have been identified in the literature, most notably ASPIC+ (Prakken, 2010), DeLP (García & Simari, 2004), DEFT (Hecham et al., 2017), Flora-2 (Wan et al., 2015), Houdini (Cristani et al., 2022a; Cristani et al., 2025) and SPINdle (Lam, 2012). However, each of these tools supports a different set of defeasible reasoning features, as detailed in (Hecham et al., 2018b). More precisely, one of DAMN's key strengths lies in its flexible handling of ambiguity and defeat in argumentation reasoning, specifically through:

* **Ambiguity Blocking vs. Ambiguity Propagating:** In ambiguity blocking semantics, when two arguments are in unresolved conflict (e.g., due to equal "strength" or missing superiority), ambiguity is contained: ambiguous statements cannot be used to attack other arguments. In contrast, ambiguity propagating semantics allows ambiguous statements to influence further reasoning, spreading uncertainty through the graph through "ambiguous attacks". Damn supports both modes, letting users choose based on the nature of their decision context.

* **Team Defeat:** Damn also allows for "team defeat", where multiple supporting arguments can collectively counter a set of attackers. "No team defeat", on the other hand, requires that each argument individually withstand all attacks. Somehow, team defeat reflects real-world collaborative defense of conclusions and enhances the expressive power of the reasoning process.

Additionally, as shown in the following Table 1, Damn is the only tool allowing for multi-agent web-based collaboration and visualization, which makes it especially well-suited for online participatory platforms and multi-stakeholder decision contexts.
<table><caption>**Table 1.** Tools' features based on and extended from (Hecham et al., 2018b) (✓ indicates the tool supports the feature)</caption><thead><tr><th colspan="2">Feature</th><th>ASPIC +</th><th>DeLP</th><th>DEFT</th><th>Flora-2</th><th>SPINdle</th><th>Houdini</th><th>DAMN</th></tr></thead><tbody><tr><td rowspan="2">Ambiguity</td><td>Propagating</td><td>✓</td><td>✓</td><td>✓</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Blocking</td><td>-</td><td>-</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td rowspan="2">Team Defeat</td><td>With</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Without</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>✓</td></tr><tr><td colspan="2">Web-based</td><td>✓</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td colspan="2">Agent collaboration</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>✓</td></tr><tr><td colspan="2">Visualization</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>✓</td></tr></tbody></table>

# 4 Methods

In this section, we describe the theoretical approach to tackle the questions presented in Section 2.

## 4.1 Automatic reasoning about justifications

A justification is a set of arguments that an agent puts forward to describe the reasoning behind his/her ranking of alternatives. These arguments can be seen as a set of “reasoning steps” to reach a conclusion. Agents may agree or contradict one another on one or many of these reasoning steps. In order to automatically build and detect conflicts between the different justifications, we need to express these justifications in a logical language representing facts and rules (as shown in Example 1 below). In the following, we use the existential rules logical language (Cali et al., 2010) which is a first order logical fragment used in the Semantic Web and Databases domain. In this language, knowledge is expressed using **atoms** which describe a property of some individuals.

**Definition 1.** We consider a logical *L* language composed of formulas built with the usual quantifiers (∃, ∀) and the connectors implications (⇒) and conjunction (∧). An atom is of the form *p(t₁,...,tₖ)* where *p* is a predicate and *t₁,...,tₖ* are variables or constants.

The logical language also allows the representation of tautology “⊤” and falsity “⊥” and are considered themselves atoms. **Tautology** indicates that some knowledge
is unconditionally true, while **Falsity** indicates contradictions. For example, the knowledge that "an animal cannot fly and not fly at the same time" is represented as "$\forall X \ fly(X) \land notFly(X) \Rightarrow \perp$".

**Definition 2.** A rule *r* is a formula of the form $\forall X,Y (B(X,Y) \Rightarrow \exists ZH(X,Z)$ where *X*, *Y* are tuples of variables, *Z* is a tuple of existential variables and *B*, *H* are finite non-empty conjunctions of atoms respectively called body and head of *r*.

We can now define a superiority relation between rules.

**Definition 3.** A (defeasible) knowledge base is a tuple $(F,R,>>)$ where $F$ is a set of facts of the form $\vdash \Rightarrow B(a)$, $R$ is a set of rules and $>>$ is an acyclic superiority relation over rules. A rule $r_1$ is said to be superior to another rule $r_2$ iff $r_1 >> r_2$ and $\neg (r_2 >> r_1)$.

Superiority relation expresses that certain rules override other rules, for example: the rule stating that "penguins are non-flying birds" overrides the rule "if it is a bird then it flies".

**Example 1.** Two agents want to know if a penguin called "kowalski" flies. Each agent has his own knowledge base and they share a common knowledge.

* Common knowledge base: $\{\vdash \Rightarrow \text{penguin}(\text{kowalski}), \forall X \ fly(X) \land notFly(X) \Rightarrow \perp\} (\text{kowalski is a penguin, and one cannot fly and not fly at the same time}).$

* Alice's knowledge base: $\{ r1: \forall X \ penguin(X) \Rightarrow bird(X) \land notFly(X) \} (\text{penguins are birds who do not fly}).$

* Bob's knowledge base: $\{ r2: \forall X \ bird(X) \Rightarrow fly(X) \} (\text{birds fly}).$

From this knowledge we can generate the following reasoning steps:

* By applying $r1$ we generate: "$\text{penguin}(\text{kowalski}) \Rightarrow \text{bird}(\text{kowalski}) \land notFly(\text{kowalski})$"

* By applying $r2$ we generate: "$\text{bird}(\text{kowalski}) \Rightarrow fly(\text{kowalski})$"

Representing the justifications in a logical language allows us to identify the links between the different reasoning steps (**support links**) when the conclusion of a rule is used in the hypothesis of another rule, and **attack links** when the conclusion of rule contradicts the hypothesis of another rule). These links are represented using Statement Graphs (Hecham et al., 2018a) where each node (also called statement) represents a reasoning step, dashed arrows represent support links while normal arrows represent attack links. The statement "$\Rightarrow \vdash$ (true)" which has an "accepted strictly" status (i.e. cannot be rejected) represents the concept of truth and is used to supports factual knowledge. For instance, the previous example can be represented in the Statement Graph depicted in Figure 2.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Statement graph of Example 1</figcaption></figure>

By using the Statement Graph formalism, we can automatically generate the reasoning steps and build the support and attack links representing agreements and contradictions which will be used afterward to establish if a justification is accepted or rejected.

Considering that accepting or rejecting a reasoning step is achieved using logical semantics (Carnielli et al., 2001), we benefit from using conflict-tolerant semantics such as defeasible reasoning using Statement Graphs (Hecham et al., 2018a). This type of semantics is an efficient form of conflict-tolerant reasoning. It allows the expression of agents' knowledge as a set of facts and rules along with a superiority relation between rules. This superiority relation also allows us to define outcomes scenarios where we can check which justifications are accepted if we consider the knowledge of one type of agent as "more important" than other agents.

Defeasible reasoning comes with different semantics (Antoniou et al., 2000). For simplicity, we will only describe *"ambiguity propagation without team defeat"* (Hecham et al., 2018a). In this method, a label to each node of the Statement Graph can either be IN (accepted), OUT (rejected), or AMBIG (ambiguous).

The intuition behind the IN, OUT, AMBIG labels is as follows:

* A statement is labeled **IN** (accepted) if all atoms in its hypothesis are supported by accepted statements and none of them is attacked by an accepted statement.
* A statement is labeled **OUT** (rejected) if one of the atoms in its hypothesis is not supported by an accepted statement or it is attacked by an accepted statement with a superior rule.

* A statement is labeled **AMBIG** (ambiguous) if one of the atoms in its hypothesis is supported by an accepted statement and it is attacked by an IN statement or it is only supported by an ambiguous statement.

**Example 2.** Consider the decision problem of Example 1. The status of "fly(kowalski)" and "notFly(kowalski)" is **ambiguous** because they are both supported and attacked by accepted statements (Figure 3). Now suppose the data engineer agrees that the rule stating "penguins are non-flying birds" overrides the rule stating "if it is a bird then it flies". In this case he/she favors the knowledge expressed by Alice over the one expressed by Bob by adding the superiority relation "r₁ >> r₂" to the common knowledge base (Figure 3). In this case "fly(kowalski)" is now rejected because it is attacked by an accepted superior rule application, and "notFly(kowalski)" is now accepted.

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Statement graph of Example 2 without superiority relation (left) and with superiority relation (right)</figcaption></figure>

By using the defeasible reasoning semantics we can obtain the status of the different reasoning steps. This would later need to be reflected in the agents' rankings.
## 4.2 Updating rankings

The aim of this step is to produce justified rankings. Once the status of justifications is established, each agent's rankings need to be updated to reflect only *"admissible"* rankings. By *"admissible"* rankings we understand a ranking that either has an accepted or a reasonably contested (i.e. *"ambiguous"*) justification. Therefore, we need to properly define admissible rankings by answering the following questions: what is a reasonably contested justification? And how should rankings that are unjustified or have rejected/ambiguous justification be handled? In the following, we propose two approaches to answer these questions.

### 4.2.1 Collaboration approach

The idea of the **collaboration approach** is to have the agents discuss their points of views and change their justifications or their rankings based on the contradictions that our automatic reasoning platform identifies. This discussion might push agents to accept proposed changes and result in only admissible rankings with accepted justifications, which will be transmitted to the selected voting rule and a final decision will be reached.

This is a favorable option since it allows agents to discuss their justifications, and conflicts might be resolved before a vote even happens. However, if the agents are not available to engage in a discussion, or even after discussing they still end up with ambiguous rankings, a more advanced approach could solve the problem.

### 4.2.2 Rank sliding approach

The idea behind **rank sliding approach** is to change the rankings of agents (if the status of their justification is rejected or ambiguous) by *"sliding"* their ranks to the closest rank with accepted justification expressed by any one of the agents. It means that a vote with rejected or ambiguous justification is considered similar to its closest justified vote. To avoid arbitrary changes of rankings we defined a set of desirable properties for a rank sliding approach:

* Inadmissible ranks should be changed towards the "best" admissible rank.

* The amount of rank changes should be minimum.

* Precocious principle: a change towards higher ranks should be favored.

* Agents should not only justify their rankings but also to either support or contradict other agents' justifications.

Based on that we propose a **skeptical rank sliding approach** with the following conditions:

1. A rank is admissible if it has an accepted justification or if it has an ambiguous justification and there is no rank with an accepted justification.

2. A rank with an ambiguous justification is changed to the closest rank with an accepted justification if it exists. If two ranks with accepted justification are at the
same distance from this rank, then it is changed to the closest higher rank with accepted justification. Otherwise, if there is no rank with an accepted justification then this rank remains unchanged.

3. A rank with a rejected justification or without a justification is simply dismissed.

To illustrate how our approach works in practice consider a use case in the NoAW project.

**Example 3.** Let us imagine a situation where three agents discuss what is the criterion that has the biggest impact on the environment, between water consumption, global warming and land use (which are part of the bigger set of LCA impact categories; see Section 6). Each of the agents gives their preferences under the form of a ranking, with (or without) some justifications:

* **Alice (waterConsumption > globalWarming > landUse):** waterConsumption is ranked 1st because it is a global issue especially with the limited supply of fresh or usable water. GlobalWarming is ranked 2nd because it is hard to control, and landUse is ranked 3rd because it has a limited weak impact on food supply compared to the other two alternatives.

* **Bob (globalWarming > landUse > waterConsumption):** globalWarming is ranked 1st because it is almost an irreversible outcome and should be the highest priority as an impact category. LandUse is ranked 2nd because it has a strong impact on food supply. WaterConsumption is ranked 3rd because it is a local problem and a solution is available through recycling.

* **Carol (landUse > waterConsumption > globalWarming):** absence of justifications.

Without considering justifications, the aggregated ranking using for example the score voting method (Baujard et al., 2018) where each rank is given a score (1st is 2 points, 2nd is 1, and 3rd is 0) would be **"globalWarming (3) ~ landUse (3) ~ waterConsumption (3)"** where each alternative has a score of 3. This means that all alternatives are equivalent. However, by taking into account the arguments and applying our rank sliding approach we can get:

1. **Global Warming:** both Alice's and Bob's rank for the globalWarming alternative have accepted justification, therefore they are not changed. However, Carol did not justify her ranking and thus her justification is rejected. Her rank of 3rd for globalWarming should be changed to the closest rank with accepted justification, which is 2nd (Figure 4);
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Statement Graphs for the globalWarming ranking and justifications.</figcaption></figure>

2. **Water Consumption:** Bob's rank for water consumption has an accepted justification therefore it is not changed. Alice's rank for water consumption has an ambiguous justification while Carol's rank has a rejected one, thus they are both changed to the closest rank with accepted justification, which is 3rd (Figure 5);

<figure><img src="image_6.png" /><figcaption>**Figure 5.** Statement Graphs for the waterConsumption ranking and justifications.</figcaption></figure>

3. **Land Use:** both Alice's and Bob's rank for land use have an ambiguous justification, and since there is no rank with an accepted justification, they remain unchanged. Carol's rank has a rejected justification therefore it is changed to the closest admissible rank, which is 2nd (Figure 6).
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Statement Graphs for the landUse ranking and justifications.</figcaption></figure>

The final updated rankings are:

* **Alice:** globalWarming (1) > landUse (0) ~ waterConsumption (0).

* **Bob:** globalWarming (2) > landUse (1) > waterConsumption (0).

* **Carol:** globalWarming (1) ~ landUse (1) > waterConsumption (0).

Using the score voting method, we can find that globalWarming has a score of 4, landUse has a score of 2, and waterConsumption has a score of 0, therefore the final aggregated ranking is: **globalWarming (4) > landUse (2) > waterConsumption (0).**

## 4.3 Justification representation

In the NoAW project, we asked the different agents to provide their rankings on the impact categories and justify them. These justifications have been expressed as paragraphs or sentences. In order to fully exploit the powerful inference mechanism of logic showcased in the previous sections, we need to represent these justifications in a logical language. Taking into account that the English language in which the survey was conducted is much more expressive than the logical language we use, a set of predefined vocabulary (also called ontology) needs to be defined in order to express the justifications in a unified manner. This fixed vocabulary offers two main advantages:

1. A unified representation of all justifications, which makes the automatic detection of agreements and disagreements easier.

2. The ability to automatically translate the reasoning steps expressed in the logical language to a sentence expressed in human language. For example, the atom "impact(waterConsumption, freshwater, negative, strong, local)" can be translated to: "Water consumption has a local strong negative impact on freshwater"; this can be
expressed in an abstract way as "impact(X,Y,Z,U,V)" and it is translated to "X has a V,U,Z impact on Y".

Given the different justifications with their explicit and implicit knowledge expressed in the survey, we have defined special vocabulary to represent explicit and implicit knowledge expressed in the survey:

* **"impact, Something, Type, Strength, Level)"** to express the *strength* (strong, medium, weak) and *type* (positive, neutral, negative) of the impact that an impact *category* has on *something* (e.g. freshwater, environment, etc.) at a specific *level* (local, global, etc.). For example, "impact(landUse, foodSupply, negative, weak, global)" which translates to "landUse has a global weak negative impact on foodSupply".

* **"accordingTo(Source, Something, Adjective)"** to express that a Source (e.g. media, own knowledge, etc.) has qualified Something (e.g. landUse) by an Adjective (e.g. important, etc.). For example, "accordingTo媒媒媒媒媒媒)" – "According to media, landUse is veryImportant".

* **"solution(Problem, Type, Adjective)"** to express that there is a Adjective (e.g. available, easy, etc.) solution to the Problem (e.g. deforestation, etc.) with a specific Type (e.g. political, technological, etc.). For example, "solution(freshwaterEcotoxicity, technological, available)" – "A technological solution to the freshwaterEcotoxicity problem is available)".

* **"action(Action, Adjective)"** to give an action an adjective. For example, "action(increasingLandConsumption, unsustainable)" – *increasingLandConsumption* is unsustainable*.

* **"essentialFor(Something1, Something2)"** to express the strong link between certain concepts. For example, "essentialFor(land,foodProduction)" – "land is essential for foodProduction".

* **"relatedTo, Something)"** to express the different concepts that are related to an impact category. For example, "relatedTo(landUse, deforestation)" – *landUse* is related to *deforestation*.

* **"cause(Something1, Something2)"** to express that Something1 causes Something2. For example, "cause(populationGrowth,increasedDemandForWater)" – "populationGrowth causes increasedDemandForWater".

* **"status(Something, Adjective, Level)"** to express the state of Something with an Adjective and a Level (global, local, etc.). For example, "status(freshwater, rare, global)" – "the status of freshwater is rare on a global level".

* Other case-specific predicates have been added depending on the needs, such as **sameAs**, **moreImportantThan**, **lessImportantThan**, etc.

Justifications provided by the agents need to be manually expressed using this vocabulary as shown in Example 4.

**Example 4.** "Water will be more precious due to population growth" as a
justification for giving "Water Consumption" the rank of 3. This is translated to
"status(water, rare, unspecified) ∧
cause(populationGrowth, increasedDemandForWater) ∧
impact(waterConsumption, freshwater, negative, unspecified, unspecified) →
rank(waterConsumption, 3)"

Using this representation we can define common sense contradictions such that a category
cannot have the same type of impact on the same thing with a different strength. For
example, “impact(landUse, foodSupply, negative, weak, global)” is in contradiction with
“impact(landUse, foodSupply, negative, strong, global)”, the same can be said for the level of
impact (local vs global).

5 Tools' workflow

To support justified collective decision-making we developed three separate tools, each
corresponding to a key step in our approach: (1) the **DAMN** tool for inputting, displaying and
sharing knowledge among agents; (2) the **ELDR** tool for reasoning and determining the
status of justifications (accepted, rejected, or ambiguous); and (3) the **Rank Sliding** module
for updating individual rankings based on justification status.

<figure><img src="image_8.png" /><figcaption>Figure 7. The DAMN tool, ELDR tool, and Rank sliding approach workflow and their interaction with a user (numbers in circles indicate the typical sequence of steps).</figcaption></figure>

The interaction between these tools is illustrated in Figure 7. It works as follows:

1. Users start by entering knowledge into the **DAMN** interface, either manually (using a specific logical format) or by uploading a file.

2. **DAMN** sends this input knowledge to **ELDR**, which performs logical reasoning to identify and evaluate arguments.

3. **ELDR** returns the results to **DAMN**, which displays the argumentation graph, showing agreement and disagreement points.
4. **DAMN** can then forward the justifications and preferences to the **Rank Sliding** tool, which adjusts each agent's ranking accordingly and returns the updated, justified rankings to the user.

Although these tools are designed to work together as a complete platform, each module
can also be used independently for its specific function. In the following sections, we provide
a detailed description of each tool.

5.1 The DAMN tool

The **DAMN** tool (Defeasible Reasoning With Statement Graph) serves as the entry point of our decision-making platform. It supports knowledge input, visualization, and real-time collaboration among agents. Available at https://ico.iate.inra.fr/damn/, DAMN allows agents (or a data engineer acting on their behalf) to input their knowledge using a dedicated logical format. It also enables collaborative editing and displays the resulting Statement Graph once reasoning has been performed. In addition, DAMN exports each agent's knowledge base in a structured file format for further processing by the other modules in the platform.

<figure><img src="image_9.png" /><figcaption>Figure 8. Project webpage of the DAMN tool.</figcaption></figure>

In the DAMN tool, every decision-making problem is called a project (Figure 8). A project can include several agents, each with their own knowledge base. It's important to understand the difference between an agent and a user: an agent is a participant in the decision-making process (a person or a group) with its own knowledge and preferences, while a user is the person operating the tool, who can manage one or more agents.
<figure><img src="image_10.png" /><figcaption>Figure 9. A typical workflow of the DAMN tool.</figcaption></figure>

Figure 9 shows the typical workflow of the DAMN tool:

1. A user logs in or creates his account on the platform;

2. He can then create or open a previously created project;

3. The user can add or remove agents to the project along with their knowledge bases;

4. He can invite other users to collaborate, insert, and modify the knowledge of one or multiple agents in real time.

5. The user can build the Statement Graph from some or all the knowledge bases and check the status of a justification by inputting a query and choosing a semantics;

6. He can then interact with the resulting displayed graph.

For further details on the tool's file formats and implementation, please refer to our supplementary material (Bisquert et al., 2024).

## 5.2 The ELDR tool

The ELDR (Existential Logic for Defeasible Reasoning) tool implements logical reasoning and argument evaluation. Its main function is to construct the support and attack links
between statements and determine the status of justifications (accepted, rejected, or ambiguous) based on the selected semantics.

As the "brain" of our decision-making platform, ELDR takes agents' knowledge bases as input in a specific file format and outputs the corresponding Statement Graph in the same format. Optionally, it can also output the status of each reasoning step.

ELDR operates as a reasoning engine and is not typically used via a graphical interface like DAMN. Instead, users interact with it through file exchange. The typical workflow is as follows:

1. Users prepare the input by creating a special file containing the logical knowledge bases of one or more agents. This file is either created manually, exported from the DAMN tool or compiled by a data engineer.

2. File is provided to the ELDR tool through a connector integrated into DAMN.

3. ELDR processes the input file, applies logical reasoning, and constructs the Statement Graph. Based on the chosen semantics, it evaluates each justification and assigns it a status.

4. ELDR outputs a file containing the complete Statement Graph, including support and attack links, and optionally the status of each reasoning step.

5. This output file is returned to the user or passed back to the DAMN interface, where the results are displayed visually for analysis or further decision-making. The same file may also be forwarded to the Rank Sliding tool for ranking updates.

For more technical details about the tool, including file formats, please refer to our supplementary material (Bisquert et al., 2024).

## 5.3 Rank sliding approach

The **Rank Sliding tool** is the component responsible for updating and generating justified rankings of alternatives based on the status of justifications computed by ELDR. It acts as the "decision-making" layer of the platform. Users interact with this tool primarily through structured input and output files. The typical workflow is as follows:

1. The user (or an upstream tool like DAMN) prepares a special file containing the Statement Graph, the status of each justification (accepted, rejected, or ambiguous) and the agents' original preference rankings and knowledge bases.

2. The tool updates the initial ranking producing a revised, argument-supported ranking and generates a new file that includes the updated justified rankings for each agent. This file can then be visualized in DAMN or exported for further analysis.

3. The updated rankings can be reviewed, compared or aggregated, providing a more transparent and rational basis for collective decision-making.

As before, for more details about the tool, please refer to our supplementary material (Bisquert et al., 2024).
# 6 Use cases assessment of methodology and tools

In order to demonstrate the genericity and usability of our proposed methodology and associated tools, we partially apply them to a first use case based on data from the EU FP7 ECOBIOCAP project, which serves as preliminary work for this study. We then fully apply them to a second use case using data generated within the EU H2020 NoAW project.

## 6.1 ECOBIOCAP project use case

The aim of the EcoBioCAP project was to provide the EU food industry with customizable, eco-efficient, biodegradable packaging solutions that offer direct benefits to both the environment and EU consumers in terms of food quality and safety. In this framework, a web-based survey was conducted in 2013 in France, gathering input from 235 consumers who shared their preferences regarding food packaging functionalities. In the following, we present a use case of our methodology, where participants expressed their preferences and corresponding justifications concerning the transparency of cheese packaging (transparent, translucent, opaque). As a preliminary step, we applied part of the method presented in this paper to this dataset. The survey results including respondents' locations, preferences and justifications are available at https://doi.org/10.57745/UHZKCC. Expert opinions were then used to evaluate the justifications provided in the survey. Justifications attacked by those expert opinions were identified and the following rankings were subsequently calculated:

1. Entire set of consumers (235 respondents) : transparent (539) > translucent (478) > opaque (393)

2. Consumers living in Paris (46 respondents): translucent (101) > transparent (98) > opaque (77)

3. Consumers living in Paris (32 respondents with accepted<fnref n="1" /> justification): transparent (69) > translucent (66) > opaque (57)

These results show that aggregated rankings are influenced by the granularity of respondents' geographical location. Additionally, for the same location (Paris), considering only respondents with accepted justifications generates a different ranking than when all responses are included, demonstrating the impact of our method. Furthermore, the impact of incorporating justifications in preference aggregation has been illustrated using the multicriteria decision support system (DSS) developed during the ECOBIOCAP project for selecting suitable packaging solutions associated with specific food products (Guillard et al. 2015). In this system, the optimal packaging characteristics for soft cheese (specifically, permeability to gases such as O₂ and CO₂) were combined with consumers' preferences. The recommended packaging solutions vary depending on the different packaging preferences calculated above (e.g., "translucent > transparent > opaque" vs. "transparent > translucent > opaque")<fnref n="2" />. This example illustrates the applicability and usefulness of our justification-based method in real-world decision-making context.

<footnote n="1">1 In that context, justifications that are not attacked by experts' arguments.</footnote>

<footnote n="2">2 For the full example, please refer to our supplementary material (Bisquert et al., 2024).</footnote>
## 6.2 NoAW project use case

In the scope of the NoAW project, stakeholders were asked to express their preferences and justifications about the importance of the LCA impact categories, which were used then to rank energy sources for electricity production. The data collection process was organized in the form of a survey during international events of the project. The survey was conducted on 31 participants from the European projects NoAW and Agrocycle (https://agrocycle.eu) consisting of both public and private stakeholders (around 64% public researchers, 26% private companies, and 10% public agencies). In the survey, the projects' participants were asked to give a ranking of importance of 18 different impact categories along with their justifications for this ranking. The input data and output files containing the results for NoAW impact indicators (survey data, logical representation of the justifications, original rankings, updated rankings, final aggregation) are available at https://doi.org/10.15454/XVL4BA.

The aggregation of these rankings has then been used in a multi-criteria decision analysis (MCDA) to rank ten sources of energy according to environmental and economic criteria (Sohn et al., 2020). More precisely, the methodology is composed of five steps: (i) collecting stakeholder preferences, (ii) aggregating preferences taking into account justifications, (iii) transforming preferences to weights associated with criteria, (iv) calibration of weights, and (v) application of multi-criteria decision analysis (MCDA). In the following, we focus on the second and the fifth steps presented above. The main objective of the second step was to obtain aggregated rankings, both with and without taking into account the potentially conflicting justifications. The objective of the fifth was to perform MCDA using obtained aggregated rankings with and without considering justifications. To demonstrate the utility of our methodology, we describe part of these results obtained by applying our proposed approaches and tools on this use case.

In the ranking of the LCA impact categories experiment, which we refer to as the *LCA experiment*, we have translated the justifications given by the different agents for 5 impact categories (global warming, water consumption, land use, freshwater eutrophication, and human carcinogenic toxicity). These categories were selected because they contained the most differences between agents' rankings, and consequently a bigger chance for these arguments to be counter-attacked. In the ranking of the energy sources for electricity production experiment using the MCDA method, which we refer to as the *energy experiment*, to demonstrate a full scale example, we used the justifications for all 18 categories.

Given that the agents were not available to perform a discussion phase, the only option we have is to use the rank sliding approach on the rankings with the current justifications. After representing the justifications using our ontology defined in Section 4.4, we build and query the Statement Graphs of each alternative to obtain the status of the justifications. Next, we present the results of aggregating rankings directly (without considering justifications) and with considering justifications.

### 6.2.1 Directly aggregating rankings

In the LCA experiment, we apply the scoring voting method where each rank is given a score (rank 1 has a score of 18, rank 2 has a score of 17, etc, rank 18 has a score of 1). The final score of each alternative is the sum of the agents' score. The aggregated ranking is
computed by ordering the impact categories from highest score to lowest score (Table 2). Therefore we can conclude that the final ranking is: **Global Warming > Human Carcinogenic Toxicity > Land Use > Water Consumption > Freshwater Eutrophication.**

<table><caption>**Table 2.** LCA impact categories of the NoAW project and their ranking aggregation scores without considering justifications</caption><thead><tr><td>Impact Category</td><td>Total Score</td></tr></thead><tbody><tr><td>Global Warming</td><td>477</td></tr><tr><td>Water Consumption</td><td>407</td></tr><tr><td>Land Use</td><td>409</td></tr><tr><td>Freshwater Eutrophication</td><td>372</td></tr><tr><td>Human Carcinogenic Toxicity</td><td>463</td></tr></tbody></table>

As in the LCA experiment, the aggregated ranking in the energy experiment is computed by ordering the energy sources from highest to lowest score, with the final ranking shown in Figure 10. The results vary depending on the economic performance. For instance, when the expected performance is between 10% and 40% the ranking is as follows: **wind > natural gas > geothermal > solar > hydro > nuclear > biomass > biogas > coal > oil**. However, from 40% the trends begin to shift: wind and solar become less important, while biogas and coal gain importance. As a result, at 90% expected performance the ranking becomes: **natural gas > geothermal > wind > biogas > hydro > nuclear > coal > biomass > oil > solar**. These results are consistent with expectations as biogas and coal are more efficient for electricity production under high-performance scenarios compared to wind and solar.

<figure><img src="image_11.png" /></figure>
Figure 10. Ranking of energy source alternatives for electricity production depending on different economical performance based on previously aggregated LCA preferences without considering justifications (left plot) and with considering justifications (right plot). Axe X shows weight associated with economical performance versus environmental performance and axe Y shows normalized values of aggregated scores including economic and environmental performances (a value of 1 is the most preferred and 0 is the least preferred alternative) (reproduced from Sohn et al. 2020).

### 6.2.2 Aggregation of rankings considering justifications

In the LCA experiment, by applying our rank sliding approach we observed 36 individual rank changes: 32 due to a lack of justification (rejected justifications) and 4 due to conflicting justifications (ambiguous justifications) (Figure 11; for more details, refer to the rank sliding data available https://doi.org/10.15454/XVL4BA). Ranks with rejected justifications were dismissed meaning they were changed to rank 19 and assigned 0 points using the score voting method. Ranks with ambiguous justifications were adjusted to the nearest rank with an accepted justification. For example, two agents gave "Water Consumption" the rank 5 with ambiguous justifications. Since rank 4 had an accepted justification, both ranks were changed to 4. It is worth noting that in some cases, agents provided ambiguous justifications for a rank, but the rank was not changed because other agents were able to supply accepted justifications for the same rank. Addressing such cases more systematically is recommended for future work.

<table><thead><tr><th rowspan="2"></th><th colspan="2">Global Warming</th><th colspan="2">Water Consumption</th><th colspan="2">Land Use</th><th colspan="2">Freshwater Eutrophication</th><th colspan="2">Human Carcinogenic Toxicity</th></tr><tr><th>Without justification</th><th>With justification</th><th>Without justification</th><th>With justification</th><th>Without justification</th><th>With justification</th><th>Without justification</th><th>With justification</th><th>Without justification</th><th>With justification</th></tr></thead><tbody><tr><td>agent1</td><td>1</td><td>1</td><td>5</td><td>4</td><td>11</td><td>11</td><td>3</td><td>3</td><td>1</td><td>1</td></tr><tr><td>agent2</td><td>1</td><td>1</td><td>4</td><td>4</td><td>10</td><td>10</td><td>5</td><td>5</td><td>1</td><td>1</td></tr><tr><td>agent3</td><td>1</td><td>1</td><td>6</td><td>6</td><td>1</td><td>1</td><td>8</td><td>8</td><td>3</td><td>3</td></tr><tr><td>agent4</td><td>4</td><td>4</td><td>6</td><td>6</td><td>6</td><td>6</td><td>5</td><td>5</td><td>1</td><td>1</td></tr><tr><td>agent5</td><td>3</td><td>3</td><td>12</td><td>12</td><td>16</td><td>15</td><td>5</td><td>5</td><td>1</td><td>1</td></tr><tr><td>agent6</td><td>3</td><td>3</td><td>7</td><td>7</td><td>6</td><td>6</td><td>5</td><td>5</td><td>1</td><td>1</td></tr><tr><td>agent7</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>4</td><td>19</td><td>3</td><td>19</td></tr><tr><td>agent8</td><td>1</td><td>1</td><td>2</td><td>2</td><td>8</td><td>8</td><td>7</td><td>7</td><td>3</td><td>3</td></tr><tr><td>agent9</td><td>18</td><td>19</td><td>1</td><td>19</td><td>1</td><td>19</td><td>1</td><td>19</td><td>1</td><td>19</td></tr><tr><td>agent10</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>19</td><td>3</td><td>19</td><td>2</td><td>19</td></tr><tr><td>agent11</td><td>1</td><td>1</td><td>2</td><td>2</td><td>3</td><td>3</td><td>14</td><td>14</td><td>5</td><td>19</td></tr><tr><td>agent12</td><td>1</td><td>1</td><td>3</td><td>3</td><td>5</td><td>5</td><td>5</td><td>5</td><td>3</td><td>3</td></tr><tr><td>agent13</td><td>3</td><td>3</td><td>7</td><td>7</td><td>7</td><td>7</td><td>6</td><td>6</td><td>1</td><td>1</td></tr><tr><td>agent14</td><td>10</td><td>19</td><td>3</td><td>19</td><td>3</td><td>19</td><td>16</td><td>19</td><td>10</td><td>19</td></tr><tr><td>agent15</td><td>1</td><td>1</td><td>18</td><td>18</td><td>15</td><td>15</td><td>18</td><td>19</td><td>1</td><td>1</td></tr><tr><td>agent16</td><td>1</td><td>1</td><td>15</td><td>16</td><td>2</td><td>2</td><td>12</td><td>12</td><td>4</td><td>4</td></tr><tr><td>agent17</td><td>1</td><td>1</td><td>2</td><td>2</td><td>4</td><td>4</td><td>7</td><td>7</td><td>6</td><td>6</td></tr><tr><td>agent18</td><td>11</td><td>11</td><td>2</td><td>2</td><td>1</td><td>1</td><td>6</td><td>6</td><td>3</td><td>3</td></tr><tr><td>agent19</td><td>2</td><td>2</td><td>3</td><td>3</td><td>1</td><td>1</td><td>9</td><td>9</td><td>15</td><td>13</td></tr><tr><td>agent20</td><td>1</td><td>1</td><td>18</td><td>18</td><td>5</td><td>5</td><td>9</td><td>9</td><td>3</td><td>3</td></tr><tr><td>agent21</td><td>11</td><td>11</td><td>1</td><td>1</td><td>2</td><td>2</td><td>4</td><td>4</td><td>8</td><td>8</td></tr><tr><td>agent22</td><td>6</td><td>6</td><td>3</td><td>3</td><td>2</td><td>2</td><td>12</td><td>12</td><td>13</td><td>13</td></tr><tr><td>agent23</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>agent24</td><td>4</td><td>19</td><td>5</td><td>19</td><td>3</td><td>19</td><td>11</td><td>19</td><td>2</td><td>19</td></tr><tr><td>agent25</td><td>2</td><td>19</td><td>4</td><td>19</td><td>7</td><td>19</td><td>1</td><td>19</td><td>8</td><td>19</td></tr><tr><td>agent26</td><td>3</td><td>3</td><td>1</td><td>1</td><td>6</td><td>6</td><td>11</td><td>19</td><td>4</td><td>4</td></tr><tr><td>agent27</td><td>8</td><td>8</td><td>16</td><td>16</td><td>11</td><td>11</td><td>3</td><td>3</td><td>14</td><td>19</td></tr><tr><td>agent28</td><td>2</td><td>2</td><td>5</td><td>4</td><td>1</td><td>1</td><td>3</td><td>3</td><td>4</td><td>4</td></tr><tr><td>agent29</td><td>4</td><td>4</td><td>10</td><td>10</td><td>18</td><td>18</td><td>12</td><td>12</td><td>1</td><td>1</td></tr><tr><td>agent30</td><td>2</td><td>2</td><td>4</td><td>4</td><td>4</td><td>4</td><td>3</td><td>3</td><td>1</td><td>1</td></tr><tr><td>agent31</td><td>3</td><td>3</td><td>13</td><td>13</td><td>16</td><td>19</td><td>8</td><td>19</td><td>2</td><td>2</td></tr></tbody><tfoot><tr><td>Score</td><td>477</td><td>435</td><td>407</td><td>345</td><td>409</td><td>328</td><td>372</td><td>274</td><td>463</td><td>358</td></tr></tfoot></table>

**Figure 11.** Overview of the ranks given by each agent with the potential changes (orange cells mean that the status of the justification is ambiguous, red cells mean that the status of the justification is rejected).
The final results after applying the rank changes are as presented in Table 3. As shown, the final ranking after considering the justifications is: **Global Warming > Human Carcinogenic Toxicity > Water Consumption > Land Use > Freshwater Eutrophication**.

It is evident that the criteria that are the most impacted by the rank sliding approach – Human Carcinogenic Toxicity, Freshwater Eutrophication and Land Use – are those associated with a higher number of challenged arguments. Global warming, on the other hand, maintained its top position. Although it was a source of disagreement in terms of preferences, the majority of supporting arguments for it remained valid. This outcome aligns with the intended effect of the approach: unjustified preferences (those supported by rejected arguments) are not taken into account. For example, Land Use, which was initially close to Water Consumption, dropped in the ranking because it lost significant justified support. These results highlight the impact of considering the strength and validity of justifications in the final ranking. They also demonstrate the potential of the rank sliding approach to encourage agents not only to justify their own rankings but also to critically evaluate those of others. As a result, unsupported rankings are dismissed as personal preferences, while well justified rankings are prioritized.

<table><caption>**Table 3.** LCA impact categories of the NoAW project and their ranking aggregation scores with considering justifications</caption><thead><tr><td>Impact Category</td><td>Total Updated Score</td></tr></thead><tbody><tr><td>Global Warming</td><td>435</td></tr><tr><td>Water Consumption</td><td>345</td></tr><tr><td>Land Use</td><td>328</td></tr><tr><td>Freshwater Eutrophication</td><td>274</td></tr><tr><td>Human Carcinogenic Toxicity</td><td>358</td></tr></tbody></table>

In the energy example, taking justifications into account leads to notable changes in the final ranking (Figure 10). For expected performance levels between 10% and 60%, the ranking becomes: **hydro > geothermal > natural gas > biomass > wind > solar > oil > nuclear > biogas > coal**, which already differs from the unjustified ranking. Starting from 70%, natural gas becomes more important than geothermal, which in turn overtakes hydro. At the same time, solar energy continues to lose its importance. By 90% expected performance, solar ranks as the least important energy source, resulting in the following order: **natural gas > geothermal > hydro > biomass > wind > oil > nuclear > biogas > coal > solar**. A key difference from the unjustified ranking, where biogas and nuclear are consistently ranked higher than oil (which is aligned well with modern environmental concerns), is that in the justified ranking oil is placed above them. This is a less intuitive outcome, but it is a result of balanced decision-making based on explicit arguments and conflicting viewpoints. These findings accompanied by expert argumentation might be a good starting point at a governmental level when discussing future energy policies or potential subsidies.
# 7 Applications and limitations

It is worth noting that while the proposed methodology and its associated tools have been partially used within the context of the ECOBIOCAP project and fully demonstrated within the context of the NoAW project, their potential application is not restricted solely to this particular domain. On the contrary, they hold relevance for any scenario where collective decision making is necessary, provided that the involved knowledge can be formalized within the logical framework we are using. In this regard, our approach can be seen as a formalized and implemented (though somewhat restrictive) counterpart to Public Reason as justified by Rawls (see e.g. (Rawls, 1997)), i.e. a decision might only be selected if it is supported by reasons that any rational individual might accept. Within our approach, we assume that a preference is acceptable if its justifications are not defeated by counter-arguments.

More specifically, we intend to apply this approach to various projects, including the development of new technologies and the selection of food packaging as already presented above on the ECOBIOCAP project. Other preliminary work has also been presented in (Yun et al., 2018a; Yun et al., 2018b), demonstrating how this approach can be used to support the selection of alternative strawberry packaging, considering consumers' preferences and associated justifications based on several criteria.

With regards to the potential areas of application, the context of pathways valorization significantly benefits from incorporating the preferences and justifications of stakeholders. This approach ensures that proposed solutions align with stakeholders' needs, thereby reducing the risk of deviation, even if they are technologically and/or environmentally optimized. A classic example would be a solution that's technically superior (e.g., in terms of productivity) but deemed financially impractical by stakeholders, thus preventing its implementation.

Having said that, while the proposed approach offers a general framework for collective decision support, it does have its limitations. Notably, in a practical setting, one limitation comes from the need for interaction between users and the tool. Specifically, the tool is expecting formalized knowledge as input (using the existential rules framework) to formally assess justifications, a logical language that is not user-friendly for non-experts. To address this, we propose exploring methods to semi-automatically translate user-provided natural language into formal representations. One possible solution is to use a controlled ontology and predefined query patterns for this translation (Pradel et al., 2014).

A major challenge in implementing this approach is the absence of a specialized ontology, which is essential for transforming natural language justifications into formal representations. Developing such an ontology requires domain-specific expertise and the systematic identification of relevant concepts and their relationships. As a step forward, constructing a specialized knowledge graph could support automatic knowledge extraction in the target domain (Yang et al., 2023; Lv et al., 2024). This graph would support the development of a specialized ontology, either during the process or through post-hoc extraction (Böhm et al., 2018). Once the ontology is established, large language models (LLMs) could assist in the automation: textual justifications could be converted into logical expressions using ontology-based concept definitions combined with prompt engineering techniques (Marvin et al., 2023).
Another limitation arises from the assumption that users will provide their true preferences and actively seek to justify them. Our proposed approach aims to favor an environment where uncertainties or errors in modeling, and subsequently in the justifications behind preferences, can be corrected by counter-arguments provided by other users. However, this requires ensuring that all participants have equal opportunities to express themselves and contribute their knowledge. Therefore, it would be interesting to investigate whether our proposed method encourages user involvement and truthfulness when justifications are required, as well as assessing the impact of a moderator that is able to facilitate users' interactions as part of the deliberation process.

# 8 Conclusion

In this article, we proposed an innovative methodology and a set of tools for collective decision-making by evaluating the justifications of rankings on different alternatives. Specifically, we introduced a web-based tool enabling stakeholders to provide justifications and compute contradictions between these justifications, an approach to adjust preferences based on accepted and rejected justifications, and a specific ontology suitable for representing preferences and justifications for LCA impact categories.

While our approach and tools are designed to function in a general context, we showcased their applicability by applying them on the LCA impact categories use case. We proposed a domain ontology tailored to representing justifications regarding preferences on LCA impact categories and assessed the resulting aggregated ranking, with or without accounting for potentially conflicting justifications, using real data from the NoAW project. Our findings showed that the final result varies, indicating that our approach offers benefits such as incentivizing agents to justify their rankings and engage in discussions about other agents' justifications to avoid modifications or discharges of their votes.

In addition to the potential limitations listed, we aim to extend our research further. On the methodological part, we aim to evaluate whether a collective decision is superior, or at least perceived as such by participants, to decisions obtained by direct aggregation of preferences. Furthermore, we recommend exploring various rank sliding approaches alongside other conflict-tolerant reasoning semantics, such as considering the number of supports and attacks for a justification. We also plan to investigate the feasibility of automatically generating summaries of arguments that justify the final ranking to enhance user comprehension. On the practical side, it is imperative to develop an automatic translation module to facilitate the population of the knowledge base by translating users' arguments from natural language to the DLGP logical language. This work is currently being carried out within the framework of the European AgriLoop project. Finally, in terms of applications, we intend to apply our approach to different use cases to assess its flexibility.
# Acknowledgement

The authors acknowledge the support of the European H2020 NoAW project (project ID 688338) and the European Union's Horizon Europe research and innovation programme AgriLoop (project ID 101081776). In addition, they thank Mathieu Roche for providing valuable comments.

# References

Alviano, M., Giordano, L., & Dupre, D. T. (2023). Complexity and scalability of defeasible reasoning with typicality in many-valued weighted knowledge bases. *JELIA*, 2023, 481-497.
https://doi.org/10.1007/978-3-031-43619-2_33

Antoniou, G., Billington, D., Governatori, G., Maher, M. J., & Rock, A. (2000). A family of defeasible reasoning logics and its implementation. In *14th European Conference on Artificial Intelligence*, pp. 459-463. IOS Press.

Baujard, A., Gavrel, F., Igersheim, H., Laslier J. F., & Lebon, I. (2018). How voters use grade scales in evaluative voting. *European Journal of Political Economy*, 55, 14-28.
https://doi.org/10.1016/j.ejpoleco.2017.09.006

Bisquert, P., Buche, P., Hecham, A., & Koptelov, M. (2024). *Justified collective decision-making in agri-food systems: an approach based on computational social choice, argumentation methods and tools (Supplementary Material)*. https://hal.science/hal-04607020

Böhm, K. & Ortiz de la Fuente, M. M. (2018). A tool for building topic-specific ontologies using a knowledge graph. In *Proceedings of the 31st International Workshop on Description Logics*, KR 2018, 12.

Brandt, F., Conitzer, V., Endriss, U., Lang, J., & Procaccia, A. D. (2016). *Handbook of computational social choice*. Cambridge University Press.

Brewka, G., & Woltran, S. (2010). Abstract dialectical frameworks. In *Twelfth International Conference on the Principles of Knowledge Representation and Reasoning*, pp. 102-111.

Cali, A., Gottlob, G., Lukasiewicz, T., Marnette, B., & Pieris, A. (2010). Datalog+/-: A family of logical knowledge representation and query languages for new applications. In *25th Annual IEEE Symposium*, pp. 228-242. IEEE.

Carnielli, W. A., & Marcos, J. (2001). Ex contradictione non sequitur quodlibet. *Bulletin of Advanced Reasoning and Knowledge*, 1, 89-109.

Coetzer, S., & Britz, K. (2022). Debugging classical ontologies using defeasible reasoning tools. *Frontiers in Artificial Intelligence and Applications*, 344, 97-111.
https://doi.org/10.3233/FAIA210374

Cristani, M., Governatori, G., Olivieri, F., Pasetto, L., Tubini, F., Veronese, C., Villa, A., & Zorzi, E. (2022). Houdini (unchained): an effective reasoner for defeasible logic. In *6th Workshop on Advances in Argumentation in Artificial Intelligence*. CEUR.
Cristani, M., Governatori, G., Olivieri, F., & Rotolo, A. (2022). From defeasible logic to counterfactual reasoning. In *International Joint Conference on Rules and Reasoning*, 65-80. Springer.

Cristani, M., Governatori, G., Olivieri, F., Palmirani, M., & Buriola, G (2025). Explainability by design: an experimental analysis of the legal coding process. *arXiv preprint arXiv:2505.01944*.

Dung, P. H. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. *Artificial intelligence*, 77, 2, 321-357. https://doi.org/10.1016/0004-3702(94)00041-X

Eakin, H., Connors, J. P., Wharton, C., Bertmann, F., Xiong, A., & Stoltzfus, J. (2017). Identifying attributes of food system sustainability: emerging themes and consensus. *Agriculture and human values*, 34, 757-773. https://doi.org/10.1007/s10460-016-9754-8

Everett, L., Morris, E., & Meyer, T. (2022). Explanation for KLM-Style defeasible reasoning. In *Southern African Conference for Artificial Intelligence Research*, pp. 192-207. Springer.

García, A. J., & Simari G. R. (2004). Defeasible logic programming: an argumentative approach. *Theory and practice of logic programming*, 4 (1-2), 95-138. https://doi.org/10.1017/S1471068403001674

Gésan-Guiziou, G., Alaphilippe, A., Aubin, J., Bockstaller, C., Boutrou, R., Buche, P., Collet, C., Girard, A., Martinet, V., Membré, J. M., Sabbadin, R., Thiollet-Scholtus, M., & van Der Werf, H. M. (2020). Diversity and potentiality of multi-criteria decision analysis methods for agri-food research. *Agronomy for Sustainable Development*, 2020, 40, 1-11. https://doi.org/10.1007/s13593-020-00650-3

Governatori, G., Maher, M. J., Antoniou, G., & Billington, D. (2004). Argumentation semantics for defeasible logic. *Journal of Logic and Computation* 14, 5, 675–702. https://doi.org/10.1093/logcom/14.5.675

Guillard, V., Buche, P., Destercke, S., Tamani, N., Croitoru, M., Menut, L., Guillaume, C., & Gontard, N. (2015). A decision support system to design modified atmosphere packaging for fresh produce based on a bipolar flexible querying approach. *Computers and Electronics in Agriculture*, 111, 131-139. https://doi.org/10.1016/j.compag.2014.12.010

Hecham, A., Croitoru, M., & Bisquert, P. (2017). Argumentation-based defeasible reasoning for existential rules. In *International Joint Conference on Autonomous Agents and Multiagent Systems*, vol. 3, pp. 1568-1569.

Hecham, A., Bisquert, P., & Croitoru, M. (2018a). On a flexible representation for defeasible reasoning variants. In *17th International Conference on Autonomous Agents and MultiAgent Systems*, pp. 1123-1131.

Hecham, A., Croitoru, M., & Bisquert, P. (2018b). A first order logic benchmark for defeasible reasoning tool profiling. In *International Joint Conference on Rules and Reasoning*, pp. 81-97. Springer.

Hecham, A., Bisquert, P., & Croitoru, M. (2020). A formalism unifying defeasible logics and repair semantics for existential rules. In *25th International Conference on Conceptual Structures*, pp. 3-17. Springer.
Jedwabny, M., Bisquert, P., & Croitoru, M. (2019). PAPOW: Papow aggregates preferences and orderings to select winners. In *International Conference on Autonomous Agents and Multiagent Systems*, pp. 2363-2365.

Karanikolas, N., Bisquert, P., Buche, P., Kaklamanis, C., & Thomopoulos, R. (2018). A decision support tool for agricultural applications based on computational social choice and argumentation. *International Journal of Agricultural and Environmental Information Systems*, 9 (3), 54-73. https://doi.org/10.4018/IJAEIS.2018070104

Lam H. P. (2012). *On the derivability of defeasible logic*. [Doctoral dissertation, University of Queensland, Australia].

Longo, L., Rizzo, L., & Dondio, P. (2021). Examining the modelling capabilities of defeasible argumentation and non-monotonic fuzzy reasoning. *Knowledge-Based Systems*, 211, 106514. https://doi.org/10.1016/j.knosys.2020.106514

Lv, B., Wu, H., Chen, W., Chen, C., Miao, Y., & Zhao, C. (2024). VEG-MMKG: Multimodal knowledge graph construction for vegetables based on pre-trained model extraction. *Computers and electronics in agriculture*, 226, 109398. https://doi.org/10.1016/j.compag.2024.109398

Macharis, C., & Bernardini, A. (2015). Reviewing the use of multi-criteria decision analysis for the evaluation of transport projects: Time for a multi-actor approach. *Transport policy*, 37, 177-186. https://doi.org/10.1016/j.tranpol.2014.11.002

Marvin, G., Hellen, N., Jjingo, D., & Nakatumba-Nabende, J. (2023). Prompt engineering in large language models. In *International conference on data intelligence and cognitive informatics*, 387-402, Springer Nature Singapore.

Meynard, J. M., Jeuffroy, M. H., Le Bail, M., Lefèvre, A., Magrini, M. B., & Michon, C. (2017). Designing coupled innovations for the sustainability transition of agrifood systems. *Agricultural systems*, 157, 330-339. https://doi.org/10.1016/j.agsy.2016.08.002

Pradel, C., Haemmerlé, O., & Hernandez, N. (2014). Swip: A natural language to SPARQL interface implemented with SPARQL. In *International Conference on Conceptual Structures*, pp. 260-274. Springer.

Prakken, H. (2010). An abstract framework for argumentation with structured arguments. *Argument and Computation*, 1 (2), 93-124. https://doi.org/10.1080/19462160903564592

Prakken, H., & Sartor, G. (1997). Argument-based extended logic programming with defeasible priorities. *Journal of applied non-classical logics*, 7, 1-2, 25-75. https://doi.org/10.1080/11663081.1997.10510900

Rawls, J. (1997). The idea of public reason revisited. *The university of Chicago law review*, 64 (3), 765-807.

Sohn, J., Bisquert, P., Buche, P., Hecham, A., Kalbar, P., Goldstein, B., Birkved, M., & Olsen, S. I. (2020) Argumentation corrected context weighting-life cycle assessment: a practical method of including stakeholder perspectives in multi-criteria decision support for LCA. *Sustainability*, 12 (6), 2170. https://doi.org/10.3390/su12062170
Triste, L., Marchand, F., Debruyne, L., Meul, M., & Lauwers, L. (2014). Reflection on the development process of a sustainability assessment tool: learning from a Flemish case. *Ecology and Society*, 19 (3). http://doi.org/10.5751/ES-06789-190347

Wan, H., Kifer, M., & Grosof, B. (2015). Defeasibility in answer set programs with defaults and argumentation rules. *Semantic Web*, 6 (1), 81-98. https://doi.org/10.3233/SW-140140

Yang, W., Yang, S., Wang, G., Liu, Y., Lu, J., & Yuan, W. (2023). Knowledge graph construction and representation method for potato diseases and pests. *Agronomy*, 14 (1), 90. https://doi.org/10.3390/agronomy14010090

Yun, B., Bisquert, P., Buche, P., Croitoru, M., Guillard, V., & Thomopoulos R. (2018a). Choice of environment-friendly food packagings through argumentation systems and preferences. *Ecological Informatics*, 48, 24-36. https://doi.org/10.1016/j.ecoinf.2018.07.006

Yun, B., Buche, P., Bisquert, P., Costa, S., Croitoru M., Cufi, J., Guillard, V., Oudot, A., & Thomopoulos, R. (2018b). Consumer perception data and scientific arguments about food packaging functionalities for fresh strawberries. *Data in Brief*, 20, 1924-1927. https://doi.org/10.1016/j.dib.2018.09.034