A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation

A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation

Nikos Karanikolas, Pierre Bisquert, Patrice Buche, Christos Kaklamanis, Rallou Thomopoulos
DOI: 10.4018/IJAEIS.2018070104
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In the current article, the authors describe an applied procedure to support collective decision making for applications in agriculture. An extended 2-page abstract of this paper has been accepted by the EFITA WCCA congress and this manuscript is an extended version of this submission. The problem the authors are facing in this paper is how to reach the best decision regarding issues coming from agricultural engineering with the aid of Computational Social Choice (CSC) and Argumentation Framework (AF). In the literature of decision-making, several approaches from the domains of CSC and AF have been used autonomously to support decisions. It is our belief that with the combination of these two fields the authors can propose socially fair decisions which take into account both (1) the involved agents' preferences and (2) the justifications behind these preferences. Therefore, this article implements a software tool for decision-making which is composed of two main systems, i.e., the social choice system and the deliberation system. In this article, the authors describe thoroughly the social choice system of our tool and how it can be applied to different alternatives on the valorization of materials coming from agriculture. As an example, that is demonstrated an application of our tool in the context of Ecobiocap European project where several decision problems are to be addressed. These decision problems consist in finding the best solutions for questions regarding food packaging and end-of-life management.
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1. Introduction

Collective decision-making and preference aggregation are widely used in modern societies. The most well-known example of collective decision-making is political elections but it is not the only one since there are a number of situations where the aggregation of the individual preferences is needed to take a decision. Consider for example situations such as a group of friends choosing where to have dinner or a committee board choosing what is the best strategy for the company. In all these cases what we are seeking is a way to fairly aggregate the preferences of the individual agents into a collective preference and thus obtain a decision which satisfies the group as a total. This setting can be directly applied also in the decision-making for agricultural problems. Such an example is a problem where the decision lays in evaluating the interest and potential of marketing new generation packaging made of agro-waste materials. Here, the consumers are asked to express preferences among different packaging.

We are studying the classical collective decision-making problem, where we have a set of alternative options and a set of agents and each agent is called to express her preference over the alternatives by producing a linear order on them. In most of these collective decision-making problems the preference aggregation is done by using simple aggregation methods, such as the plurality voting rule, and the tools that are used are simple and not even intended to serve this purpose. For example, doodle is one such unsuitable tool that is used for preference aggregation while its original functionality was for scheduling joint activities. Therefore, we propose a procedure for supporting more complex collective decision-making problems, which can be directly applied in the context of agricultural applications. Our objective is to expand this classical collective decision-making problem by asking the agents to consider the reasoning behind the linear ordering of the alternatives. Hence, the goal of this work is to build, i.e., design and implement, a software tool for decision-making that takes into account theoretical insights from social choice and argumentation in order to propose social fair decisions that take into account the preferences of the agents and the reasoning behind these preferences.

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