Reasons and Means to Model Preferences as Incomplete

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences

Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical-econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques-that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests

Machine learning-driven approach for large scale decision making with the analytic hierarchy process

The Analytic Hierarchy Process (AHP) multicriteria method can be cognitively demanding for large-scale decision problems due to the requirement for the decision maker to make pairwise evaluations of all alternatives. To address this issue, this paper presents an interactive method that uses online learning to provide scalability for AHP. The proposed method involves a machine learning algorithm that learns the decision maker’s preferences through evaluations of small subsets of solutions, and guides the search for the optimal solution. The methodology was tested on four optimization problems with different surfaces to validate the results. We conducted a one factor at a time experimentation of each hyperparameter implemented, such as the number of alternatives to query the decision maker, the learner method, and the strategies for solution selection and recommendation. The results demonstrate that the model is able to learn the utility function that characterizes the decision maker in approximately 15 iterations with only a few comparisons, resulting in significant time and cognitive effort savings. The initial subset of solutions can be chosen randomly or from a cluster. The subsequent ones are recommended during the iterative process, with the best selection strategy depending on the problem type. Recommendation based solely on the smallest Euclidean or Cosine distances reveals better results on linear problems. The proposed methodology can also easily incorporate new parameters and multicriteria methods based on pairwise comparisons.This research was funded by National Funds through the FCT—Portuguese Foundation for Science and Technology, References UIDB/05256/2020 and UIDP/05256/2020

Methodological review of multicriteria optimization techniques: aplications in water resources

Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use

Estimating utility functions of Greek dairy sheep farmers: A multicriteria mathematical programming approach

Mathematical programming models are commonly used to approach decision making in livestock farms. The majority of these models assume gross margin maximisation as the sole objective of farmers. In this study an alternative multicriteria model is built to test the hypothesis of the multiplicity of the objectives of Greek sheep farmers. A farm typology is constructed to account for diversified farm structures and a non-interactive methodology is used to elicit the utility function of farmers. The results of the analysis indicate that the multicriteria model allows for a better representation of the farms, compared to the gross margin maximisation model

The Decision-Conflict and Multicriteria Logit

We study two tractable random non-forced choice models that explain behavioural patterns suggesting that the choice-deferral outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable. The *decision-conflict logit* extends the logit model with an outside option by assigning a menu-dependent value to that option. This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it. The *multicriteria logit* is a special case of this model and introduces multiple utility functions that jointly predict behaviour in a multiplicative-logit way. Every multicriteria logit admits a simple discrete-choice formulation

A multicriteria hierarchical discrimination approach for credit risk problems

Recently, banks and credit institutions have shown an increased interest in developing and implementing credit-scoring systems for taking corporate and consumer credit granting decisions. The objective of such systems is to analyze the characteristics of each applicant (firm or individual) and support the decision making process regarding the acceptance or the rejection of the credit application. This paper addresses this problem through the use of a multicriteria classi - fication technique, the M.H.DIS method (Multi-group Hierarchical DIScrimination). M.H.DIS is applied to real-world case studies regarding the assessment of corporate credit risk and the evaluation of credit card applications. The results obtained through the M.H.DIS method are compared to the results of three wellknown statistical techniques, namely linear and quadratic discriminant analysis, as well as logit analysis.peer-reviewe