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This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes' weights and the alternatives' scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments. • We present an integrated MADM and DEA model for problems with heterogeneous attributes. • Heterogeneous attributes are classified into desirable and undesirable categories. • Our model does not rely on the weights defined by the experts to determine the ranking. • The rankings derived remain stable independently of the normalization formula applied. • We study the fulfillment of Sustainable Development Goals among European countries. [ABSTRACT FROM AUTHOR]

The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability. [ABSTRACT FROM AUTHOR]

It is generally assumed that the rankings provided by Multi-Attribute Decision-Making (MADM) techniques are definitive. Once the ranking is delivered, decision makers (DMs) are expected to choose the first alternative and dismiss the remaining ones, concluding the application of the corresponding model. The MADM literature has incorporated fuzziness and imprecision to its models to deal with evaluation uncertainties but has not accounted for its consequences defined in terms of regrettable choices. That is, MADM models do not consider the possible consequences of having chosen an alternative whose actual characteristics do not correspond to those expected by the DM. This paper aims at designing an integrated MADM framework with interval variables where the DM is allowed to modify the initial alternative chosen after observing the realizations of its characteristics. In order to do so, sequences of alternatives including the initial choice as well as subsequent alternate choices should be ranked in place of single alternatives. We analyze the combinatorial decision environment that arises from defining and evaluating sequences of choices by accounting for the whole set of potential realizations and any subsequent change in the alternatives selected. The TOPSIS method is used to design the integrate evaluation framework producing the final ranking. A case study analyzing the entry decision of a firm within a group of European countries based on their levels of ICT development is presented. We illustrate how the countries selected and their order may differ substantially when accounting for the complementarities existing among them. Moreover, the selection process and any subsequent decision vary with the number of modifications considered relative to the initial country selected. The results obtained are of interest not only to firms facing a similar problem, but also to DMs or managers dealing with strategic selection processes where the wrong choice of alternatives may lead to increasingly complex sequential disruptions. • We analyze a Multi-Attribute Decision-Making setting with interval evaluations. • We formalize the regrettable choices inherent to the uncertain evaluations received. • The resulting choice sequences account for combinations of all potential realizations. • Sequences are determined by the number of modifications in the alternatives selected. • We study the entry decision of firms based on the ICT development level of countries. [ABSTRACT FROM AUTHOR]

The current paper analyzes the formation of social networks determined by the preferences of their users, who are endowed with incomplete information regarding the characteristics of other users from who they receive friendship requests. The acceptance or rejection decision is determined by the limited information available when receiving the requests, the expectations of the users regarding the remaining characteristics of the requesters and the resulting improvement in network capacity derived from accepting the friendship requests. We illustrate how the similarity in preferences among users leads to more concentrated clusters within the incomplete information scenario analyzed. At the same time, the emergence of disutility costs derived from a suboptimal decision when accepting to interact with other users increments the dispersion between clusters. In this regard, the inclusion of requesters endowed with average preferences relative to those of the standard users composing the network acts as a connectivity-enhancing mechanism designed to reduce the dispersion and differences existing between clusters. [ABSTRACT FROM AUTHOR]

The current paper presents a theoretical model where rational decision makers (DMs) observe credible signals regarding the existence of technologically superior products and generate the demand structure determining their evolution within the market. We will illustrate how consumers may stick to an inferior product when market frictions or their own expectations dictate them to do so. This will be the case even if the newcomer firm credibly guarantees an improvement upon the main characteristics of the incumbent product. Indeed, the prevalence of a suboptimal technology can be the result of the correct choice being made at a given point in time. Moreover, we will compute the expected prevalence of a given product in the market when information regarding the existence of a technologically superior product spreads across consumers following different diffusion processes. The consequences derived from the existence of path dependence phenomena will be analysed from a dynamic perspective by explicitly accounting for the emergence of network effects that may take place after firms signal the availability of a technologically superior set of products. [ABSTRACT FROM AUTHOR]

ABSTRACT Most real-life decisions are made with less than perfect information and there is often some opportunity to acquire additional information to increase the quality of the decision. In this article, we define and study the sequential information acquisition process of a rational decision maker (DM) when allowed to acquire any finite amount of information from a set of products defined by vectors of characteristics. The information acquisition process of the DM depends both on the values of the characteristics observed previously and the number and potential realizations of the remaining characteristics. Each time an observation is acquired, the DM modifies the probability of improving upon the products already observed with the number of observations available. We construct two real-valued functions whose crossing points determine the decision of how to allocate each available piece of information. We provide several numerical simulations to illustrate the information acquisition incentives defining the behavior of the DM. Applications to knowledge management and decision support systems follow immediately from our results, particularly when considering the introduction and acceptance of new technological products and when formalizing online search environments. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach – The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained. Findings – The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs. Originality/value – The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered. [ABSTRACT FROM AUTHOR]

Abstract: We consider a rational utility maximizer decision maker (DM) who must gather two pieces of information from a set of multidimensional products before making a choice. We analyze the resulting sequential information acquisition process where the DM tries to find the best possible product subject to his information acquisition constraint. In addition, we introduce publicly observable signals that allow the DM to update his expected utility functions following a standard Bayesian learning rule. Even though it seems intuitively plausible to assume that the transmission of positive and credible information may lead DMs to accept any product signaled more eagerly, this paper illustrates how transmitting credible positive information is not sufficient to decrease the rejection probability faced by the information sender on its set of products. A significant difference in product rejection probabilities arises depending on the characteristic on which signals are issued, as will be illustrated numerically for both risk-neutral and risk-averse DMs. [Copyright &y& Elsevier]

• A decision maker (DM) observes the numerical value assigned by raters to an alternative. • The DM defines an uncertain interval within which the evaluation observed is contained. • Its width depends on the subjective perception differences between the DM and the raters. • The DM may improve upon an uncertain evaluation by retrieving more uncertain information. • Several scenarios are built based on the capacity of the DM to fully assimilate uncertainty. We formalize the information acquisition and choice structure of a decision maker (DM) when the main characteristics defining the alternatives are not directly observed but numerical evaluations of unknown quality are provided by external raters. The DM observes the overall numerical value assigned by the raters to an alternative and defines an uncertain interval within which the evaluation observed is contained. The width of the interval is determined by the subjective perception and evaluation differences existing between the DM and the raters transmitting the information. We analyze the incentives of the DM to improve upon an evaluation contained within an uncertain interval by retrieving further information from the raters of other alternatives. Different scenarios will be developed based on the ability of the DM to fully assimilate uncertainty and the introduction of heuristic approximations to account for the potential frictions arising from uncertainty. One of the main qualities of the current framework is its capacity to formalize interactions among alternatives determined by interval width differences across their characteristics, providing an analytical advantage over the operational complexity involved in the use of fuzzy and intuitionistic fuzzy sets. The same remark applies to the formalization of the interactions across attributes that must be considered when defining sequential decision processes or dynamical systems while dealing with multiple sources of uncertainty. Numerical simulations are provided to compare the different scenarios developed and describe the main consequences derived from ignoring the uncertainty inherent to the evaluations received. In particular, we illustrate the ranking consequences derived from increasing the spread of the evaluation uncertainty, an effect that can be easily combined with the risk attitude exhibited by DMs. The inclusion of both these features bridges the gap between economics, psychology and multiple criteria decision making, whose techniques do not generally account for these differences among DMs. [ABSTRACT FROM AUTHOR]

Highlights: [•] Information acquisition is essential for sustaining a firm’s competitive advantage. [•] We analyze the strategic side of the information acquisition process. [•] Decision makers (DMs) are allowed to exhibit any type of risk attitude. [•] Managers can estimate our model using linguistic evaluations obtained from DMs. [•] Our model can be implemented within several multicriteria decision making methods. [Copyright &y& Elsevier]