Learning the preferences of physicians for the organization of result lists of medical evidence articles

Objectives Despite many clinical decision support systems (CDSSs) being rated as highly usable, CDSSs have not been widely adopted in clinical practice. We posit that there are factors aside from usability that impact adoption of CDSSs; in particular we are interested in the role played by MDs intrinsic motivation to use computer-based support. Our research aim is to investigate the relationship between usability and intrinsic motivation in order to learn about adoption of CDSS in clinical practice. Methods Following the evaluation of a CDSS, 19 MDs completed a 2 part questionnaire about their intrinsic motivation to use computer-based support in general and the usability of the evaluated CDSS. Results The analysis of MDs motivation to use computer-based support demonstrated that MDs are comfortable using computer-based support and in general find using it quite easy (a motivation rating of 0.66 on a (0, 1) scale was computed). However MDs also reported a perceived lack of competence associated with a lack of prior experience using technology in practice, which results in pressure and tension. The considered CDSS scored highly on all usability dimensions and a usability rating of 0.74 was recorded. The examination of the relationship between motivation and usability suggested that users who were motivated to use computer-based support experienced better usability than those who reported low levels of motivation. Conclusions Our small case study suggests that an important factor supplementing the usability of CDSSs is intrinsic motivation to use computer-based support in general. We posit that the lack of such a measure thus far in CDSS evaluation may to some extent explain seeming MD satisfaction with CDSSs on one hand, but their limited adoption on the other. We recommend that clinical managers responsible for deploying CDSS should invest in training MDs to use technology underlying computer-based support applications instead of focusing only on the features of the specific CDSS to be deployed

Proper and improper uses of MCDA methods in energy systems analysis

Global Challenges (FGGA

Dominance-based rough set analysis for understanding the drivers of urban development agreements

The rise of neoliberalism in the context of urban development has en- couraged the cooperation between public and private parties. This co- operation is structured by contracts, generally called Urban Develop- ment Agreements (DAs). Being part of the urban regeneration strate- gies, these projects aim at achieving a durable improvement of an area according to sustainability principles. Thus, within the negotiation be- tween private and public, multiple and conflicting instances have to be faced case by case. Despite the uniqueness of each DA, it is possi- ble to define a set of pertinent characteristics that play a crucial role in determining the fairness and appropriateness of the public-private part- nership. Given this context, the work proposes the Dominance Rough Set Approach (DRSA) for exploring the relationship between condi- tion attributes or criteria and decision with the aim of supporting ne- gotiations on the basis of specific features of the DA under evaluation. Specifically, DRSA has been applied on a sample of DAs recently con- cluded in the Lombardy Region, and tested on the other sample of DAs under the negotiation phase. The analysis has accounted for the char- acteristics referring to the following five contexts: urban, institutional, negotiation, development, and economic. The inferred decision rules provide useful knowledge for supporting complex decision processes such as DAs

Robust multi-criteria ranking with additive value models and holistic pair-wise preference statements

We consider a problem of ranking alternatives based on their deterministic performance evaluations on multiple criteria. We apply additive value theory and assume the Decision Maker¿s (DM) preferences to be representable with general additive monotone value functions. The DM provides indirect preference information in form of pair-wise comparisons of reference alternatives, and we use this to derive the set of compatible value functions. Then, this set is analyzed to describe (1) the possible and necessary preference relations, (2) probabilities of the possible relations, (3) ranges of ranks the alternatives may obtain, and (4) the distributions of these ranks. Our work combines previous results from Robust Ordinal Regression, Extreme Ranking Analysis and Stochastic Multicriteria Acceptability Analysis under a unified decision support framework. We show how the four different results complement each other, discuss extensions of the main proposal, and demonstrate practical use of the approach by considering a problem of ranking 20 European countries in terms of 4 criteria reflecting the quality of their universities

Robust multi-criteria ranking with additive value models and holistic pair-wise preference statements

We consider a problem of ranking alternatives based on their deterministic performance evaluations on multiple criteria. We apply additive value theory and assume the Decision Maker¿s (DM) preferences to be representable with general additive monotone value functions. The DM provides indirect preference information in form of pair-wise comparisons of reference alternatives, and we use this to derive the set of compatible value functions. Then, this set is analyzed to describe (1) the possible and necessary preference relations, (2) probabilities of the possible relations, (3) ranges of ranks the alternatives may obtain, and (4) the distributions of these ranks. Our work combines previous results from Robust Ordinal Regression, Extreme Ranking Analysis and Stochastic Multicriteria Acceptability Analysis under a unified decision support framework. We show how the four different results complement each other, discuss extensions of the main proposal, and demonstrate practical use of the approach by considering a problem of ranking 20 European countries in terms of 4 criteria reflecting the quality of their universities

Stochastic ordinal regression for multiple criteria sorting problems

We present a new approach for multiple criteria sorting problems. We consider sorting procedures applying general additive value functions compatible with the given assignment examples. For the decision alternatives, we provide four types of results: (1) necessary and possible assignments from Robust Ordinal Regression (ROR), (2) class acceptability indices from a suitably adapted Stochastic Multicriteria Acceptability Analysis (SMAA) model, (3) necessary and possible assignment-based preference relations, and (4) assignment-based pair-wise outranking indices. We show how the results provided by ROR and SMAA complement each other and combine them under a unified decision aiding framework. Application of the approach is demonstrated by classifying 27 countries in 4 democracy regimes

Stochastic ordinal regression for multiple criteria sorting problems

We present a new approach for multiple criteria sorting problems. We consider sorting procedures applying general additive value functions compatible with the given assignment examples. For the decision alternatives, we provide four types of results: (1) necessary and possible assignments from Robust Ordinal Regression (ROR), (2) class acceptability indices from a suitably adapted Stochastic Multicriteria Acceptability Analysis (SMAA) model, (3) necessary and possible assignment-based preference relations, and (4) assignment-based pair-wise outranking indices. We show how the results provided by ROR and SMAA complement each other and combine them under a unified decision aiding framework. Application of the approach is demonstrated by classifying 27 countries in 4 democracy regimes

A multi-criteria inference approach for anti-desertification

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts

Optimization of multiple satisfaction levels in portfolio decision analysis

We consider a portfolio decision problem in which a set of projects forming a portfolio has to be selected taking into account multiple evaluation criteria and some constraints related to the limited resources (e.g., available budget). Traditionally, such a problem has been approached by Multiple Attribute Value Theory (MAVT) with the aim of maximizing the sum of values associated with the projects included in the selected portfolio. Using MAVT, one represents preferences on the individual projects, and a value of a portfolio is just an aggregate of values of the component projects. This linear value approach does not explicitly account for portfolio balance requirements, raising the risk of selecting a portfolio which is, e.g., composed of projects with good evaluations on the same criterion or on the same small subset of criteria. Thus, we propose a different approach that enables the Decision Maker (DM) to control the distribution of good evaluations on different criteria over the projects composing a portfolio. With this aim, for each criterion we fix a certain number of reference levels corresponding to the qualitative satisfaction degrees. The number of projects entering a portfolio and attaining each of these levels becomes an objective to be maximized. To solve thus formulated multi-objective optimization problem, we use Dominance-based Rough Set Approach (DRSA). The DM is expected to point out some prospective portfolios in a current sample of non-dominated portfolios. DRSA represents the DM's preferences with a set of decision rules induced from such indirect preference information. Their use permits to progressively focus the search on the part of the non-dominated portfolios that satisfy the DM's preferences in the best way

Robust multi-criteria sorting with the outranking preference model and characteristic profiles

We present a new multiple criteria sorting approach that uses characteristic profiles for defining the classes and outranking relation as the preference model, similarly to the Electre Tri-C method. We reformulate the conditions for the worst and best class assignments of Electre Tri-C to increase comprehensibility of the method and interpretability of the results it delivers. Then, we present a disaggregation procedure for inferring the set of outranking models compatible with the given preference information, and use the set in deriving, for each decision alternative, the necessary and possible assignments. Furthermore, we introduce simplified assignment procedures and prove that they maintain a no class jumps-property in the possible assignments. Application of the proposed approach is demonstrated by classifying 40 land zones in 4 classes representing different risk levels