Effects of imidazoline agents in a rat conditioned place preference model of addiction

Agmatine (AG), idazoxan (IDZ), and efaroxan (EFR) are imidazoline receptor ligands with beneficial effects in central nervous system disorders. The present study aimed to evaluate the interaction between AG, IDZ, and EFR with an opiate, tramadol (TR), in a conditioned place preference (CPP) paradigm. In the experiment, we used five groups with 8 adult male Wistar rats each. During the condition session, on days 2, 4, 6, and 8, the rats received the drugs (saline, or TR, or IDZ and TR, or EFR and TR, or AG and TR) and were placed in their least preferred compartment. On days 1, 3, 5, and 7, the rats received saline in the preferred compartment. In the preconditioning, the preferred compartment was determined. In the postconditioning, the preference for one of the compartments was reevaluated. TR increased the time spent in the non-preferred compartment. AG decreased time spent in the TR-paired compartment. EFR, more than IDZ, reduced the time spent in the TR-paired compartment, but without statistical significance. AG reversed the TR-induced CPP, while EFR and IDZ only decreased the time spent in the TR-paired compartment, without statistical significance.

Road Safety Resource Allocation Using Interactive Multiobjective Optimization

A model is proposed for allocating safety resources to various hazard sites. Due to budget constraints, allocation of resources for necessary countermeasures is a critical issue in safety improvement programs. Therefore, the Decision Maker needs a tool that can prioritize the identified countermeasures looking at several objectives, the most important of which are: reducing the number of accidents and minimizing the costs. A number of countermeasures could be implemented simultaneously in the same location and this was considered, so that the solution that best optimizes the objectives was selected. Since the considered objectives are not commensurable, a new methodology with interactive multi-objective optimization in the case of 0-1 integer variables was proposed, based on the application of a logical preference model built using dominance-based Rough Set Approach (IMO-DRSA). Finally, an application of the methodology is presented considering a sample of Italian urban intersections and a set of mutually exclusive alternatives at each location.

Visual Harmony of Engineering Structures in a Mountain Stream

This study uses the cognitive factor of “visual harmony” to assess the visual quality of stream engineering in a mountainous region. Images of engineering structures such as revetments and submerged dams in the mountain streams of Taiwan were collected. Three image groups with different structures invaded by vegetation were used for a questionnaire survey, which yielded 154 valid samples. We used statistical analysis to develop a model of visual harmony H with respect to the percentage of visible greenery GR, that is, the perceived curve of vegetation change. A comparison of our data with the literature determined the upper and lower bound curves of the relationship between H and GR. We found that the physical elements of “softscape” and “hardscape”—namely, percentage of visible water WR, visible structure IR, and visible natural material on the structure NR—affected this relationship. Results show that H is equivalent to visual preference P, and both can be improved by better green visibility (increasing GR and GR < 50%), avoiding low water visibility (WR < 10%), or increasing the amount of visible natural material (NR > 0.9). High visibility of the structures (IR > 0.3) may decrease H and P. We ultimately propose a visual harmony or preference model concerning a combined physical indicator that comprises GR, WR, IR and NR. Results of this study could be helpful to improve or access the aesthetics of stream engineering design.

A Self-Learning Based Preference Model for Portfolio Optimization

An investment in a portfolio can not only guarantee returns but can also effectively control risk factors. Portfolio optimization is a multi-objective optimization problem. In order to better assist a decision maker to obtain his/her preferred investment solution, an interactive multi-criterion decision making system (MV-IMCDM) is designed for the Mean-Variance (MV) model of the portfolio optimization problem. Considering the flexibility requirement of a preference model that provides a guiding role in MV-IMCDM, a self-learning based preference model DT-PM (decision tree-preference model) is constructed. Compared with the present function based preference model, the DT-PM fully considers a decision maker’s bounded rationality. It does not require an assumption that the decision maker’s preference structure and preference change are known a priori and can be automatically generated and completely updated by learning from the decision maker’s preference feedback. Experimental results of a comparison show that, in the case that the decision maker’s preference structure and preference change are unknown a priori, the performances of guidance and fitness of the DT-PM are remarkably superior to function based preference models; in the case that the decision maker’s preference structure is known a priori, the performances of guidance and fitness of the DT-PM is approximated to the predefined function based model. It can be concluded that the DT-PM can agree with the preference ambiguity and the variability of a decision maker with bounded rationality and be applied more widely in a real decision system.

Social discount rate: spaces for agreement

We study the problem of aggregating discounted utility preferences into a social discounted utility preference model. We use an axiom capturing a social responsibility of individuals’ attitudes to time, called consensus Pareto. We show that this axiom can provide consistent foundations for welfare judgments. Moreover, in conjunction with the standard axioms of anonymity and continuity, consensus Pareto can help adjudicate some fundamental issues related to the choice of the social discount rate: the society selects the rate through a generalized median voter scheme.

How Does Uncertainty Affect Voters' Preferences?

Rational voters care about outcomes, while parties campaign on policy proposals, the outcomes of which are never perfectly known. Can parties exploit this uncertainty to shape public opinion? This article presents a spatial preference model for policy proposals with uncertain outcomes. It reports the results of a large pre-registered survey experiment that involved presenting respondents with predictions about the effects of three policy proposals. The findings show that respondents update their attitudes to the proposals as their beliefs about outcomes change, and that parties are no less able to influence beliefs than non-partisan experts. Contrary to previous research, respondents discount outcome uncertainty by giving equal weight to conflicting optimistic and pessimistic predictions. The study shows that parties can shape public opinion by influencing voter beliefs, and that voters are not repelled by the uncertainty inherent in conflicting information.

The hierarchical SMAA-PROMETHEE method applied to assess the sustainability of European cities

Measuring the level of sustainability taking into account many contributing aspects is a challenge. In this paper, we apply a multiple criteria decision aiding framework, namely, the hierarchical-SMAA-PROMETHEE method, to assess the environmental, social, and economic sustainability of 20 European cities in the period going from 2012 to 2015. The application of the method is innovative for the following reasons: (i) it permits to study the sustainability of the mentioned cities not only comprehensively but also considering separately particular macro-criteria, providing in this way more specific information on their weak and strong points; (ii) the use of PROMETHEE and, in particular, of PROMETHEE II, avoids the compensation between different and heterogeneous criteria, that is arbitrarily assumed in value function aggregation models; finally, (iii) thanks to the application of the Stochastic Multicriteria Acceptability Analysis, the method provides more robust recommendations than a method based on a single instance of the considered preference model compatible with few preference information items provided by the Decision Maker.