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.

A Preference Structure in Multi-attribute Decision Making – an Algorithmic Approach Based on Hesitant Fuzzy Sets

This paper introduces a new methodology for solving Multi-Attribute Decision Making (MADM) problems under hesitant fuzzy environment. The uncertainty in Hesitant Fuzzy Elements (HFE) are derived by means of entropy. The resulting uncertainty is subsequently used in HFE to derive a single representative value (RV) of alternatives in each attribute. Our work transforms the RVs into their linguistic counterparts and then formulates a methodology for pairwise comparison of the alternatives via their linguistically defines RVs. The Eigen vector corresponding to maximum Eigen value of the pairwise comparison matrix prioritize the alternatives in each attribute. The priority vectors of the alternatives are aggregated to derive the weights of the attributes using Quadratic programming. The weighted aggregation of the attribute values provides the ranking of the alternatives in MADM. An algorithm is written to validate the procedure developed. The proposed methodology is compared with similar existing methods and the advantages of our method are presented. The robustness of our methodology is demonstrated through sensitivity analysis. To highlight the procedure a car purchasing problem is illustrated.

Interdependent Altruistic Preference Models

Altruistic preferences or the desire to improve the well‐being of others even at one’s own expense can be difficult to incorporate into traditional value and utility models. It is straightforward to construct a multiattribute preference structure for one decision maker that includes the outcomes experienced by others. However, when multiple individuals incorporate one another’s well‐being into their decision making, this creates complex interdependencies that must be resolved before the preference models can be applied. We provide representation theorems for additive altruistic value functions for two-person, n-person, and group outcomes in which multiple individuals are altruistic. We find that in most cases it is possible to resolve the preference interdependencies and that modeling the preferences of altruistic individuals and groups is tractable.

Preference Structure on the Design of Hydrogen Refueling Stations to Activate Energy Transition

As a countermeasure to the greenhouse gas problem, the world is focusing on alternative fuel vehicles (AFVs). The most prominent alternatives are battery electric vehicles (BEV) and fuel cell electric vehicles (FCEVs). This study examines FCEVs, especially considering hydrogen refueling stations to fill the gap in the research. Many studies suggest the important impact that infrastructure has on the diffusion of AFVs, but they do not provide quantitative preferences for the design of hydrogen refueling stations. This study analyzes and presents a consumer preference structure for hydrogen refueling stations, considering the production method, distance, probability of failure to refuel, number of dispensers, and fuel costs as core attributes. For the analysis, stated preference data are applied to choice experiments, and mixed logit is used for the estimation. Results indicate that the supply stability of hydrogen refueling stations is the second most important attribute following fuel price. Consumers are willing to pay more for green hydrogen compared to gray hydrogen, which is hydrogen produced by fossil fuels. Driver fuel type and perception of hydrogen energy influence structure preference. Our results suggest a specific design for hydrogen refueling stations based on the characteristics of user groups.