Information avoidance

Information is normally considered as a mean to a desired end. However, a growing theoretical and experimental literature suggested that information may directly enter the agent’s utility function. This can create an incentive to avoid information, even when it is useful, free, and independent of strategic considerations. In this study, the researchers have reviewed information avoidance, as well as theoretical and empirical researches that discussed the reasons why people avoid information, depending on the economical researches, psychological researches, and other disciplines. The study also discussed some of the diverse (and often costly) individual and societal consequences of information avoidance.

Combinatorial Design of the MAUT and PAMSSEM II Methods for Multiple Attributes Group Decision Making with Probabilistic Linguistic Information

This paper considers the application of probabilistic linguistic term sets (PLTS) in multiple-attribute group decision-making (MAGDM) when the weights can’t be determined. First, as an improvement of the PROMETHEE method, the PAMSSEM method can not only handle missing evaluations, but also proposes a rejection threshold to calculate the overall consistency of the plan, so as to rank the plan more reasonably. At the same time, the MAUT uses the marginal utility function to reallocate the attribute values of the alternatives in the interval , and then calculate the total utility to sort them. Because the utility function is beneficial in expressing consumer satisfaction, we combine the MAUT method and PAMSSEM II method and apply it to solve decision-making problems under probabilistic linguistic environment. Secondly the coefficient of variation method, entropy method and analytic hierarchy process are used to calculate the weights in a combination. In the process of data processing, we use the transfer function to convert the PLTS into the hesitant probabilistic fuzzy set (HPFS) for calculation. Finally, the PL-MAUT-PAMSSEM II method, PROMRTHE method, TOPSIS method and ARAS method are compared with each other.

Navigation by Revealing Trade-offs for Content-Based Recommendations

Conversational recommender systems have been introduced to provide users the opportunity to give feedback on items in a turn-based dialog until a final recommendation is accepted. Tourism is a complex domain for recommender systems because of high cost of recommending a wrong item and often relatively few ratings to learn user preferences. In a scenario such as recommending a city to visit, conversational content-based recommendation may be advantageous, since users often struggle to specify their preferences without concrete examples. However, critiquing item features comes with challenges. Users might request item characteristics during recommendation that do not exist in reality, for example demanding very high item quality for a very low price. To tackle this problem, we present a novel conversational user interface which focuses on revealing the trade-offs of choosing one item over another. The recommendations are driven by a utility function that assesses the user’s preference toward item features while learning the importance of the features to the user. This enables the system to guide the recommendation through the search space faster and accurately over prolonged interaction. We evaluated the system in an online study with 600 participants and find that our proposed paradigm leads to improved perceived accuracy and fewer conversational cycles compared to unit critiquing.

Integration of Different Mobility Behaviors and Intermodal Trips in MATSim

MATSim is an open-source simulation framework for mesoscopic traffic simulations that has gained popularity in recent years. In this paper, we present a MATSim model for the city of Vienna, with a particular emphasis on the intermodal routing framework used to create agent trips, and the development of a utility function to specify different agents’ mode preferences. To create agent activity chains, we use mobility diaries from the national transportation survey in Austria and disaggregate the available geospatial information to best fit the reported travel times. The novelty of the intermodal framework is the ability to create trips that do not consist of only one mode of transportation, but to also include bicycle, car, and demand-responsive transport (e.g., cab, car sharing) trips in combination with public transportation. To represent the different mobility behaviors of agents, we divide the population into groups and assign them different utility functions for transportation modes according to their socio-demographic characteristics. After presenting the validation of the model, we discuss ways to improve the model.

NON-CENTRAL MOMENTS OF THE TRUNCATED NORMAL VARIABLE IN FINANCE

This paper computes the Non-central Moments of the Truncated Normal variable, i.e. a Normal constrained to assume values in the interval with bounds that may be finite or infinite. We define two recursive expressions where one can be expressed in closed form. Another closed form is defined using the Lower Incomplete Gamma Function. Moreover, an upper bound for the absolute value of the Non-central Moments is determined. The numerical results of the expressions are compared and the different behavior for high value of the order of the moments is shown. The limitations to the use of Truncated Normal distributions with a lower negative limit regarding financial products are considered. Limitations in the application of Truncated Normal distributions also arise when considering a CRRA utility function.

Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.

Funkcja użyteczności — krytyka

The utility function has long been the main tool used by many economists due to the mathematization of economics. Some textbooks on mathematical economics state that the application of mathematics in economics imposes greater rigor, precision, and transparency of assumptions, not significantly different from the non-mathematical method. When analyzing utility function, it can be noticed that its application influences the economic analysis by imposing certain behaviors on people and giving them a mechanical character, primarily by introducing a constant relationship between the goods or goals considered by the person. For this reason, it is almost impossible to take into account the real uncertainty in theoretical research, which overly mechanizes the description of decision-making and economic processes.