Search results

Using fuzzy measures and fuzzy integrals, the paper presents a mathematical model of learning which is able to learn through fuzzy information. The characteristics of the model are studied theoretically and in numerical examples, where the model is compared with an ordinary Bayesian learning model. The problem of seeking an extremum of multimodel objective function is given as an example.

Entropy measures of fuzzy events of a set X with a (not necessary finite) positive measure µ defined on a σ‐algebra of subsets of X are studied. Using fuzzy measures and fuzzy integrals, a theorem is presented, which gives a sufficient condition for the existence of entropy measures under a list of reasonable axioms. The result is used to define entropy measures for fuzzy numbers.

Dietary public health advice has been to consume fish. The fatty acids in fish, specifically the ratio of n‐3 to n‐6, are reported to be instrumental in determining the benefits. Looks at the available literature on differences in fatty acid composition between wild and farmed fish. Supports the hypothesis that farmed fish have a higher fat content and a different fat composition to wild fish. Suggests that more detailed studies, are required to compare the fat compositions of the various types of wild and farmed fish.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored.

The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders.

The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure.

Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability.

This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

Asset pricing dynamics in a multi-asset framework when investors’ trading exhibits the disposition effect is studied. The purpose of this paper is to explore asset pricing dynamics and the switching behavior among multiple assets.

The dynamics of complex financial markets can be best explored by following agent-based modeling approach. The artificial financial market is populated with traders following two heterogeneous trading strategies: the technical and the fundamental trading rules. By simulation, the switching behavior among multiple assets is investigated.

The proposed framework can explain important stylized facts in financial time series, such as random walk price dynamics, bubbles and crashes, fat-tailed return distributions, absence of autocorrelation in raw returns, persistent long memory of volatility, excess volatility, volatility clustering and power-law tails. In addition, asset returns possess fractal structure and self-similarity features; though the switching behavior is only allowed among the asset markets.

The model demonstrates stylized facts of most real financial markets. Thereafter, the proposed model can serve as a testbed for policy makers, scholars and investors.

To the best of knowledge, no research has been conducted to introduce the disposition effect to a multi-asset agent-based model.

A problem of handling fuzzy quantities in a process of knowledge acquisition and deriving an inference mechanism by means of fuzzy relation equations is studied in extensive way. It is clearly pointed out that both of them are closely related and correspond to various types of fuzzy relation equations that are considered. Their relevance to the form of knowledge collected is also indicated. A problem of dimension reduction of a knowledge base is considered as well. Two modes of the use of the knowledge base (goal‐, and data‐driven) are also studied.

The purpose of this paper is to examine consumer behavior towards Greek convenience stores chains and to determine the criteria on which store selection is based. The paper also analyses the stores’ position in the retail sector.

A random sample of 360 convenience store consumers was surveyed across four major cities. Data were collected through personal interviews, using a structured questionnaire. Statistical analysis was applied to further evaluate the information.

Consumers choose convenience stores mainly to cover their daily needs and save time on shopping, despite their dissatisfaction with the provided services. The survey also identified the characteristics of convenience store customers and their preferences.

The principal contribution of the present research is its focus on consumer behavior towards convenience stores in Greece.

To permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes.

The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (S_f), severity (S) and detectability (S_d) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the risk. The fuzzy linguistic assessment model was developed using toolbox platform of MATLAB 6.5 R.13.

The applicability of the proposed approach is investigated with the help of an illustrative case study from the paper industry. Fuzzy risk assessment is carried out for prioritizing failure causes of the hydraulic system, a primary element of the feeding system. The results provide an alternate ranking to that obtained by the traditional method. It is concluded from the study that the fuzzy logic‐based approach not only resolves the limitations associated with traditional methodology for RPN evaluation but also permits the experts to combine probability of occurrence (S_f), severity (S) and detectability (S_d) of failure modes in a more flexible and realistic manner by using their judgement, experience and expertise.

The paper integrates the use of fuzzy logic and expert database with FMEA and may prove helpful to system safety and reliability analysts while conducting failure mode and effect analysis to prioritize failures for taking corrective or remedial actions.