We study asset pricing dynamics in artificial financial markets model. The financial market is populated with agents following two heterogeneous trading beliefs, the technical and the fundamental prediction rules. Agents switch between trading rules with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behaviour depends on the past performance of the trading strategies in terms of an evolutionary fitness measure. We propose a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated. For comparison, we study pricing dynamics of a financial market populated with chartists perceive losses and gains symmetrically. One of our contributions is validating the agent-based models using real financial data of the Egyptian Stock Exchange. We find that our framework can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns, excess volatility, volatility clustering, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns. In addition to this, we find that loss aversion improves market quality and market stability.

This paper aims to investigate the relationship between virtual water (VW) exports and crop exchange by employing the methodology of social network analysis (SNA). This descriptive analysis gives prudence for policy-makers about both central importers and influential exporters of VW using the degree and eigenvector centrality measures. In addition, to facilitate the communications between trading partners, each of them should reach the others with the fewest number of links, so, the small world network properties could be examined. This approach is applied on the yearly average VW exports of the Nile basin countries over the period 2000–2013, and some insights for VW exchange structure are investigated. The empirical results show that all Nile basin countries do not suffer from vulnerable VW export structure. They have a stable and balanced crop export structure. Kenya, Uganda, and Tanzania are identified as the most influential and effective countries in exporting VW of crops. The presence of these countries is unavoidable in drawing trade policy and water management plans. While Kenya succeeded in saving a significant amount from VW export network, Tanzania, Uganda, and Ethiopia are gaining losses. Furthermore, VW export network of crops among Nile basin countries satisfies the conditions of small world effect.

Compulsory school-dropout is a serious problem affecting not only the education systems, but also the developmental progress of any country as a whole. Identifying the risk of dropping out, and characterizing its main determinants, could help the decision-makers to draw eradicating policies for this persisting problem and reducing its social and economic negativities over time. Based on a substantially imbalanced Egyptian survey dataset, this paper aims to develop a Logistic classifier capable of early predicting students at-risk of dropping out. Training any classifier with an imbalanced dataset, usually weaken its performance especially when it comes to false negative classification. Due to this fact, an extensive comparative analysis is conducted to investigate a variety of resampling techniques. More specifically, based on eight under-sampling techniques and four over-sampling ones, and their mutually exclusive mixed pairs, forty-five resampling experiments on the dataset are conducted to build the best possible Logistic classifier. The main contribution of this paper is to provide an explicit predictive model for school dropouts in Egypt which could be employed for identifying vulnerable students who are continuously feeding this chronic problem. The key factors of vulnerability the suggested classifier identified are student chronic diseases, co-educational, parents' illiteracy, educational performance, and teacher caring. These factors are matching with those found by many of the research previously conducted in similar countries. Accordingly, educational authorities could confidently monitor these factors and tailor suitable actions for early intervention.

Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches.

For politicians, to promote intended messages to different groups of individuals, they could employ strategic individuals called “informed agents.” The aim of this article is to explore and measure the impact of two competing groups of informed agents on opinion dynamics within a society exposed to two extreme opinions. Thus, an agent-based model is developed as an extension to the bounded confidence model by assuming the existence of two groups of informed agents. The impact of these agents with respect to their social characteristics, such as, their size in the society, how tolerant they are, their self-weight and attitudes about others’ opinions is explored. Different assumptions about the initial opinion distributions and their effect are also investigated. Due to the difficulty of observing a real society, social simulation experiments are constructed based on artificial societies. The simulations conducted resulted in some interesting findings. With no dominating group of the two informed agents, the society will be ended up concentrated around a moderate position. On the other hand, with significant difference between the two group sizes, the larger group will polarize the population towards its opinion. However, this conclusion will not apply if the population is skewed towards the other opinion. In such case, the larger group will only succeed to turn some of the society to be more moderate. In a society skewed towards extreme opinion, dominant informed agents adopting the other extreme will not be able to shift the society towards their opinion. Finally, in radical societies informed agents could turn most of the society to be extremists.