This paper puts forward a multi-level model, based on system dynamics methodology, to understand the impact of cyber crime on the financial sector. Consistent with recent findings, our results show that strong dynamic relationships, amongst tangible and intangible factors, affect cyber crime cost and occur at different levels of society and value network. Specifically, shifts in financial companies' strategic priorities, having the protection of customer trust and loyalty as a key objective, together with considerations related to market positioning vis-à-vis competitors are important factors in determining the cost of cyber crime. Most of these costs are not driven by the number of cyber crime incidents experienced by financial companies but rather by the way financial companies choose to go about in protecting their business interests and market positioning in the presence of cyber crime. Financial companies' strategic behaviour as response to cyber crime, especially in regard to over-spending on defence measures and chronic under-reporting, has also an important consequence at overall sector and society levels, potentially driving the cost of cyber crime even further upwards. Unwanted consequences, such as weak policing, weak international frameworks for tackling cyber attacks and increases in the jurisdictional arbitrage opportunities for cyber criminals can all increase the cost of cyber crime, while inhibiting integrated and effective measures to address the problem.

Bayesian neural networks were used to model the relationship between input parameters, Democracy, Allies, Contingency, Distance, Capability, Dependency and Major Power, and the output parameter which is either peace or conflict. The automatic relevance determination was used to rank the importance of input variables. Control theory approach was used to identify input variables that would give a peaceful outcome. It was found that using all four controllable variables Democracy, Allies, Capability and Dependency; or using only Dependency or only Capabilities avoids all the predicted conflicts.

The computational intelligence (CI) methods such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have many applications in chemistry, oil and gas, electronics, financial, telecommunications, and many others. In this article, ANN and ANFIS are used to model and predict the biodiesel conversion under several conditions. The inputs of the proposed CI models are oil type, catalyst type, calcination temperature, catalyst concentration, methanol-to-oil ratio, n-hexane-to-oil volume ratio, reaction time, and reaction temperature and the output is biodiesel conversion. Experimental data of available literature are used to train and test the CI models in MATLAB 7.0.4 software. Comparison between the proposed ANN and ANFIS models and the experimental data show that the proposed CI models are very efficient and fast tools, and there is a good agreement between them and the experimental data with a minimum error. Also, it can be found that the introduced ANN model is more accurate than the ANFIS model. The proposed ANN model has overall MRE% (mean relative error percentage) <1.5%, RMSE (root mean square error) <1.34, R (correlation coefficient) >0.9995, and MAE (mean absolute error) <0.9.

This article develops and compares two Bayesian neural network models, a more restrictive Bayesian framework using Gaussian approximation and a less restrictive one using a hybrid version of Markov Chain Monte Carlo method (HMC), for the prediction of militarized interstate disputes (MIDs). In addition, to compare and analyze different Bayesian models for international conflict, the authors introduce a new measurement to interpret the relative influence of the model variables on the MIDs. The results indicate that the Gaussian approximation and HMC models are not statistically different in their performance. However HMC correctly recognized a marginally higher number of militarized disputes whose classification is important for policy purpose. On the variable effect, both models indicate similar patter of influences, where the two key liberal variables, democracy and economic interdependence, produce a strong dynamic feedback loop among each other, which greatly increases or decreases the probability of MIDs.

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