Valuing flexibility in the migration to flexible-grid networks

Increasing network demand is expected to put pressure on the available capacity in core networks. Flexible optical networking can now be installed to increase network capacity in light of future traffic demands. However, this technology is still in its infancy and might lack the full functionality that may appear within a few years. When replacing core network equipment, it is therefore important to make the right investment decision between upgrading toward flexible-grid or fixed-grid equipment. This paper researches various installation options using a techno-economic analysis, extended with real option insights, showing the impact of uncertainty and flexibility on the investment decision. By valuing the different options, a correct investment decision can be made

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Multigame models of innovation in evolutionary economics

We incorporate information measures representing knowledge into an evolutionary model of coevolving firms and markets whereby the growing orderliness of firms potentiates a predictable progression of market exchange innovations which themselves become beneficial only with the growing orderliness of firms. We do this by generalizing Nelson and Winter style evolutionary models which are well suited to the study of entry, exit, and growth dynamics at the level of individual firms or entire industries. The required innovation is to use information measures to impose an order on the routines constituting a firm, and by correlating order with firm profitability, allow the preferential selection of innovations which increase order. In this viewpoint, the coherent mathematical framework provided by information and probability theory describes firm orderliness and variability, as well as all selection operations. This informational approach allows modelling the synergistic interactions between routines in a single firm and between different firms in a general but comprehensive manner, so that we can successfully model and predict innovations specifically focussed on organizational order. In particular, we can predict the coevolution over time of firm organizational complexity and of increasingly sophisticated market exchange mechanisms for routines permitting that increased organizational order. We demonstrate our approach using numerical simulations and analytic techniques exploiting a multigame player environment.Evolution, Knowledge, Markets, Evolutionary dynamics, Games, Multigame Environments

Exploiting Qualitative Information for Decision Support in Scenario Analysis

The development of scenario analysis (SA) to assist decision makers and stakeholders has been growing over the last few years through mainly exploiting qualitative information provided by experts. In this study, we present SA based on the use of qualitative data for strategy planning. We discuss the potential of SA as a decision-support tool, and provide a structured approach for the interpretation of SA data, and an empirical validation of expert evaluations that can help to measure the consistency of the analysis. An application to a specific case study is provided, with reference to the European organic farming business

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Carbon Capture and Sequestration: How Much Does this Uncertain Option Affect Near-Term Policy Choices?

One of the main issues in the climate policy agenda, the timing of abatement efforts, hinges on the uncertainties of climate change risks and technological evolution. We use a stochastic optimization framework and jointly explore these two features. First, we embed in the model future potential large-scale availability of Carbon Capture and Storage (CCS) technologies. While non-CCS mitigation that reduces fossil energy use is modelled as exerting inertia on the economic system, mainly due to the durability of the capital in energy systems and to technology lock-in and lock-out phenomena, the implementation of CCS technologies is modelled as implying less resilience of the system to changes in policy directions. Second, climate uncertainty is related in the model to the atmospheric temperature response to an increase in GHGs concentration. Performing different simulation experiments, we find that the environmental target, derived from a cost-benefit analysis, should be more ambitious when CCS is included in the picture. Moreover, the possible future availability of CCS is not a reason to significantly reduce near-term optimal abatement efforts. Finally, the availability of better information on the climate cycle is in general more valuable than better information on the CCS technological option.Climate change, Uncertainty, Sequestration, Cost-benefit analysis