Real options "in" projects and systems design : identification of options and solutions for path dependency

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.Includes bibliographical references (p. 289-298).This research develops a comprehensive approach to identify and deal with real options in" projects, that is, those real options (flexibility) that are integral parts of the technical design. It represents a first attempt to specify analytically the design parameters that provide good opportunities for flexibility for any specific engineering system. It proposes a two-stage integrated process: options identification followed by options analysis. Options identification includes a screening and a simulation model. Options analysis develops a stochastic mixed-integer programming model to value options. This approach decreases the complexity and size of the models at each stage and thus permits efficient computation even though traditionally fixed design parameters are allowed to vary stochastically. The options identification stage discovers the design elements most likely to provide worthwhile flexibility. As there are often too many possible options for systems designers to consider, they need a way to identify the most valuable options for further consideration, that is, a screening model. This is a simplified, conceptual, low-fidelity model for the system that conceptualizes its most important issues. As it can be easily run many times, it is used to test extensively designs under dynamic conditions for robustness and reliability; and to validate and improve the details of the preliminary design and set of possible options. The options valuation stage uses stochastic mixed integer programming to analyze how preliminary designs identified by the options identification stage should evolve over time as uncertainties get resolved. Complex interdependencies among options are specified in the constraints.(cont.) This formulation enables designers to analyze complex and problem-specific interdependencies that have been beyond the reach of standard tools for options analysis, to develop explicit plans for the execution of projects according to the contingencies that arise. The framework developed is generally applicable to engineering systems. The dissertation explores two cases in river basin development and satellite communications. The framework successfully attacks these cases, and shows significant value of real options "in" projects, in the form of increased expected net benefit and/or lowered downside risk.by Tao Wang.Ph.D

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005

Approximate methods for dynamic portfolio allocation under transaction costs

The thesis provides robust and efficient lattice based algorithms for solving dynamic portfolio allocation problems under transaction costs. The early part of the thesis concentrates upon developing a toolbox based on multinomial trees. The multinomial trees are shown to provide a reasonable approximation for most popular transaction cost models in the academic literature. The tool, once forged, is implemented in the powerful Mathematica based parallel computing environment. In the second part of the thesis we provide applications of our framework to real world problems. We show re-balancing portfolios is more valuable in an investment environment where the growth and volatility of risky assets is non-constant over the time horizon. We also provide a framework for modeling random transaction costs and compute the loss of expected utility of an investor faced with random transaction costs. Approximate methods are provided to solve portfolio constraints such as portfolio insurance and draw-down. Finally, we also highlight a lattice based framework for pairs trading

Incorporating Machine Learning with Satellite Data to Support Critical Infrastructure Measurement and Sustainable Development

Under the umbrella concept of Artificial Intelligence (AI) for good, recent advances in machine learning and large-scale data analysis have opened new opportunities to solve humanity’s most pressing challenges. Improvements in computation complexity and advances in AI (e.g., Vision Transformers) have led to faster and more effective techniques for extracting high-dimensional patterns from large-scale heterogeneous datasets (big data). Further, as satellite data become increasingly available at varying temporal-spatial resolutions, AI tools are helping us to better understand the underlying causes of environmental and socioeconomic changes at an unprecedented scale, ushering in an era of data-driven decision-making to support sustainable and equitable development. Based on these, we propose data-driven methods and techniques for critical infrastructure measurement and sustainable development. Using machine learning and remotely sensed data, we show that we can exploit knowledge and temporal-spatial characteristics learned from data-rich regions to improve data-driven predictions in regions with scant to no data. Specifically, we focus on three critical infrastructures: rivers, roads, and electricity access. Knowledge rivers, particularly their discharge, can help us understand how climate change is evolving, its manifestation on global water resources, and its impact on critical sectors like agriculture and renewable energy generation. On the other hand, better roads facilitate societal development, enabling access to local and global markets and socioeconomic opportunities, leading to better equality in service provision, faster socioeconomic development, and, ultimately, better human outcomes. Finally, we develop tools to support sustainable development, focusing on supporting electricity demand stimulation to improve energy access in rural communities. These methodologies and techniques can help emerging economies achieve their primary sustainable development goals (SDGs) by 2030

Risk Management

Every business and decision involves a certain amount of risk. Risk might cause a loss to a company. This does not mean, however, that businesses cannot take risks. As disengagement and risk aversion may result in missed business opportunities, which will lead to slower growth and reduced prosperity of a company. In today's increasingly complex and diverse environment, it is crucial to find the right balance between risk aversion and risk taking. To do this it is essential to understand the complex, out of the whole range of economic, technical, operational, environmental and social risks associated with the company's activities. However, risk management is about much more than merely avoiding or successfully deriving benefit from opportunities. Risk management is the identification, assessment, and prioritization of risks. Lastly, risk management helps a company to handle the risks associated with a rapidly changing business environment