A Simulation Model For Investment Decision Making

As societies face the challenge of securing an efficient and environmentally sound energy supply, researchers have strived to determine the future development of energy consumption, infrastructure and technology endowment. This book proposes a new agent-based approach to studying the development of urban energy systems. Decisions of private and commercial investors and operators are modeled using a bounded rational decision model which are parameterized by socio-economic data.

In this study is being considered the process of learning under laboratory conditions of decision-making. During the simulation of investment, a group of thirty examinees is choosing strategies from defined set trying to maximize their own effect in the course of discreetly arranged time period. Information about alternatives and backward information are limited; therefore there are two levels of limitation. The research is directed toward a scenario with richer information-content, and certain conclusions are also referred to the topic of "minimal feedback in game" which was treated in the previous research. Decision-making and learning during the game is analysed through indicators: attitude toward the risk, perception of successful and unsuccessful strategies, promptness in orientation reversing and consideration of missed opportunities. In this matter are being recognized the models of learning of a behavioural game theory, with limitations since in the scenario of investment there is no interactions. Efficiency results and present models of behaviour and learning at individuals and population of players undoubtedly indicate that backward information is very influential and significant.

In order to create value, companies must allocate their resources effectively and evaluate investment alternatives. This book examines, from a theoretical and empirical point of view, how managerial flexibility can be integrated into investment decisions through the optional approach. Unlike the traditional net present value method, the actual options take into account indeterminate elements. These lead to unpredictable cash flows at the time of the investment decision, especially in the context of complex and risky projects. The book puts into perspective the use of optional models and their interactions. The different categories of options are the subject of practical applications, through analysis of investment decisions where uncertainty is growing. Therefore, studies make it possible to consider the flexible nature of investment choices by integrating new information and risk over time.

Microscopic Simulation (MS) uses a computer to represent and keep track of individual ("microscopic") elements in order to investigate complex systems which are analytically intractable. A methodology that was developed to solve physics problems, MS has been used to study the relation between microscopic behavior and macroscopic phenomena in systems ranging from those of atomic particles, to cars, animals, and even humans. In finance, MS can help explain, among other things, the effects of various elements of investor behavior on market dynamics and asset pricing. It is these issues in particular, and the value of an MS approach to finance in general, that are the subjects of this book. The authors not only put their work in perspective by surveying traditional economic analyses of investor behavior, but they also briefly examine the use of MS in fields other than finance. Most models in economics and finance assume that investors are rational. However, experimental studies reveal systematic deviations from rational behavior. How can we determine the effect of investors' deviations from rational behavior on asset prices and market dynamics? By using Microscopic Simulation, a methodology originally developed by physicists for the investigation of complex systems, the authors are able to relax classical assumptions about investor behavior and to model it as empirically and experimentally observed. This rounded and judicious introduction to the application of MS in finance and economics reveals that many of the empirically-observed "puzzles" in finance can be explained by investors' quasi-rationality. Researchers use the book because it models heterogeneous investors, a group that has proven difficult to model. Being able to predict how people will invest and setting asset prices accordingly is inherently appealing, and the combination of computing power and statistical mechanics in this book makes such modeling possible. Because many finance researchers have backgrounds in physics, the material here is accessible. Emphasizes investor behavior in determining asset prices and market dynamics Introduces Microscopic Simulation within a simplified framework Offers ways to model deviations from rational decision-making