Gravitino phenomenology and cosmological implications of supergravity

In this thesis we consider the phenomenology of supergravity, and in particular the particle called "gravitino". We begin with an introductory part, where we discuss the theories of inflation, supersymmetry and supergravity. Gravitino production is then investigated into details, by considering the research papers here included. First we study the scattering of massive W bosons in the thermal bath of particles, during the period of reheating. We show that the process generates in the cross section non trivial contributions, which eventually lead to unitarity breaking above a certain scale. This happens because, in the annihilation diagram, the longitudinal degrees of freedom in the propagator of the gauge bosons disappear from the amplitude, by virtue of the supergravity vertex. Accordingly, the longitudinal polarizations of the on-shell W become strongly interacting in the high energy limit. By studying the process with both gauge and mass eigenstates, it is shown that the inclusion of diagrams with off-shell scalars of the MSSM does not cancel the divergences. Next, we approach cosmology more closely, and study the decay of a scalar field S into gravitinos at the end of inflation. Once its mass is comparable to the Hubble rate, the field starts coherent oscillations about the minimum of its potential and decays pertubatively. We embed S in a model of gauge mediation with metastable vacua, where the hidden sector is of the O'Raifeartaigh type. First we discuss the dynamics of the field in the expanding background, then radiative corrections to the scalar potential V(S) and to the Kähler potential are calculated. Constraints on the reheating temperature are accordingly obtained, by demanding that the gravitinos thus produced provide with the observed Dark Matter density. We modify consistently former results in the literature, and find that the gravitino number density and T_R are extremely sensitive to the parameters of the model. This means that it is easy to account for gravitino Dark Matter with an arbitrarily low reheating temperature.

Communicative and Anticipatory Decision-making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.