Bayesian-adjusted estimates in project selection - a comparison study of Bayesian and non-Bayesian decision makers with empirical evidence from the pharmaceutical industry

Companies select projects to invest in based on uncertain estimates of their performance. Theories and empirical evidence suggest that if the uncertain estimates are taken at face value, the true performance of the selected projects tends to be lower than estimated, causing the decision makers (DMs) to experience post-decision disappointment. Taking prior information into account through Bayesian adjustment can result in more realistic estimates of the project performances and thus higher expected performance among the selected projects. However, Bayesian adjustment makes it less likely to predict extreme outcomes and, consequently, may lead to missing out on big wins. This thesis studies the differences in the investment strategies of Bayesian and non-Bayesian DMs and outcomes of these strategies. This is done by employing a combined approach of both qualitative and qualitative research methodology. The quantitative approach of this thesis is in the form of a mathematical model that is used both to derive analytic results and for Monte Carlo simulation. The qualitative approach of this thesis is utilized to test theoretical findings empirically. The key results reveal that when fewer than 50% of project proposals would truly perform well (e.g., have truly positive NPV), a Bayesian DM invests in too few and a non-Bayesian DM to too many projects. Moreover, the average ex post performance of the projects funded by a Bayesian DM is higher than that of a non-Bayesian DM. However, a non-Bayesian DM will have a higher proportion of funded projects that result in big wins, but also a higher proportion of projects that result in losses. If, on the other hand, more than 50% of project proposals would truly perform well, the roles of a Bayesian and non-Bayesian DM are reversed. The less accurate the performance estimates, the more pronounced the differences between the investment outcomes of a Bayesian and a non-Bayesian DM. These analytic results are testified empirically in the R&D portfolio selection decisions in the pharmaceutical industry. Accordingly, the decision-making environment of the pharmaceutical industry displays characteristics of an environment with high estimate errors that amplify the differences in outcomes of a Bayesian DM’s versus a non-Bayesian DM’s investment decisions. As the DMs in this industry show quintessential characteristics of non-Bayesian DMs and the observed empirical outcomes perfectly coincide with the theoretical outcomes for non-Bayesian investment decisions, our theoretical findings are well-reflected empirically. From a theoretical perspective, this thesis contributes novel analytic results on the differences between the investment strategies adopted by Bayesian and non-Bayesian DMs and validates these results with empirical evidence. From a practitioner’s point of view, this thesis gives insights into how estimation uncertainties affect investment decisions and outcomes. Understanding such effects can help managers make better-informed decisions

Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method

This paper proposes an approach based on the Symbiotic Organism Search (SOS) for optimal determining sizing, siting, and number of Distributed Generations (DG) in distribution systems. The objective of the problem is to minimize the power loss of the system subject to the equality and inequality constraints such as power balance, bus voltage limits, DG capacity limits, and DG penetration limit. The SOS approach is defined as the symbiotic relationship observed between two organisms in an ecosystem, which does not need the control parameters like other meta-heuristic algorithms in the literature. For the implementation of the proposed method to the problem, an integrated approach of Loss Sensitivity Factor (LSF) is used to determine the optimal location for installation of DG units, and SOS is used to find the optimal size of DG units. The proposed method has been tested on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the SOS algorithm have been compared to those of other methods in the literature. The simulated results have demonstrated that the proposed SOS method has a very good performance and effectiveness for the problem of optimal placement of DG units in distribution systems

SYNTHESIS AND PROPERTIES OF LIPOIC ACID STABILIZED GOLD NANOCLUSTERS

Disulfide lipoic acid was used to synthesize new aqueous soluble gold nanomaterials. The first one is an ultra-small plasmonic nanoparticle displaying a weak plasmonic band at 520 nm. At the transition zone between larger metallic nanoparticles and smaller non-metallic nanoclusters, intriguing electrochemical and optical features were observed. The other cluster is a new molecular-like nanocluster with unique optical features. Distinct UV-visible absorption bands were observed corresponding to discrete energy states/orbitals along with a weak photoluminescence if ultracentrifuge purification is adopted. Dialysis purification yielded a tenfold increase in photoluminescence while the absorption bands diminish. This transition is attributed to the gradual oxidation of some of the sulfur atoms at the core-ligand interface. Annealing with a known amount of excess thiol is shown to expedite and better control the transitions observed through the synthesis and purification along with yielding an enhancement of the electrochemiluminescence by more than ten folds

Studies of the N-end Rule Pathway in Bacteria and Mammals

Many intracellular proteins are either conditionally or constitutively short-lived, with in vivo half-lives that can be as brief as a minute or so. The regulated and processive degradation of intracellular proteins is carried out largely by the ubiquitin (Ub)-proteasome system (UPS), in conjunction with molecular chaperones, autophagy, and lysosomal proteolysis. The N-end rule pathway, the first specific pathway of UPS to be discovered, relates the in vivo half-life of a protein to the identity of its N-terminal residue. Physiological functions of the N-end rule pathway are strikingly broad and continue to be discovered. In bacteria and in eukaryotic organelles mitochondria and chloroplasts all nascent proteins bear the pretranslationally formed N-terminal formyl-methionine (fMet) residue. What is the main biological function of this metabolically costly, transient, and not strictly essential modification of N-terminal Met, and why has Met formylation not been eliminated during bacterial evolution? One possibility is that the formyl groups of N-terminal Met in Nt formylated bacterial proteins may signify a proteolytic role of Nt-formylation. My colleagues and I addressed this hypothesis experimentally, as described in Chapter 3 of this thesis. Among the multitude of biological functions of the mammalian Arg/N-end rule pathway are its roles in the brain, including the regulation of synaptic transmission and the regulation of brain’s G-protein circuits. This regulation is mediated, in part, by the its Ate1-mediated arginylation branch of the Arg/N-end rule pathway. One role of the Ate1 arginyltransferase (R-transferase) is to mediate the conditional degradation of three G-protein down-regulators, Rgs4, Rgs5, and Rgs16. Ate1-/- mice, which lack the Ate1 R-transferase, exhibit a variety of abnormal phenotypes. Chapter 4 describes our studies of neurological abnormalities in Ate1-/- mice (and also in mice that express Ate1 conditionally, upon the addition of doxycycline), with an emphasis on the propensity of these mice to epileptic seizures. </p

Reaching high accuracy for energetic properties at second-order perturbation cost by merging self-consistency and spin-opposite scaling

Quantum chemical methods dealing with challenging systems while retaining low computational costs have attracted attention. In particular, many efforts have been devoted to developing new methods based on the second-order perturbation that may be the simplest correlated method beyond Hartree-Fock. We have recently developed a self-consistent perturbation theory named one-body M{\o}ller-Plesset second-order perturbation theory (OBMP2) and shown that it can resolve issues caused by the non-iterative nature of standard perturbation theory. In the present work, we extend the method by introducing the spin-opposite scaling to the double-excitation amplitudes, resulting in the O2BMP2 method. We assess the O2BMP2 performance on the triple-bond N2 dissociation, singlet-triplet gaps, and ionization potentials. O2BMP2 performs much better than standard MP2 and reaches the accuracy of coupled-cluster methods in all cases considered in this work.Comment: 22 pages, 9 figures, 2 table