Choosing Among Alternative New Product Development Projects: The Role of Heuristics

The initial screening decision that marketing managers make is critical. It requires the selection of which innovation project to invest in, which is fundamental to marketing success. However, our knowledge of how managers make these decisions and how this impacts performance is limited. By drawing upon cognitive psychology and the managerial decision-making literature, we address two critical questions. The first question focuses on identifying specific decisionmaking types (e.g., specific heuristics, intuition) used when making an innovation-screening decision. Based on this analysis and prior research, we develop specific decision-maker profiles about how an individual manager decides. The second research question is about connecting these profiles with performance. Specifically, it addresses what the consequences of different decision-maker profiles are on the perceived accuracy and speed of decision-making? Data were collected from 122 senior managers in these industries. We find that when heuristics are used alone, or concurrently with intuition, managers make decisions that are as accurate as when they rely on analytical decision-making. However, the process is significantly faster. The findings provide an important step towards a more comprehensive understanding of decisionmaking at the front-end of innovation

Physical Theories, Eternal Inflation, and Quantum Universe

We present a framework in which well-defined predictions are obtained in an eternally inflating multiverse, based on the principles of quantum mechanics. We show that the entire multiverse is described purely from the viewpoint of a single "observer," who describes the world as a quantum state defined on his/her past light cones bounded by the (stretched) apparent horizons. We find that quantum mechanics plays an essential role in regulating infinities. The framework is "gauge invariant," i.e. predictions do not depend on how spacetime is parametrized, as it should be in a theory of quantum gravity. Our framework provides a fully unified treatment of quantum measurement processes and the multiverse. We conclude that the eternally inflating multiverse and many worlds in quantum mechanics are the same. Other important implications include: global spacetime can be viewed as a derived concept; the multiverse is a transient phenomenon during the world relaxing into a supersymmetric Minkowski state. We also present a theory of "initial conditions" for the multiverse. By extrapolating our framework to the extreme, we arrive at a picture that the entire multiverse is a fluctuation in the stationary, fractal "mega-multiverse," in which an infinite sequence of multiverse productions occurs. The framework discussed here does not suffer from problems/paradoxes plaguing other measures proposed earlier, such as the youngness paradox, the Boltzmann brain problem, and a peculiar "end" of time.Comment: 66 pages, 13 figures, longer version of the abstract in the body of the paper; v2: minor revisio

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage.

Load forecasting models employed in the electric utility industry have become increas ingly dependent upon information about the electricity used by indivi dual appliances (i.e., end uses). Currently, information on appliance usage is obtained from two fundamentally different sources: (1) engi neering estimates and (2) conditional demand estimates. Bayesian anal ysis provides the means by which these two sources can be formally co mbined. Observed usage data (via the conditional demand approach) are used to modify a set of prior beliefs (the engineering approach), transforming them into a posterior distribution that describes appliance usage patterns and reflects the evidence provided by both approaches. Coauthors are Joseph A. Herriges, Kenneth E. Train, and Robert J. Windle. Copyright 1987 by MIT Press.