Display screen and method of manufacture therefor

A screen assembly that combines an angle re-distributing prescreen with a conventional diffusion screen is disclosed. The prescreen minimizes or eliminates the sensitivity of the screen assembly to projector location. The diffusion screen provides other desirable screen characteristics. The prescreen is preferably formed by a collection of light transmitting and refracting elements, preferably spheres 80, partially embedded in a light blocking layer. Toward the back of the spheres 80 are effective apertures 82 where the light blocking layer 81 is absent or at least thinner than in other regions toward the side of the spheres. The projected image enters spheres 80 through the effective apertures 82, and exits the spheres 80 centered orientationally about the normal to the lens axis. The re-oriented light rays then enter the diffusion screen for viewing

On Mitigability of Uncertainty and the Choice Between Predictive and Nonpredictive Strategy

Managers face a critical issue in deciding when to employ a predictive planning approach versus a more adaptive and flexible strategic approach.We suggest that determiningwhich approach is ideal for a given context hangs on the extent to which uncertainty is, or might be, mitigable within that context. To date, however, the mitigability of uncertainty has not been adequately distilled. Here, we take on this issue, distinguishing mitigable ignorance of pertinent but knowable information (i.e., “epistemic uncertainty”) from immitigable indeterminacy (i.e., “aleatory uncertainty”). We review the current state of the debate on the existence of free will, because the acceptance or rejection of conscious agents as a true first cause has fundamental implications. A critical examination of the arguments for and against the free will hypothesis land us on the side of voluntarism, which implies immitigable indeterminacy (but not complete unpredictability) wherever conscious actors are involved. Accepting the existence of immitigable or aleatory uncertainty, then, we revisit the determination of strategic logics and produce important theoretical nuance and key boundary conditions in the normative choice between predictive and nonpredictive strategies

Mitigating versus Managing Epistemic and Aleatory Uncertainty

We are grateful for Holmes and Westgren’s (2020) thoughtful response to our recent article (Packard & 872 Academy of Management Review October Clark, 2020a). In it, they argued that “a mitigability– immitigability axis does not map well onto the aleatory–epistemic uncertainty axis” (p. 7). This challenge to our delineation casts doubt to its usefulness in strategic theorizing, as we have supposed. They thus proposed a revision to our definitions that encapsulates epistemic uncertainty within the confines of the present state of knowledge and the costs of acquiring such knowledge, allowing strategic analysis of the value of mitigation efforts to be more clearly assessed. While we are open minded toward such a revision to our framework, we do not see the proposed revision as a clear advancement over our original model, for reasons that we shall here expound

Probability Logic Fails in Immitigable Uncertainty, but Strategic Logic Does Not

We are grateful to Professor Arend (2020) for his engagement with our work on uncertainty and the choice of strategic logics (Packard & Clark, in press). We easily acknowledge that there are reasons to disagree with our conclusions as they imply a minimization, if not outright rejection, of much of modern behavioral research. It was not surprising, then, to receive Professor Arend’s (2020) criticisms, which appear to be based in what we might call the “epistemic camp” (contra our own “aleatory camp”) of behavioral research.1 The epistemic camp holds all uncertainty to be what we, in our article, describe as “epistemicuncertainty”andhas elsewhere beencalled “ambiguity” (Packard, Clark, & Klein, 2017)—that is, there is always a probability distribution that can be applied to a decision, even if it is unknown and/or subjectively generated in the mind of the decisionmaker. This assumption is quite seductive, as it allows probabilistic models to be applied to literally any choice situation and lends the appearance of scientific rigor. Professor Arend elaborated this position to conclude that any uncertainty that falls outside of it is essentially chaotic and cannot be managed by anything beyond luck. Our contrary position in the aleatory camp is that this epistemic uncertainty or ambiguity should not be confused with aleatory uncertainty—they are different in nature. Thus, there is no valid way to “convert” aleatory uncertainty into ambiguity by imposing a probability distribution onto something that cannot have one. Most business uncertainty involves such aleatory uncertainty due, in Knight’s (1921: 311) words, to “the inherent, absolute unpredictability of things, out of the sheer brute fact that the results of human activity cannot be anticipated and then only in so far as even a probability calculation in regard to them is impossible and meaningless.” Thus, while the probabilistic approach to decision-making preferred by the epistemic camp has a place, we, like most managers (Harrison, 1977), reject it as unrealistic for the majority of real-world choice scenarios. In the language of set theory, we hold the typical set of options available to an actor to be “open” or, more precisely, “infinite” (Packard et al., 2017), and not “closed,” as is required by probability theory. This renders the probability-based logic employed in the epistemic camp’s behavioral research, and in Professor Arend’s critique, “impossible and meaningless.

Federal Pell Grant Eligibility and Receipt: Explaining Nonreceipt and Changes to EFC Using National and Institutional Data

In examining national data on Federal Pell Grant eligibility in the National Postsecondary Student Aid Study (NPSAS), we were puzzled to discover that many students who appear to have eligible Expected Family Contributions (EFCs) do not receive the award. We use institutional data from a large public university to understand and enumerate changes from initial Free Application for Student Financial Aid (FAFSA) EFC to final Pell Grant EFC and explore why EFC changes occur. We determine that the nonreceipt of Pell Grant observed in NPSAS is likely due to NPSAS not reporting final Pell Grant EFCs. We examine how the verification process results in changes to EFC and describe how nearly half of students who experienced a change in EFC during the award year were not asked to verify. We also observe that selection for Quality Assurance verification and EFC changes varied based on students’ demographics characteristics. The paper concludes with discussion of improving the verification process

Seamless tiled display system

A modular and scalable seamless tiled display apparatus includes multiple display devices, a screen, and multiple lens assemblies. Each display device is subdivided into multiple sections, and each section is configured to display a sectional image. One of the lens assemblies is optically coupled to each of the sections of each of the display devices to project the sectional image displayed on that section onto the screen. The multiple lens assemblies are configured to merge the projected sectional images to form a single tiled image. The projected sectional images may be merged on the screen by magnifying and shifting the images in an appropriate manner. The magnification and shifting of these images eliminates any visual effect on the tiled display that may result from dead-band regions defined between each pair of adjacent sections on each display device, and due to gaps between multiple display devices

Pseudorandom Number Generators and the Square Site Percolation Threshold

A select collection of pseudorandom number generators is applied to a Monte Carlo study of the two dimensional square site percolation model. A generator suitable for high precision calculations is identified from an application specific test of randomness. After extended computation and analysis, an ostensibly reliable value of pc = 0.59274598(4) is obtained for the percolation threshold.Comment: 11 pages, 6 figure

Uncertainty Types and Transitions in the Entrepreneurial Process

While judgment has hitherto typically been viewed as a discrete decision process, we propose that it be conceptualized instead as a continuous and dynamic process of reassessment and revision. Adopting this approach, we revisit the nature of entrepreneurial decision making under uncertainty. We begin with a novel typology of uncertainty that defines and delineates different types of uncertain contexts. We then examine the nature of decision making within these distinct contexts, highlighting differences in how entrepreneurs make decisions within different types of uncertainty. We build these insights into a theory of the entrepreneurial process that highlights the transitory nature of uncertainty as entrepreneurs make certain judgments and revise those judgments over time. We discuss how uncertainty transitions throughout the judgment process, how the judgment process continues dynamically even after a judgment is made, and how the nature of uncertainty shifts over time due to endogenous and exogenous change