From Knightian uncertainty to real‐structuredness: Further opening the judgment black box

Research Summary: Entrepreneurial judgment remains a concept that resembles a black box. This article attempts to further open that black box by developing a dimensionalization of types of judgment. To achieve this, it joins recent efforts to explicitly link entrepreneurship to Simonian themes by integrating the notion of decision problem structures into the judgment-based approach (JBA) to entrepreneurship. This article proposes a more comprehensive and nuanced approach to judgment in the face of decision problems we label “realstructured.” Extending the JBA comes with several important implications: It uncovers additional entrepreneurial knowledge problems, provides new insights for both economic organization and judgment communicability, and informs research on entrepreneurial success and failure. It also sheds new light on the controversy over the relationship between effectuation and judgment. Managerial Summary: When taking decisions, entrepreneurs cannot know how the future will pan out. Those decisions are made under conditions of uncertainty and only time will tell whether they prove astute or otherwise. The uncertainty of the future leads entrepreneurs to exercise judgment based on their individual beliefs and to act accordingly. The components of that entrepreneurial judgment remain rather underexplored. The purpose of this article is to dig deeper into, and thereby improve, the understanding of entrepreneurial judgment. The main result of this article is a four-part dimensionalization of judgment, covering entrepreneurial (sub-)judgments on the effects incurred by action, the appraisal of action alternatives, the goals underlying action, and resolving the decision problem

On predictive entrepreneurial action in uncertain, ill-structured conditions

Decision-making is at the heart of entrepreneurship. Unsurprisingly, entrepreneurship research has engaged with processes of entrepreneurial decision-making resulting, most importantly, in the notions of causation, effectuation, and enactment. Nevertheless, the range of processes delineated to date remains somewhat incomplete. Drawing on crucial insights from the analysis of decision problem structures reveals that entrepreneurship theory has lacked a process that both recognizes the ill-structuredness typically surrounding entrepreneurial decisions and places prognoses center stage. While effectuation implicitly addresses structural defects but denies prognoses a central role, causation emphasizes the importance of predictions while being associated with well-structured, risky environments, and thus, unaffected by structural defects. Theorizing about a combination thereof, that is, a process recognizing and considering the ill-structuredness of entrepreneurial environments yet building on predictions of the future is overdue. This paper, therefore, seeks to foster a more comprehensive yet nuanced understanding of entrepreneurial decision-making processes by outlining the intrinsic features of one such process that we term execution and relating it to existing processes

Photoexcitation of valley-orbit currents in (111)-oriented silicon metal-oxide-semiconductor field-effect transistors

We demonstrate the injection of pure valley-orbit currents in multivalley semiconductors and present the phenomenological theory of this effect. We studied photoinduced transport in (111)-oriented silicon metaloxide-semiconductor field effect transistors at room temperature. By shining circularly polarized light on exact oriented structures with six equivalent valleys, nonzero electron fluxes within each valley are generated, which compensate each other and do not yield a net electric current. By disturbing the balance between the valley fluxes, we demonstrate that the pure valley-orbit currents can be converted into a measurable electric current

Orbital photogalvanic effects in quantum-confined structures

We report on the circular and linear photogalvanic effects caused by free-carrier absorption of terahertz radiation in electron channels on (001)-oriented and miscut silicon surfaces. The photocurrent behavior upon variation of the radiation polarization state, wavelength, gate voltage and temperature is studied. We present the microscopical and phenomenological theory of the photogalvanic effects, which describes well the experimental results. In particular, it is demonstrated that the circular (photon-helicity sensitive) photocurrent in silicon-based structures is of pure orbital nature originating from the quantum interference of different pathways contributing to the absorption of monochromatic radiation.Comment: 8 pages, 5 figures, two culumne

Photon helicity driven electric currents in graphene

We report on the observation of photon helicity driven currents in graphene. The directed net electric current is generated in single layer graphene by circularly polarized terahertz laser radiation at normal as well as at oblique incidence and changes its sign upon reversing the radiation helicity. The phenomenological and microscopic theories of the observed photocurrents are developed. We demonstrate that under oblique incidence the current is caused by the circular photon drag effect in the interior of graphene sheet. By contrast, the effect at normal incidence stems from the sample edges, which reduce the symmetry and result in an asymmetric scattering of carriers driven by the radiation field. Besides a photon helicity dependent current we also observe photocurrents in response to linearly polarized radiation. The microscopic mechanisms governing this effect are discussed.Comment: 13 pages, 7 figure

Predicting sex from brain rhythms with deep learning

We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10-5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20-25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology