A classical approach to collecting and elaborating information to make entrepreneurial decisions combines search heuristics, such as trial and error, effectuation, and confirmatory search. This paper develops a framework for exploring the implications of a more scientific approach to entrepreneurial decision making. The panel sample of our randomized control trial includes 116 Italian startups and 16 data points over a period of about one year. Both the treatment and control groups receive 10 sessions of general training on how to obtain feedback from the market and gauge the feasibility of their idea. We teach the treated startups to develop frameworks for predicting the performance of their idea and conduct rigorous tests of their hypotheses, very much as scientists do in their research. We let the firms in the control group instead follow their intuitions about how to assess their idea, which has typically produced fairly standard search heuristics. We find that entrepreneurs who behave like scientists perform better, are more likely to pivot to a different idea, and are not more likely to drop out than the control group in the early stages of the startup. These results are consistent with the main prediction of our theory: a scientific approach improves precision—it reduces the odds of pursuing projects with false positive returns and increases the odds of pursuing projects with false negative returns. This paper was accepted by Marie Thursby, entrepreneurship and innovation.

Moreover, for α < N p , we provide an explicit description of the domain. Spectral properties of the operator L are also obtained.

We find necessary and sufficient conditions for the validity of weighted Rellich and Calderón-Zygmund inequalities in L p , 1 ≤ p ≤ ∞, in the whole space and in the half-space with Dirichlet boundary conditions. General operators like L = ∆ + c x |x| 2 · ∇ − b |x| 2 are considered. We compute best constants in some situations.Mathematics subject classification (2010): 26D10, 35PXX, 47F05.

This paper deals with the problem of non-cooperative target recognition. Specifically, the aim is the automatic recognition of ship targets from inverse synthetic aperture radar (ISAR) images. For this purpose a new two-step multi-feature based technique is proposed; this technique uses a number of features extracted from the ship radar image and matches these features with those extracted from the images obtained by properly projecting the target models of the classification library. Both cases of a priori known or unknown ship aspect angles are considered: the knowledge of the ship aspect (as available from tracking data) allows the selection of the candidate models on the basis of the matching between the ship and the model length, thus resulting in a performance improvement. Moreover, both single- and multi-frame-based processing techniques are proposed in order to assess the performance improvement achievable when an increasing number of ISAR images are involved in the decision; the fusion strategy adopted for the exploitation of the information from the multiple images is also described. The performance of the overall proposed technique is deeply investigated against simulated data. Results of its application to a set of live ISAR images of a ship target are also provided showing the effectiveness of the proposed approach

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