Stages Of Discovery And Entrepreneurship

In an attempt at a systematic theory of entrepreneurship, this paper connects various literatures, from economics and business. In economics, there are many notions of entrepreneurship, some of which seem to contradict each other. For example, there are notions of entrepreneurship as an equilibrating and as a disequilibrating force. In this paper, these differences are connected with the issue of exploitation and exploration from the business literature. The question is how one can explore while maintaining exploitation. For this, a cycle of discovery has been proposed, with stages of equilibration and disequilibration which build on each other, in process where exploitation leads to exploration. It is proposed that different notions of entrepreneurship can be associated with different stages of that cycle. In this way, different types of entrepreneurship complement each other in an ongoing process of discovery.entrepreneurship;innovation;organizational learning;discovery

A case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence.

In this paper, we propose a framework for iterative knowledge discovery from unstructured text using Formal Concept Analysis and Emergent Self Organizing Maps. We apply the framework to a real life case study using data from the Amsterdam-Amstelland police. The case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. Our human-centered framework facilitates the exploration of the data and allows for an efficient incorporation of prior expert knowledge to steer the discovery process. This exploration resulted in the discovery of faulty case labellings, common classification errors made by police officers, confusing situations, missing values in police reports, etc. The framework was also used for iteratively expanding a domain-specific thesaurus. Furthermore, we showed how the presented method was used to develop a highly accurate and comprehensible classification model that automatically assigns a domestic or non-domestic violence label to police reports.Formal concept analysis; Emergent self organizing map; Text mining; Actionable knowledge discovery; Domestic violence;

Two stochastic models useful in petroleum exploration

A model of the petroleum exploration process that tests empirically the hypothesis that at an early stage in the exploration of a basin, the process behaves like sampling without replacement is proposed along with a model of the spatial distribution of petroleum reserviors that conforms to observed facts. In developing the model of discovery, the following topics are discussed: probabilitistic proportionality, likelihood function, and maximum likelihood estimation. In addition, the spatial model is described, which is defined as a stochastic process generating values of a sequence or random variables in a way that simulates the frequency distribution of areal extent, the geographic location, and shape of oil deposit

Self-discovery enabling entrepreneurial discovery processes

This chapter interprets the entrepreneurial discovery process (EDP) as a process of self-discovery and transition spanning knowledge exploitation and exploration. Exploitation refers to using existing knowledge and refining existing processes; exploration refers to new knowledge discovery and creation. This transition requires changes in agency, which can be framed in terms of the division of labour between the triple-helix actors and a collective regional actor: ‘the self’ in self-discovery is a public–private partnership. The chapter presents the development of the conceptualisation of agency (‘self’) from the individual entrepreneur by Kirzner to the collective actor in the EDP presented by Foray. EDP and smart specialisation are compared with cluster theories and innovation system approaches.fi=vertaisarvioitu|en=peerReviewed

Information Theoretic Structure Learning with Confidence

Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension. If these dependencies are assumed to follow a Markov factor graph model, this exploration process is called structure discovery. For discrete-valued samples, estimates of the information divergence over the parametric class of multinomial models lead to structure discovery methods whose mean squared error achieves parametric convergence rates as the sample size grows. However, a naive application of this method to continuous nonparametric multivariate models converges much more slowly. In this paper we introduce a new method for nonparametric structure discovery that uses weighted ensemble divergence estimators that achieve parametric convergence rates and obey an asymptotic central limit theorem that facilitates hypothesis testing and other types of statistical validation.Comment: 10 pages, 3 figure

Purposive discovery of operations

The Generate, Prune & Prove (GPP) methodology for discovering definitions of mathematical operators is introduced. GPP is a task within the IL exploration discovery system. We developed GPP for use in the discovery of mathematical operators with a wider class of representations than was possible with the previous methods by Lenat and by Shen. GPP utilizes the purpose for which an operator is created to prune the possible definitions. The relevant search spaces are immense and there exists insufficient information for a complete evaluation of the purpose constraint, so it is necessary to perform a partial evaluation of the purpose (i.e., pruning) constraint. The constraint is first transformed so that it is operational with respect to the partial information, and then it is applied to examples in order to test the generated candidates for an operator's definition. In the GPP process, once a candidate definition survives this empirical prune, it is passed on to a theorem prover for formal verification. We describe the application of this methodology to the (re)discovery of the definition of multiplication for Conway numbers, a discovery which is difficult for human mathematicians. We successfully model this discovery process utilizing information which was reasonably available at the time of Conway's original discovery. As part of this discovery process, we reduce the size of the search space from a computationally intractable size to 3468 elements

The Geography of Exploration: A Study in the Process of Physical Exploration and Geographical Discovery

Exploration has been a common literary topic throughout the history of humans. However, much of this historical tradition bas possessed a fairly narrow Ill focus, emphasizing the drama and heroics of an individual explorer or concentrating on a description of a particular exploration. There has been little attempt at understanding the process of exploration and placing this important process in context with the historic and geographic phenomena that both affect and are affected by it. In this thesis, the author has broken the process of exploration down into a theoretical structure that is presented in a holistic model. This model has then been applied to the history of 15th century Portuguese exploration to test its applicability and usefulness