I think therefore I learn? Entrepreneurial cognition, learning and knowing in practice

In observing recent theoretical developments in the field, it is apparent that two distinctive yet relatively separate areas of study have emerged—entrepreneurial cognition and entrepreneurial learning. This conceptual paper aims to create some measure of reconciliation between these two perspectives to provide a more robust and multidisciplinary conceptual platform for understanding the entrepreneur. We augment an appreciation of the social dimensions of the learning process by which entrepreneurs cognitively acquire and transform knowledge. Through the application of influential practice-based theorizing we offer an integrative organizing framework that places participation at the heart of entrepreneurial practice, knowledge and identity

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution

A Self-Adaptive Database Buffer Replacement Scheme.

The overall performance of a database system is very sensitive to the buffer replacement algorithm used. However, the performance evaluation of database buffer replacement algorithms commonly assumes that database accesses are independent and the probability for each individual database record to be accessed is fixed. Due to these rigid assumptions, the results of performance evaluation are not always reliable. In this dissertation, we apply Simon\u27s model of information accessing to model database accessing frequencies. This approach relaxes the independent assumption, and since it also allows certain dynamic behavior in accessing frequencies; thus, it is more robust and preferable over the traditional artificial data approach. Furthermore, taking advantage of the conceptual similarity between the self-organizing linear search heuristics and the traditional buffer replacement algorithms, we propose a self-adaptive buffer replacement scheme that outperforms conventional database buffer replacement algorithms. The findings of our study can be further applied to many other computer applications, e.g. the more complex problem of archival storage design in larger database systems

The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems

In this paper we conceptualize the impact of information technology on marketing decision-making. We argue that developments in information technology affect the performance of marketing decision-makers through different routes. Advances in information technology enhance the possibilities to collect data and to generate information for supporting marketing decision-making. Potentially, this will have a positive impact on decision-making performance. Managerial expertise will favor the transformation of data into market insights. However, as the cognitive capabilities of marketing managers are limited, increasing amounts of data may also increase the complexity of the decision-making context. In turn, increased complexity enhances the probability of biased decision processes (e.g., the inappropriate use of heuristics) thereby negatively affecting decision-making performance. Marketing management support systems, also being the result of advances in information technology, are tools that can help marketers to benefit from the data explosion. These systems are able to increase the value of data and, at the same time, make decision-makers less vulnerable to biased decision processes. Our analysis leads to the expectation that the combination of marketing data, managerial judgment, and marketing management support systems will be a powerful factor for improving marketing management. Implications of our analysis are discussed.decision making;decision biases;information technology;marketing management support systems

Can Synergy in Triple-Helix Relations be Quantified? A Review of the Development of the Triple-Helix Indicator

Triple-Helix arrangements of bi- and trilateral relations can be considered as adaptive eco-systems. During the last decade, we have further developed a Triple-Helix indicator of synergy as reduction of uncertainty in niches that can be shaped among three or more distributions. Reduction of uncertainty can be generated in correlations among distributions of relations, but this (next-order) effect can be counterbalanced by uncertainty generated in the relations. We first explain the indicator, and then review possible results when this indicator is applied to (i) co-author networks of academic, industrial, and governmental authors and (ii) synergies in the distributions of firms over geographical addresses, technological classes, and industrial-size classes for a number of nations. Co-variation is then considered as a measure of relationship. The balance between globalizing and localizing dynamics can be quantified. Too much synergy locally can also be considered as lock-in. Tendencies are different for the globalizing knowledge dynamics versus locally retaining wealth from knowledge in industrial innovations

Locally Self-Adjusting Skip Graphs

We present a distributed self-adjusting algorithm for skip graphs that minimizes the average routing costs between arbitrary communication pairs by performing topological adaptation to the communication pattern. Our algorithm is fully decentralized, conforms to the $\mathcal{CONGEST}$ model (i.e. uses $O(\log n)$ bit messages), and requires $O(\log n)$ bits of memory for each node, where $n$ is the total number of nodes. Upon each communication request, our algorithm first establishes communication by using the standard skip graph routing, and then locally and partially reconstructs the skip graph topology to perform topological adaptation. We propose a computational model for such algorithms, as well as a yardstick (working set property) to evaluate them. Our working set property can also be used to evaluate self-adjusting algorithms for other graph classes where multiple tree-like subgraphs overlap (e.g. hypercube networks). We derive a lower bound of the amortized routing cost for any algorithm that follows our model and serves an unknown sequence of communication requests. We show that the routing cost of our algorithm is at most a constant factor more than the amortized routing cost of any algorithm conforming to our computational model. We also show that the expected transformation cost for our algorithm is at most a logarithmic factor more than the amortized routing cost of any algorithm conforming to our computational model