Category Archives: Leadership & Management

Oxford Graduation and various thoughts on critical thinking

I was recently in Oxford to receive my degree in Master of Business Administration, or Magister Administrationis Negotii as they say in Latin,and started thinking on what does it really mean to study there.

During the hectic days and nights struggling on books and notes, I mostly noticed the evident aspects of the academic curriculum: finance, operations, accounting, leadership, and the list goes on.

However, I now realize that the difference in Oxford starts from the  basic expectations of the University.

Mortarboards flying by Martin Griffiths

For example it is reasonably possible to get a “Pass” (50-69), all is required is to  understand the basics of the discipline. The difference in this range is only on how thorough the underlying theory is reported. The next band is a distinction (70-100) and the qualification to this range is given only if the student presents the concepts and frameworks taught during the lectures in a critical manner.

To “distinctively” make it, students must point out the limits of the very theory discussed in class and raise original and outstanding issues. Distinctions are seldom awarded.

In Oxford you are expected to think on your own and innovate. There is of course no premium in reporting back a lesser version of the lectures.

Maybe, after all, the most important lesson was just outside the books!

One note: the degree ceremony is something of complicated, typical of the University. Have a look at the brochure.

Do  Fidem!

Network Map of Knowledge and Art V2.0: Preview

I planned to improve this project since last year and now finally I found some more time to dedicate this hobby. The idea is to overcome the major limitation of my previous work create a more realistic map of influence that considers the nested influence: if A influences B and B influences C, then A should influences C as well. This longer chain was not considered in my first model and has deep implications on how to consider authors influence.

Until some time ago I was stuck with two major roadblocks. The first is size of the data – my Wikipedia query contains over 13000 people – so I had to experiment different solutions and implementations to settle with a matrix representation to simplify the computational aspects and the Excel VBA code. 

Sketch of the network representation and pseudo-algorithm

Sketch of the network representation and pseudo-algorithm

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Network map of Knowledge and Art: Datasets

I realized I had posted no dataset, so here you are!

You are most welcome to experiment and improve. Please  post your considerations as comments to this article.

Network map of Knowledge and Art: possible improvements

Thanks to everybody for reading me and  the good suggestions on how to improve the model. I will work on this again, if you have ideas, please leave a comment to this post. My plan in the next weeks/months is to:

  • Research more the Wikipedia Ontology and the SPARQL language. I only have a shallow knowledge in this field and the data extraction is tricky…for example to make things more difficult, in certain cases the field used is “influenced”, instead of “influenced” or “influenced by”. Any patient volunteer on this to help writing advanced queries is welcome!
  • Add the time dimension. The information is readily available in the Wikipedia infoboxes. This will allow to get a better view on how ideas a propagated and their persistence
  • Improve the influence concept. At the moment the model contemplate a fairly subjective and simple model. Need to add weights and longer chain of influence (e.g.: Cicero => Rousseau => Kant => … => …). This part will be tricky as we are not talking about hard disciplines (try to tell a write his work is deeply influenced by XYZ actors to see either anger or tears…)

Edison, the light bulb and disruptive technologies

Thomas Alva Edison

Thomas Alva Edison

One of my favorite paper analyzes how Edison and his team introduced the light bulb and the electric lighting in an industry dominated by gas illumination firms.

This classic article, still relevant today, helps to evolve our understanding of Edison from a lone inventor to the entrepreneur supported by collaborators, social ties and a stern determination to succeed. Furthermore, the elements of the business strategy such as robust design, are relevant to any innovator facing the tension between the established institutions and new ideas.

In 1878, a few gas companies dominated the lighting industry in New York. With assets invested in the business of about 1.5 billion dollars, these players had deep ties with the political and social ecosystem. Let us consider as an example, the political implications of the employed workforce or the network of suppliers for gas lamps or gas-related artifacts. Continue reading

Network map of Knowledge and Art

Network map of Knowledge and Art

Finally, after weeks, I have the time and the energy to post complex content. I wrote the essay below for a online course on network analysis. The overall experience was great as I discovered a world of possibilities for the discipline; any type of connection: power , conspiracy, social or knowledge network can be analyzed with the same underlining theory.


I wish to propose a network model to map the knowledge and ideas of the people contained in Wikipedia. The methodology of the creation of the dataset is generic and can be re-applied to any category of Wikipedia. The algorithms used were successful in identifying the clusters and to provide some insights on the dynamics of knowledge. The analysis is performed by utilizing different metrics such as modularity, weighted degrees and eccentricity. A small world test according to the Watts and Strogatz model is performed as well. You can find a printable and zoomable version of the full map here or the high res image here.

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Paolo Negrini's professional network

The networked world: it’s not who you know, it’s who they know!

inMaps is an interesting feature of LinkedIN used to  visualize professional networks.

Understanding the underlying dynamics of Social Networks is of crucial importance for a successful business and the topic is thoroughly researched.

The very short version is somehow counter-intuitive:  a healthy network should be sparse, that is, include loosely connected individuals.

The main target of managing network is to gather valuable information or resources from it. As the theory goes, this is more likely if contacts  would not be able to connect with each other without your involvement.  Structural holes are opportunities for knowledge brokerage that is, the exchange of information or resources between areas of the network.  As an added bonus, loosely connected individuals are likely to be able to reach beyond your usual environment: it’s not who you know, it’s who they know!

On the other hand, highly connected structures,  offer less potentials as the level of knowledge will be homogeneous. If somebody is interacting mainly with the same contacts, say the same high-school class, the network map would look very dense, and every node will be able to get information without involving you. In this case, contacts are likely to share the same level of knowledge and there is not much value available. as mentioned, social networks are fascinating entities, and I will write more on the topic.

Looking now at my LikedIN network map below, it reflects fairly accurately my past. I have three main clusters highlighted by different colors:

  • Bombardier:  I worked in different countries and roles, the colors represent locations or departments
  • Alstom Power: here I have been only in Switzerland, colors represent departments. Notably, there is a certain degree of exchange between these two first clusters as firms operates in similar industries.
  • Oxford: this is the most far-reaching and diverse cluster in my network. Interestingly, it took only 18 months to gain as many contacts as in my earlier jobs. Colors represent different sub-network (my class, other classes, academics, etc)

My professional network


Geroski. P.A. 2003. The Evolution of New Markets (TENM). Oxford University Press

Borgatti, S.P., A. Mehra, D.J. Brass, and G. Labianca. 2009. “Network analysis in the social sciences,” Science, vol. 323 (5916), pp 892-895. 13 Feb