Information Flows on Mobiles

The idea to use mobile phones (here and here) to help economic development in the most remote corners of the world is fascinating and definitely smart. For one thing, mobile phones have already reached the Bottom Billion. In 2007 there were 45 subscribers per 100 inhabitants in the developing countries. That means that we can now expect to have one mobile in every family. Everywhere. As well in communities where services like water, electricity, hospitals, schools or transportation are still far away.

What poor people mostly need are functioning institutions. And market is one of these. If market is not working, farmers will pay higher prices for what they buy and got less money for what they sell.  Moreover they could buy or sell at the wrong time and possibly in the wrong place. In the words of the government of Rwanda,

the success of these farmers has been greatly affected by lack of access to pricing information. Many times, farmers speculate what crops to grow and what prices to charge at harvest. Some farmers depend on middlemen to dictate the prices and in most cases the latter exploit the former. For any farmer to earn a decent living from agriculture, easy access to information on market prices is of paramount importance.

Making information flows on mobile phones could

empower farmers to enable them make more informed market pricing decisions and ultimately more successful farming.

The idea of mobile banking goes in the same direction: making a  service so critical for development accessible to almost everyone. That will not end poverty, but  will probably make the task easier.

Thursday, 16 April 2009

Understanding Capitalism

Nobel-winning economist Amartya Sen argues, in an article published on The New York Review of Books, that the way out from the crisis passes through a better understanding of the ideas that contributed to build the actual economic system. Adam Smith, John Maynard Keynes, Arthur Cecil Pigou, should be read, not just quoted. And I quote

Smith viewed markets and capital as doing good work within their own sphere, but first, they required support from other institutions—including public services such as schools—and values other than pure profit seeking, and second, they needed restraint and correction by still other institutions—e.g., well-devised financial regulations and state assistance to the poor—for preventing instability, inequity, and injustice. If we were to look for a new approach to the organization of economic activity that included a pragmatic choice of a variety of public services and well-considered regulations, we would be following rather than departing from the agenda of reform that Smith outlined as he both defended and criticized capitalism.

We must understand how institutions work and make them work better. But not just aiming at economic growth.

There is a critical need for paying special attention to the underdogs of society in planning a response to the current crisis, and in going beyond measures to produce general economic expansion.

A crisis not only presents an immediate challenge that has to be faced. It also provides an opportunity to address long-term problems when people are willing to reconsider established conventions. This is why the present crisis also makes it important to face the neglected long-term issues like conservation of the environment and national health care, as well as the need for public transport (…).

Sunday, 22 March 2009

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RSS r-bloggers.com

  • Lecture slides: Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance)
    These are slides from a lecture I gave at the School of Applied Sciences in Münster. In this lecture, I talked about Real-World Data Science and showed examples on Fraud Detection, Customer Churn & Predictive Maintenance. Real-World Data Scie...
  • Use foreach with HPC schedulers thanks to the future package
    The future package is a powerful and elegant cross-platform framework for orchestrating asynchronous computations in R. It's ideal for working with computations that take a long time to complete; that would benefit from using distributed, parallel frameworks to make them complete faster; and that you'd rather not have locking up your interactive R session. You […]
  • Feature Selection using Genetic Algorithms in R
    From a gentle introduction to a practical solution, this is a post about feature selection using genetic algorithms in R.
  • Using clusterlab to benchmark clustering algorithms
    Clusterlab is a CRAN package (https://cran.r-project.org/web/packages/clusterlab/index.html) for the routine testing of clustering algorithms. It can simulate positive (data-sets with __1 clusters) and negative controls (data-sets with 1 cluster). Why test clustering algorithms? Because they often fail in identifying the true K in practice, published algorithms are not always well tested, and we need to know […]
  • Selecting ‘special’ photos on your phone
    At the beginning of the new year I always want to clean up my photos on my phone. It just never happens. So now (like so many others I think) I have a lot of photos on my phone from … Continue reading →

RSS Simply Statistics

  • How Data Scientists Think - A Mini Case Study
    In episode 71 of Not So Standard Deviations, Hilary Parker and I inaugurated our first “Data Science Design Challenge” segment where we discussed how we would solve a given problem using data science. The idea with calling it a “design challenge” was to contrast it with common “hackathon” type models where you are presented with […]
  • The Netflix Data War
    A recent article in the Wall Street Journal, “At Netflix, Who Wins When It’s Hollywood vs. the Algorithm?” by Shalini Ramachandran and Joe Flint details some of the internal debates within Netflix between the Los Angeles-based content team, which is in charge of developing and marketing new content for the streaming service, and the data […]
  • The Role of Theory in Data Analysis
    In data analysis, we make use of a lot of theory, whether we like to admit it or not. In a traditional statistical training, things like the central limit theorem and the law of large numbers (and their many variations) are deeply baked into our heads. I probably use the central limit theorem everyday in […]

RSS Statistical Modeling, Causal Inference, and Social Science