Most markets now boast a few platform businesses. These platforms don’t get their hands dirty with products or services. Instead, they illuminate the market. But why is there no light in the organizational development world?
Platforms turn on the lights. They make it easier for buyers and sellers to find each other, they optimize transactions, and they learn about the market. The organizational and professional development marketplaces have many large players, but none of them have flipped the switch from a product business to a platform business. Why?
There are four reasons.
We have reached the end of our project and, to be humble, we find that computational social science, in its attempt to quantify and rationalize the complex decisions and actions of human beings, requires extensive efforts to reinterpret and correctly formulate data analysis to achieve its fundamental ends. People, their ideas, and drives do not fit into quantifiable categories. However, we have successfully reached our goals. We have created a populism index across the European union and isolated features that indicate a populist. …
This week we worked on refining the map visualization we have and improving the interactivity of that. We missed two countries in the previous visualization and corrected it. We also added one marker for each country that shows the name of the country and the corresponding populism score.
The markers will show when the mouse clicks on the corresponding country. Our next step is to add bar charts to the markers that provide visualization of data within each country, which would be similar to the following example (from Folium tutorial page):
This week we performed an additional statistical analysis, undertook a machine learning sample, and refined our conception of populism to indicate particular electoral preferences. We ran two different types of machine learning experiments on our data this past week: SVM and regression. We used the regression method to learn a function that best fits the scores of our labelled data, and used SVM to learn a binary classifier from our labelled data that will determine whether a person is populist or non-populist.
We performed a simple linear regression data on the information using the Linear Regression module from sklearn. We…
This week we began preprocessing our data for running machine learning experiments and explored different ways of visualizing populism scores on a world map with potential interactivity. We’ve made progress on both and this blogpost will focus on our visualization results.
We intend to visualize our final results on a world map, where each EU country will be highlighted in a color that corresponds to the populism score it receives. Towards this purpose, we decide to use Folium, a python package that integrates the data analysis strengths of python and the mapping strengths of leaflet.js library. …
Our group is studying a data set that contains 12 thousand survey responses from a random sampling of individuals throughout 28 countries in the EU and the United States. This data was collected in the hopes of better understanding the “Trump Effect” which is the rise of populism in the United states as manifested in Donald Trump’s victory.
The data is a set of 90 questions, a subset of which are posed to each individual in the dataset based off of the individual’s citizenship. The questions range from basic demographic data to political affiliations and moral tendencies.
We have chose…
Blog Post 1: The Trump Effect — Challenges and Goals
We have a dataset of twelve thousands randomly selected respondents to an international survey conducted by Dalia Research. Dalia Research collected this data in order to measure the “Trump Effect” — the surge in populism that has given rise to radical political outcomes including Trump’s victory, and the U.K’s vote to ceded from the EU. Populism seems to be on the rise and there is a lot to be gained from understanding its development.
Our team is attempting to use this data to create a model that predicts populist sentiment…
Don’t learn too much. Aim for just above average or you’ll get overwhelmed with how little you actually know.