As I write this I’m watching Vijay Narayanan’s, Director of Algorithms and Data Science Solutions at Microsoft, talk at the DataEDGE conference at UC Berkeley.
The talk is about “The Data Science Economy.” It began with a history of the evolution of the human centralized nervous system. He then went on to show the centralizing trend of the data economy. Data collection will be become more mobile, data processing will be done in the cloud. This data will be sifted by software and used to power a marketplace of services, which ultimately deliver intelligence to their users.
It was wonderful to see somebody so in the know reaffirming what has been a suspicion I’ve had since starting graduate school but have found little support for in the academic setting. The suspicion is that what’s needed to accurately model the data science economy is a synthesis of cognitive science and economics that can show the comparative market value and competitiveness of different services.
This is not out of the mainline of information technology, management science, computer science, and other associated disciplines that have been at the nexus of business and academia for 70 years. It’s an intellectual tradition that’s rooted in the 1940’s cybernetics vision of Norbert Wiener and was going strong in the social sciences as late as Beniger‘s The Control Revolution, which, like Narayanan, draws an explicit connection between information processing in the brain and information processing in the microprocessor–notably while acknowledging the intermediary step of bureaucracy as a large-scale information processing system.
There’s significant cross-pollination between engineering, economics, computer science, and cognitive psychology. I’ve read papers from, say, the Education field in the late 80’s and early 90’s that refers to this collectively as “the dominant paradigm”. At UC Berkeley today, it’s fascinating to see a departmental politics play out over ‘data science’ that echoes some of these concerns that a powerful alliance of ideas are getting mobilized by industry and governments while other disciplines are struggling to find relevance.
It’s possible that these specialized disciplinary discourses are important for the cultivation of thought that is important for its insight despite being fundamentally impractical. I’m coming to a different view: that maybe the ‘dominant paradigm’ is dominant because it is scientifically true, and that other disciplinary orientations are suffering because they are based on unsound theory. If disciplines that are ‘dominated’ by another paradigm are floundering because they are, to put it simply, wrong, then that is a very elegant explanation for what’s going on.
The ramification of this is that what’s needed is not a number of alternatives to ‘the dominant paradignm’. What’s needed is that scholars double down on the dominant paradigm and learn how to express in its logic the complexities and nuances that the other disciplines have been designed to capture. What we can hope for, in terms of intellectual continuity, is the preservation of what’s best of older ideas in a creative synthesis with the foundational principles of computer science and mathematical biology.