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Computational Eudaemonics: Expert Happiness Systems
Marcelo Rinesi   Aug 16, 2009   Frontier Economy  

This is an interview with Marko A. Rodriguez, a scientist at the Los Alamos National Laboratory. Besides doing basic research on applied mathematics and computer science, he is doing work on computational eudaemonics — the use of computer algorithms to increase happiness by helping us make better decisions, even suggesting new options.

Do you think the widespread use of eudaemonic algorithms will be contingent on the embracing of an Aristotelian ethic and concept of happiness, or is their usage compatible, in the sense of there being a potentially strong demand for them, with the contemporary ethos?

I think the concepts of Aristotle, Norton, Hobbes, Flanagan, and even Rand (to some extent) are all barking up the same tree. And while that species of tree may be the same, the individual instances of it will be different. That is, each person will have to find their own eudaemonic path, where the role of computational eudaemonics is to support the individual in this discovery process. Moreover, for these algorithms, it’s a process too. Some will live and some will die in this “society of algorithms”, but the society will continue to evolve and adapt to the human condition. When individual algorithms work well with the human and the human allows them to work, then computational eudaemonics will be serving its purpose.

How do you see the process of research and validation of an eudaemonic algorithm for decisions with impact over the long term?

With respect to validation, I believe the answer to that is the answer to this: “do people use it?” Take Google for example. There is no formal proof that PageRank is a good algorithm to rank webpages. However there is a pragmatic proof. The pragmatic proof is the fact that people use Google regularly. Similarly for a eudaemonic algorithm, if it survives to be used another day, then it is good…it is valid.

Do you feel we have or will have enough information in this generation to data mine patterns about, say, the number of children a couple might want to have?

I think what will happen is that more and more data will be exposed in the Web of Data. At first, it might just be a better “recommendation” algorithm —  but with enough information in the Web of Data and enough insight on the part of the algorithm designers, we may just end up putting more faith in the algorithm.

p>There’s a clear profit motive for a search engine or a retailer to create a good algorithm —  we interact with them often enough to infer their quality and either become repeating customers or shift to a competitor with a better algorithm — but for decisions take very seldom (e.g., choosing a major), there would be both a great demand for good algorithms and an unclear process by which the worst ones could be filtered out. What are your thoughts about who might come up with eudaemonic algorithms and their motivations?

p>As you note, there are recommendations that are based on repetition: e.g., movies, books, music, webpages, etc. And, as you say as well, there are “one time only” recommendations: e.g., which major to choose in college. However, you can see these “one time only” recommendations as happening in repetition — not through the individual, but through the population. While the “one time” algorithm may be faulty for an individual at a particular point in time, it may be gathering enough data points to be successful for the next individual down the line. I think the saying is: “Rome wasn’t build in a day.” I don’t know how accurate these algorithms can get, but there is a sense of better and worse. Moreover, we understand, to some degree, why we like certain movies, books, ideas, etc. So, being able to represent those biases computationally may bring us beyond recommendation and into a world of eudaemonia.

For further information:

Rodriguez, M.A., Watkins, J., “Faith in the Algorithm, Part 2: Computational Eudaemonics,” Proceedings of the International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Invited Session: Innovations in Intelligent Systems, eds. Velásquez, J.D., Howlett, R.J., and Jain, L.C., Lecture Notes in Artificial Intelligence, Springer-Verlag, LA-UR-09-02095, Santiago, Chile, April 2009. []


Marcelo Rinesi is the IEET's Chief Technology Officer, and former Assistant Director. He is also a freelance Data Intelligence Analyst.


Very interesting article & references - our suggestor system at is based on similar principles. Thank you.

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