Can your computer make you happy, with Computational Eudaemonics
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Can your computer make you happy, with Computational Eudaemonics
 
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Our own Marko Rodriguez of
LANL and Knowledge Reef is heading in an interesting new direction, to use the
power of computers to make us “happy”. Below is an interview with
him. It raises several issues: 1) He seems to be proposing a “data mining” approach
to determine what makes people happy and a system to improve decision making
and suggestions so more people make good “happiness enhancing”
decisions. Having worked for many years with data mining systems, I can see a
number of practical and theoretical problems. For example, how to define and
measure the many dimensions of “happiness”, how to get the actual
data, how to identify and evaluate all the options for a decision, how to
balance short term and long term happiness, and so forth 2) He proposes that “use” is a good way to
determine if the system is actually effective. That is, if people use it then
it meets its goals of increasing happiness. That does not seem to me to be an
intellectually satisfying way of determining if a system is effective in
meeting its purported goals. Is there a better way of determining this? 3)
He makes an important
distinction between “frequent” decisions such as which restaurant to eat in, and “once in a
lifetime” decisions such as who to marry (for most people) or where to go
to college. He recognizes the data problems of evaluating the “once in a
lifetime” decisions, but seems confident that simply looking at many such
decisions will give his system all the data it needs. I’m a skeptic. Having
looked at billions of decisions for “frequent” actions, e.g., soft
drink purchases, you quickly find out your decision matrixes are actually
pretty sparse when you try to account for many of
the key variables, and your statistical models based on these do not always
have the accuracy you need. Expanding this to “once in a lifetime”
decisions adds orders of magnitude to the difficulty of the problem. Jack Stafurik Computational
Eudaemonics: Expert Happiness Systems
Marcelo
Rinesi
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. [http://arxiv.org/abs/0904.0027] Marcelo
Rinesi is the Assistant Director of the IEET. Mr. Rinesi is
Editor-in-Chief of Frontier Economy. ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org |
Re: Can your computer make you happy, with Computational Eudaemonics
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Alot of times our decisions are repetitious patterns created by our own limitations and beliefs and not necessarily what leads to happiness. So history doesn't always portend the desired future results.
On Sun, Sep 6, 2009 at 9:43 PM, Jack Stafurik <[hidden email]> wrote:
============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org |
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