This content is cross-posted to Tumbld Thoughts. A loosely-formed story in two parts about the pros and cons of predicting the outcome of and otherwise controlling complex sociocultural systems. Kurt Godel is sitting in the afterlife cafe right now, scoffing but also watching with great interest.
I. It's an All-encompassing, Self-regulation, Charlie Brown!
Here
is a video [1] by the complexity theorist Dirk Helbing about the possibility of a
self-regulating society. Essentially, by combining big data with the principles
of complexity would allow us to solve previously intractable problems [2]. This
includes more effective management of everything from massively parallel collective behaviors to very-rare events.
But controlling how big data is used can keep us from
getting into trouble as well. Writing at Gigaom blog, Derrick Harris argues that the potentially
catastrophic effects of AI taking over society (the downside of the
singularity) can be avoided by keeping key data away from such systems [3]. In this
case, even hyper-complex AI systems based on deep learning can become
positively self-regulating.
NOTES:
[2]
For a cursory review of algorithmic regulation, please see: Morozov, E. The rise of data and
the death of politics. The Guardian, July 19 (2014).
For a discussion as to why governmental regulation is a wicked problem and how algorithmic approaches might be inherently unworkable, please see: McCormick, T. A brief exchange with Tim O’Reilly about “algorithmic regulation”. Tim McCormick blog, February 15 (2014).
[3] Harris, D. When data become dangerous: why Elon Musk is right and wrong about AI. Gigaom blog, August 4 (2014).
II. Arguing Past Each Other Using Mathematical Formalisms
Here are a few papers on argumentation, game theory, and culture. My notes are below each set of citations. A good
reading list (short but dense) nonetheless.
Brandenburger, A. and Keisler, H.J. An Impossibility Theorem on Beliefs in
Games. Studia Logica, 84(2), 211-240 (2006).
* shows that any two-player game is embedded in a system
of reflexive, meta-cognitive beliefs. Players not only model payoffs that
maximize their utility, but also model the beliefs of the other player. The
resulting "belief model" cannot be completely self-consistent:
beliefs about beliefs have holes which serve as sources of logical
incompleteness.
* introduction to a logical paradox which can be resolved
by distinguishing between sets and sets that describe sets using a hierarchical
classification method. This paradox is the basis for the Brandenburger and
Keisler paper.
Mercier, H. and Sperber, D. Why do humans reason? Arguments for an
argumentative theory. Behavioral and Brain Sciences, 34, 57-111
(2011).
The Argumentative Theory: a
conversation with Hugo Mercier. Edge Magazine, April 27 (2011).
Oaksford, M. Normativity, interpretation, and
Bayesian models. Frontiers in Psychology, 5, 332 (2014).
* a new-ish take on culture and cognition called
argumentation theory. Rather than reasoning to maximize individual utility,
reasoning is done to maximize argumentative context. This includes
decision-making that optimizes ideonational consistency. This theory predicts
phenomena such as epistemic closure, and might be thought of as a postmodern
version of rational agent theory.
There also seems to be an underlying connection between
the "holes" is a culturally-specific argument and the phenomenon of
conceptual blending, but that is a topic for a future post.
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