Articles
Symbol-Crunching With the Gambler's Ruin Problem
by Doron Zeilberger
The Gambler's Ruin problem is an apt metaphor for life. A tiny advantage gets
amplified enormously in an artificial way, and just being a tiny bit luckier
(or smarter or stronger or better-looking) gets you very far in the long run.
That's why my good friend Geroge appears to be a so much better backgammon
player than his wife Martha, even though he is only slightly better. But today,
thanks to our silicon brethern, we can answer many more questions, exactly
and symbolically, than Abraham De Moivre and James Bernoulli could answer
three hundred years ago.
Why Simulate? To Develop a Mental Model
by Andrzej Nowak, Agnieszka Rychwalska and Wojciech Borkowski
By internalizing computer simulations as a mental model, a researcher also internalizes
the limitations of the simulation, which may translate into unconscious constraints
in thinking when using the mental model.
Thomas
C. Schelling and the Computer: Some Notes on Schelling's Essay "On
Letting a Computer Help with the Work"
by Rainer Hegselmann
When Thomas Schelling first presented the influential "Schelling Model" of the
dynamics of social segregation, he advocated investigating it manually, rather
than on a computer. Today it is the basis of highly effective computer simulations
(see example). Hegselmann
explores Schelling's views and the history of his Model.
Books
Report of a Workshop on the Scope and
Nature of Computational Thinking
by the Committee for the Workshops
on Computational Thinking; National Research Council
Computational thinking is a fundamental analytical skill that everyone can use
to help solve problems, design systems, and understand human behavior, making
it useful in a number of fields. (full
text online)
Videos
What is Probabilistic Computing
by Navia Systems
The world's first natively probabilistic computers, a technology as suited to
making judgements in the presence of uncertainty as traditional computing
technology is to large-scale record keeping.
Bayesian models of human inductive learning
by Josh Tenenbaum
In everyday learning and reasoning, people routinely draw successful
generalizations from very limited evidence, far outstripping the capabilities
of conventional learning machines. How do they do it? And how can we bring
machines closer to these human-like learning abilities?
Links
Helping Business Leaders make Big Decisions
Advanced Artificial Intelligence in combination with proprietary expert process
enables Epagogix to provide studios, independent producers and investors
with early analysis and forecasts of the Box Office potential of a script.
(See also How
algorithms shape our world)
Social Physics
By using Big Data to build a predictive, computational theory of human
behavior we can hope to engineer better social systems.
