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.


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)


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?


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.