Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016). Shahriari et al. Proceedings of the IEEE Introduction “Mathematically we are considering the problem of finding a global maximizer (or minimizer) of an unknown objective function f, where X is some design space of interest; …” ”…in global optimization, X is often a compact subset of R^d but the Bayesian optimization framework can be applied to more unusual search spaces that involve categorical or conditional inputs.

Dušan Teodorovic. Swarm Intelligence systems for transportation engineering: principles and applications. Transportation Research, 2008. This is one of the most comprehensive articles I have found that attempts to connect swarm intelligence heuristics to transportation systems. Most of the article is dedicated to introducing four multi-agent systems that leverage information sharing models to optimize search techniques, and I will recap those here. Ant Colony Optimization (ACO) Ants leave pheromone trails, and an ant will use the strength of the signal to weight their choice of path, as well-trod paths traveled by ants heading to a food source have a stronger pheromone signal.

This paper applies models of bounded and biased cognition to the creation of a generative model for human choices in decision problems. Perhaps more importantly, the authors attempt to infer preferences (not beliefs) from this model. They focus their attention on four types of agents: Hyperbolic-discounting (i.e. my favorite new way of saying procrastinating) Agents using Monte Carlo approximations of expected utility “Myopic” agents Bounded value-of-information agents Choice is made in proportion to a softmax function of expected utility:

“Mathematics - Application and Applicability”, by Mark Steiner. Included in The Nature of Nature: Examining the Role of Naturalism in Science (2011). Edited by Bruce L. Gordon and William A. Dembski. ISI Books: Wilmington, Deleware. Key ideas Canonical applications (theories developed to describe an application) vs. non-canonical (applying mathematics in situations other than those that created them) Distinguishing applications of mathematics from mathematics itself Exploration of individual thinkers and their attempts to reconcile mathematics and the empirical world (including Gottlob Frege, Hartry Field, Eugene Wigner), with the group-theoretic leading the charge

Choices, Risks, and Reward Reports: Charting Public Policy for RL Systems. Thomas Krendl Gilbert, Sarah Dean, Tom Zick, Nathan Lambert (CLTC, 2022). With new machine learning advances comes new risks. This white paper addresses the risks associcated with Reinforcement Learning with an aime to empower policy makers with some richer understanding to ensure safety. The unique challenges facing RL are summed up well in the intro - “In ML, the primary risks have to do with outputs that a model generates.

What comes after minds? by Marvin Minsky. From The New Humanists: Science at the Edge (2003). Edited by John Brockman. Barnes & Noble Books: New York. Key quotes “No uniform scheme will lead to machines as resourceful as the human brain. Instead, I’m convinced that this will require many different ‘ways to think’ - along with bodies of knowledge about how and when to use them”. “Computer science has helped us envision a far wider range of ways to represent different types and forms of knowledge,…” “I see each emotional state as a distinctly different way to think”.