Refers to a collection of algorithms used to statistically sample a space of parameters or variables to optimize a system, but also often used to sample a huge space for information. There are many variants, including simulated annealing, genetic algorithms, ant colony optimization, hill-climbing, etc
Published in Chapter:
AI and Ideas by Statistical Mechanics
Lester Ingber (Lester Ingber Research, USA)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch009
Abstract
A briefing (Allen, 2004) demonstrates the breadth and depth complexity required to address real diplomatic, information, military, economic (DIME) factors for the propagation/evolution of ideas through defined populations. An open mind would conclude that it is possible that multiple approaches may be required for multiple decision makers in multiple scenarios. However, it is in the interests of multiple decision-makers to as much as possible rely on the same generic model for actual computations. Many users would have to trust that the coded model is faithful to process their inputs. Similar to DIME scenarios, sophisticated competitive marketing requires assessments of responses of populations to new products. Many large financial institutions are now trading at speeds barely limited by the speed of light. They colocate their servers close to exchange floors to be able to turn quotes into orders to be executed within msecs. Clearly, trading at these speeds require automated algorithms for processing and making decisions. These algorithms are based on "technical" information derived from price, volume and quote (Level II) information. The next big hurdle to automated trading is to turn "fundamental" information into technical indicators, e.g., to include new political and economic news into such algorithms.