AI and Ideas by Statistical Mechanics

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
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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.
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The concept of “memes” is an example of an approach to deal with DIME factors (Situngkir, 2004). The meme approach, using a reductionist philosophy of evolution among genes, is reasonably contrasted to approaches emphasizing the need to include relatively global influences of evolution (Thurtle, 2006).

There are multiple other alternative works being conducted world-wide that must be at least kept in mind while developing and testing models of evolution/propagation of ideas in defined populations: A study on a simple algebraic model of opinion formation concluded that the only final opinions are extremal ones (Aletti et al., 2006). A study of the influence on chaos on opinion formation, using a simple algebraic model, concluded that contrarian opinion could persist and be crucial in close elections, albeit the authors were careful to note that most real populations probably do not support chaos (Borghesi & Galam, 2006). A limited review of work in social networks illustrates that there are about as many phenomena to be explored as there are disciplines ready to apply their network models (Sen, 2006).

Key Terms in this Chapter

Global Optimization: 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

DIME: Represents diplomatic, information, military, and economic aspects of information that must be merged into coherent pattern

ISM: An anacronym for Ideas by Statistical Mechanics in the context of the noun defined as: A belief (or system of beliefs) accepted as authoritative by some group or school. A doctrine or theory

especially: a wild or visionary theory. A distinctive doctrine, theory, system, or practice

Meme: Alludes to a technology originally defined to explain social evolution, which has been refined to mean a gene-like analytic tool to study cultural evolution

Memory: This may have many forms and mechanisms. Here, two major processes of neocortical memory are used for AI technologies, short-term memory (STM) and long-term memory (LTM)

Copula Analysis: This transforms non-Gaussian probability distributions to a common appropriate space (usually a Gaussian space) where it makes sense to calculate correlations as second moments.

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