Russ Abbott

Emergence explained: Abstractions: Getting epiphenomena to do real work

Emergence—macro-level effects from micro-level causes—illustrates the fundamental dilemma of science and is at the heart of the conflict between reductionism and functionalism: how can there be autonomous higher level laws of nature (the functionalist claim) if everything can be reduced to the fundamental forces of physics (the reductionist position)? In this, the first of two papers, we conclude the following. (a) What functionalism calls the special sciences (sciences other than physics) do indeed study autonomous laws. (b) These laws pertain to real higher level abstractions (discussed in this paper) and entities (discussed in the second paper). (c) Higher level interactions are epiphenomenal in that they can always be reduced to fundamental physical forces. (d) Since higher-level models are simultaneously both real and reducible we cannot avoid multiscalar systems. (e) Multiscalar systems are downward entailing and not upward predicting.

Putting complex systems to work

A primary objective of this paper (as well as of this symposium) is to examine concepts from the field of complex systems that can be applied to systems engineering. In this paper we focus primarily on the notions of emergence and entities and discuss their implications for systems engineering.

Abstraction abstracted

An abstraction is the reification and conceptualization of a distinction. We use the process of forming abstractions to make sense of the world, i.e., to form concepts. Once created we are often able to externalize these concepts as software. Abstractions are what give software elegance. Abstractions build on each other, producing a hierarchical dependency structure that often creates challenges for understanding. We can teach the use of pre-packaged abstractions. It is more difficult to teach the self-awareness necessary for inventing new abstractions. The process of building abstractions is bottom-up. Thought externalization is where top-down meets bottom-up.

Equity markets and computational intelligence

I propose a new characterization of the types of problems for which computational intelligence (CI) tends to be used, namely the identification of approximate abstractions. I then suggest that equity markets provide a challenging example for CI. Because markets are inherently adaptive, they pose a more difficult problem than traditional CI domains. I discuss my experience teaching a CI class that took the development of stock trading systems as a theme. A simple genetic algorithm to generate a trading strategy was developed as a class example. Although the astonishingly good results it achieved were due at least in part to data snooping, a simple unevolved version of the same strategy was almost as profitable. Yet it too had subtle data snooping problems—showing how difficult it is to avoid data snooping entirely, especially in adaptive domains.

Bits Don’t Have Error Bars: Upward Conceptualization and Downward Approximation

How engineering enabled abstraction in computer science. Engineering and computer science are both constructive disciplines: both fields build new artifacts. Computer science has evolved to focus primarily on abstractions: what aspects of a construction can be factored out and used elsewhere and more generically? Engineering has evolved to focus primarily on approximating physical reality: how close does one have to come to the underlying physics so that the construction is successful? The primary reason for this difference is that computer science builds on the foundation of the bit, a physically implemented symbol.

Abstraction, emergence, and thought

My research focuses on the relationships among abstraction, emergence (as in the study of complex systems), and the externalization of thought as software-in particular, on the application of computer science perspectives, especially abstraction, to long-standing philosophical issues.

From energy to information and back

This is a report of work in progress relating complex systems, energy flows, and interpretive cycles.