David A. Eubanks | Searcharxiv

arXiv2014

Causal Interfaces

The interaction of two binary variables, assumed to be empirical observations, has three degrees of freedom when expressed as a matrix of frequencies. Usually, the size of causal influence of one variable on the other is calculated as a single value, as increase in recovery rate for a medical treatment, for example. We examine what is lost in this simplification, and propose using two interface constants to represent positive and negative implications separately. Given certain assumptions about non-causal outcomes, the set of resulting epistemologies is a continuum. We derive a variety of particular measures and contrast them with the one-dimensional index.

cs.AI↗

arXiv2008

Survival Strategies

This paper addresses the theoretical conditions necessary for some subject of study to survive forever. A probabilistic analysis leads to some prerequisite conditions for preserving, say, electronic data indefinitely into the future. The general analysis would also apply to a species, a civilization, or any subject of study, as long as there is a definition of "survival" available. A distinction emerges between two approaches to longevity: being many or being smart. Natural selection relies on the first method, whereas a civilization, individual, or other singular subject must rely on the latter. A computational model of survival incorporates the idea of Kolmogorov-type complexity for both strategies to illustrate the role of data analysis and information processing that may be required. The survival-through-intelligence strategy has problems when the subject can self-modify, which is illustrated with a link to Turing's Halting Problem. The paper concludes with comments on the Fermi Paradox.

q-bio.PE↗