Monthly Archives: November 2010


Parsimony means efficient simplicity. In science and philosophy, it often guides the quest for the shortest, cleanest path or explanation. This word comes from the Latin verb parcere – to spare. It is usually a safe bet that most series of events proceed under the rule of ‘common sense’ where waste of energy and/or time is minimized. Understanding and theorizing about such processes can benefit from the heuristic of parsimony.

One example of parsimony-guided analysis is found in an area of evolutionary biology called phylogenetics. Here, closeness of relationships is determined by counting the number of evolutionary changes between taxa (groups of organisms). For example, DNA base pair differences can be enumerated to hypothesize a most likely ancestral tree with speciation, hybridization, and extinction defining the branches. Even for just a few taxa, many different phylogenetic trees are possible. Applying parsimony, the most likely is the one that requires the fewest genetic changes. This scheme offers the hope that an entire systematic taxonomy (tree of life) can be compiled.

Parsimony is a valid heuristic in comparing phylogenetic trees because each tree results from the same theory (evolution) in general, and ancestry in particular. However, parsimony must not be applied when comparing competing theories. This can give misleading, and sometimes completely wrong, results. The only basis for comparing theories is each one’s ability to explain current observations and make correct predictions, not their elegance or parsimony. Parsimony cannot be used as a logical principle. For example, it is a fallacy to use parsimony to argue against what is in fact a fundamental requirement for life: complexity.

Ethics of Synthetic Life

Mildred K. Cho and David A. Relman

Synthetic “Life,” Ethics, National
Security, and Public Discourse

SCIENCE Vol 329 2 July 2010

found at

Review of implications of synthetic life. Interesting take on how biology is no longer the realm of only biologists in this “rapidly changing landscape”.

Expert System

One of the more mature and successful applications of AI (Artificial Intelligence) is the Expert System, in which a knowledge base (domain) is stored on a computer, and then delivered back to users via an ‘inference engine’. An inference engine is a sort of active decision tree, wherein branches can be taken according to current conditions, and thinking can even be ‘backtracked’. Backtracking is necessary if the current conditions change or if an unfruitful path is taken (perhaps a wrong guess). The goal is to have a machine-based, portable ‘expert’ that can make decisions within this domain using varying problem parameters like a human can. One person or persons contribute knowledge, and a different set of people then can use that knowledge. These two groups may be widely separated in time and location, perhaps even in areas of sparse population or hazardous conditions. Interestingly, the user is not necessarily another human. Expert Systems are sometimes employed in automated systems to enable machines to make (artificially) intelligent decisions.

Expert systems are usually created within a ‘shell’. This is a framework that allows rules to be defined and stored along with the facts that define the domain. The human expert imparts her knowledge using the development tools in the shell. Once created, users can then use the shell to apply that stored expertise, by means of an interactive dialog, to new sets of problems within the domain.

Of course, machines lack the ability to apply emotional, cultural, and social context. This can be a hinderance or a benefit depending on the application.