Research Confirms ID hypothesis in the field of Informatics

ID Theory is a study of information and design, and highly involved in informatics. Some even consider ID Theory to be synonymous with informatics. ID Theory has long held for decades that biological and genetic information behaves as algorithms.

Computer algorithms have been written to simulate evolution. It’s an interesting field of study if you’re into higher math and computer science.

Fruit Flies:

In the eyes of a few researchers, the secrets to breakthroughs in distributed computing lies in the nervous system of the fruit fly. And new, performance-improving algorithms based around these findings could help to better detect earthquakes or cure disease.  Scientists at Carniegie-Mellon University and Tel Aviv University created a new algorithm for computer networks that determined which computers would issue commands, based around the fruit fly research.[1]

An article featuring biological algorithms was published in the February 2011 edition of Science.[2]The article written by Jeffrey Kephart, entitled “Learning from Nature,” is about building better computer networks.  The article provides a photo of the fruit fly’s bristly head with a caption stating, “Studying the development of a fruit fly’s sensory bristles provided insight into developing a more practical algorithm for organizing networked computers.”

Kephart further elaborates,

“The tradition of biologically inspired computing extends back more than half a century to the original musings of Alan Turing about artificial intelligence and John von Neumann’s early work on self-replicating cellular automata in the 1940s,” he noted.  “Since then, computer scientists have frequently turned to biological processes for inspiration.  Indeed, the names of major subfields of computer science—such as artificial neural networks, genetic algorithms, and evolutionary computation—attest to the influence of biological analogies.”


Ants leave behind them a trail, a biochemical byproduct of the evaporation of pheromone they deposit, which causes the ants to form the shortest routes. This idea has been taken on by computer network engineers in order to design an effective way of send data packets.

The Steiner Tree is related to computer algorithms.[3] As such, the Steiner Tree in a certain sense is an ID prediction because if it shows up in the biological world, it would validate ID Theory.

By the year 2000, an optimization algorithm had been applied to ant colonies, called ant colony optimization (ACO).  Within a couple years, ants were observed to conform to the Steiner Tree by the year 2002.

Last year, biologists at the University of Sydney were involved with researching ant behavior. Their findings are shown here,,ants-solve-dynamic-optimisation-puzzle.aspx. Recently, they collected 60 Argentine ant colonies from university grounds, and forced them to navigate a diamond-shaped maze to reach a food source one metre away.

Based upon observations, ID Theory has been able to propose various hypotheses in this area, such as:

Hypothesis #1: Perhaps molecular machines appear to look designed because they really are designed.

Hypothesis #2:  In the evolutionary process, an increase in biological complexity does not represent a “free lunch” — it is bought and paid for, because random genetic variation is subjected to natural selection by the environment, which itself is already structured.

Hypothesis #3: Ant behavior conforming to the Steiner Tree would be evidence of design because an algorithm is CSI.[4] The test for this hypothesis is the ant behavior. If you place ants in similar situations, their behavior would still conform to the Steiner Tree. The test is falsifiable and repeatable.  If such movement patterns do not confirm to an algorithm, then the hypothesis is falsified for that particular association.

The first two hypotheses are theoretical, typical in such fields as information theory and physics,  Hypothesis #3 is a practical AFFIRMATIVE hypothesis, testable and falsifiable, that can be CONFIRMED by ant behavior.

The following experiment can be set up to demonstrate an OBVIOUS CORRELATION with ant behavior and algorithms.  The success of ants confirming Hypothesis #3 inspires continued research with additional hypotheses and studies. The empirical data speaks for itself.

Showing that a population of ants does not conform to the Steiner Tree refutes the hypothesis set forth by ID that ant behavior conforms to computer algorithms.

ID Theory: Biological information is designed

ID Observation: Computer algorithms are proof of design. Ants appear to behave in logical networks.


Experiment: Provide food source to Argentine ants, and record results.

Results: Argentine ants conform to the Steiner Tree (a computer algorithm).

Conclusion: Hypothesis confirmed. ID Theory upheld.

Comments: In order for science to be science, the TEST MUST BE FALSIFIABLE AND REPEATABLE. This test is falsifiable and repeated. Had the ants NOT conformed to the Steiner Tree, then the hypothesis would be falsified. It’s very elementary.

Supercolony Trails Follow Mathematical Steiner Tree

William Dembski notes an interesting discovery regarding Argentine ants:

“Colonies of ants, when they make tracks from one colony to another minimize path-length and thereby also solve the Steiner Problem (see ‘Ants Build Cheapest Network’). So what does this mean in evolutionary terms? In ID terms, there’s no problem — ants were designed with various capacities, and this either happens to be one of them or is one acquired through other programmed/designed capacities. On Darwinian evolutionary grounds, however, one would have to say something like the following: ants are the result of a Darwinian evolutionary process that programmed the ants with, presumably, a genetic algorithm that enables them, when put in separate colonies, to trace out paths that resolve the Steiner Problem. In other words, evolution, by some weird self-similarity, embedded an evolutionary program into the neurophysiology of the ants that enables them to solve the Steiner problem (which, presumably, gives these ants a selective advantage).”[5]

Dembski brings to our attention in this blog that the ant behavior was predicted by ID informatics experts, and conforms to a computer algorithm. There is a great body of research in this field of study.[6]

ID is a study of information and design. Dembski is saying that in the absence of ID Theory, there is a burden placed upon Darwism to provide a natural explanation as to why ant behavior conforms to a proven and known man-made algorithm. We see these anomalies all the time in the universe, which is exactly why there are laws of physics, and a sense of fine-tuning of the universe. With ID Theory, naturalism falls short of fully explaining extraordinary evidences of design and order that otherwise occur by sheer happenstance, which is an unacceptable conclusion.

Regarding the Argentine ant experiment, ‘Supercolonies’ of 500, 1000 or 2000 workers were studied to identify methods for self-organising sensors, robots, computers, and autonomous cars.  The experiment was set up in the following manner.  Researchers put three or four nests of ants in empty, one-metre-wide circular arenas to observe how they went about connecting the nests.  As with railway networks, directly connecting each nest to every other nest would allow individual ants to travel most efficiently, but required a large amount of trail to be established.[7]

Instead, the ants used central hubs in their networks – an arguably complex design for creatures that University of Sydney biologist Tanya Latty described as having “tiny brains and simple behaviors.”

“We found that ants almost always made networks that minimised the total amount of trail, consistent with optimisation at a colony level, rather than at an individual level,” Latty told iTnews.

The research also revealed the process by which the ants solve network design problems without the help of a leader.  Dr. Tanya Latty, principal author from the School of Biological Sciences said the ants make as many trails as possible, then prune them back to the most efficient configuration.

The story was also covered in Dr. Latty further explained,

“The findings sheds light on how other ‘simple’ natural systems without leaders or even brains – such as fungi, slime molds and mammalian vascular systems – are able to form efficient networks, and can help humans design artificial networks in situations lacking central control.

“Engineers and urban planners face the task of designing efficient and cost effective networks. Building longer roads or tracks requires more resources and is therefore more costly, so a challenge for engineers is to design transportation networks that minimise resource use while still maintaining connectivity between sites such as cities or stations.

“Argentine ants face the same dilemma as transport engineers. This species of ant is a highly invasive pest in many countries because it can form super colonies that consist of thousands of nests connected by a network of pheromone trails. Because longer trails require more pheromone to build and maintain, the ants would benefit greatly from building efficient networks with the shortest possible trail length.” [8]

The researchers hoped that their study of ant colonies would also yield “self-healing” organic computing networks, since nodes were controlled individually and not by a central control unit.


As Jeffrey Kephart noted above, algorithms were connected with biological applications since the 1940’s.  The Steiner Tree goes back to at least 1968.[9] By the year 2000, algorithms were being applied to ant colonies, and observed to conform to the Steiner Tree by the year 2002.  Computer engineers were interested in the ant colonies to help write algorithms because they design their algorithms to have decaying data packets along pathways just like the ants leave a pheromone trail.  The algorithm makes the data take routes where the data packet has decayed the least.

In the instant 2010 experiment, the researchers already knew that the movement of ants adheres to the Steiner Tree.  The researchers conducted this 2010 experiment to better understand the evaporating pheromones. Here, biomimicry was being employed to perform a solution to a computer algorithm problem.

It should be noted that we have here a human design process for network packaging that replicates the processes used by an ant colony. However, this is by no means a fallacy that ID proponents are erroneously making the assumption that because the network connection is designed that the ant behavior must also be designed.  This is a common accusation by the critics of ID Theory.

The point is that over the centuries, design proponents have long held that if nature is designed, then evidence of design would become evident. One of the earlier design proponents was Alfred Russel Wallace. Wallace was the co-founder of natural selection. Wallace also proposed the theory of intelligent evolution.

Algorithms were applied to biology in the 1940’s, and the Steiner Tree (1968) predates algorithmic application to ant colonies (2000) by 22 years.  No one trained ants to form a Steiner Tree.  The Steiner Tree is a valid prediction that the biological world, at least mobilization of ant colonies, are designed.  As noted above, this simple hypothesis is very much falsifiable, testable and repeatable.

[1] Adrian Covert “How The Fruit Fly Could Revolutionise Distributed Computing,”, By on January 15, 2011,

[2]  Jeffrey O. Kephart, “Computer science: Learning from Nature,” Science, 11 February 2011: Vol. 331 no. 6018 pp. 682-683, DOI: 10.1126/science.1201003.


[7] Liz Tay, “Ants build cheapest networks,”, Feb 18, 2011,,ants-build-cheapest-networks.aspx.


[9] E.N. Gilbert,   H.O. Pollak,   “Steiner minimal trees”  SIAM J. Appl. Math. , 16 (1968)  pp. 1–29

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