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For Ants, Building a Bridge Is No “Simple” Task

There is nothing “simple” about designing neural systems and the computer systems to receive and interpret neural sensory inputs

Researching for my previous Mind Matters article about bird and bee biological software, I came across a short piece at Quanta Magazine entitled “The Simple Algorithm That Ants Use to Build Bridges.” Really, a “simple” insect algorithm? Intriguing.

Eric Cassell’s book, Animal Algorithms (2021), reveals the complex and intricate hardware-software systems enabling bird and insect procedures for migration, building nests and structures, social cooperation, and navigation. Grounded in engineering training and experience, Cassell shows that animal algorithms must be designed top-down starting with a goal, fashioning the data input sensors, developing the necessary procedures, and implementing them in software to direct hardware. Yet the Quanta Magazine piece reported that Panamanian army ants’ procedures for building bridges of living ants is accomplished using a “simple algorithm.”

The problem the army ants must solve: crossing gaps and holes appearing in the path of a migrating ant colony. The Quanta piece reports research suggesting the ants deploy an algorithm with these basic elements:

  1. As the colony migrates roughly in a line of ants, the leading ant detects a gap in the path.
  2. Upon detecting a gap, each ant naturally stops.
  3. When the lead ant stops, the ants traveling at 12 centimeters per second behind her start walking on her back. Feeling the trampling triggers her to “freeze” in place, allowing the ants to walk over her.
  4. Repeating steps 2 and 3, the next ant comes to a stopping point (on top of the previous ant), then herself feels the trampling, and freezes.
  5. Each such ant remains frozen in place until she no longer feels any trampling. Then the last bridge link ant picks herself up and marches across the ants in the bridge in front of her. By doing so, she disconnects the bridge from the soil and it swings down vertically. Then, by the same means for each ant in the sequences, the bridge disassembles as its component ants move forward on top of each other.

Seemingly “Simple” Verbs Conceal Immense Sophistication

The army ants’ algorithm does seem simple, if we use easy words and phrases expressing ideas such as “detects a gap,” “freezes in place,” “feels the stampede,” and “unfreezes and resumes marching.” These are commands you could teach a toddler with the promise of a cookie. (Don’t even think of teaching any self-respecting cat these tricks, however.)

Take 99 seconds to view the National Geographic video showing the ants building their bridge structures:

The video shows the “simple” bridge building algorithm gives rise to extraordinarily dedicated and furious activities by hundreds of ants, coordinated in a chaotic interaction that nevertheless builds and maintains a body bridge. Even when researchers intentionally extend the distance of the bridge, the ants recalibrate and continue their efforts successfully. And when the ants are all on or past the bridge, the whole bridge falls vertically, with the ants scrambling methodically on top of each other to finish crossing to the destination side of the gap. 

Buried within the Quanta piece’s common verbs like “detects” is one whale of a lot of hardware and software. To “detect a gap” requires first the sensory hardware. The army ant needs a fully functional neural system with the sense of touch, probably smell, and perhaps some vision. There is nothing “simple” about designing neural systems and the computer systems to receive and interpret neural sensory inputs. Human scientists and engineers have labored for decades working on how to fashion neural-like hardware. 

In parallel with the sensory hardware must be software to use the neural inputs to achieve the goal, in this case, to execute the “simple” bridging algorithm. The hardware alone couldn’t do it. To write an algorithm requires knowing the purpose and plan. There is no reason, and no evolutionary “survival advantage” to the army ants’ algorithm all by itself. There must exist a need to build bridges, envisioned in advance. 

With the purpose and plan in mind, a software designer can develop a set of instructions to carry out the ants’ bridge-building procedure. That work isn’t trivial, instead requiring a detailed understanding of the neural inputs signaling a “gap” to trigger the algorithm. At minimum, the software has to expect and then engage in pattern-matching. The internal subroutine would have IF-THEN-ELSE features, e.g., “IF you receive neural inputs fuzzily matching the ‘gap’ pattern, THEN engage the bridge-building instructions, ELSE keep operating as normal.”

The software that monitors the neural inputs mimics a computer operating system that watches for “interrupts” that call for processing service. The algorithm to engage in bridge-building must be resident in the biological system, waiting for a call from the operating system. 

Closer Looks Discover Even Greater Design Challenges

Breaking the “detect a gap” procedure down into its component systems, for example, doesn’t make any of it “simple.” Animal Algorithms calls such a procedure an example of complex programmed behaviors (CPBs), which analogize to computer software. Even Ernst Mayr, the evolutionary biologist, is quoted in Animal Algorithms as advocating the concept of software programs in biology that draw from “coded or prearranged information that controls a process or behavior leading it toward a given end.” 

To implement an algorithm in software requires at least these five elements:

  • A code system to encode and decode information in symbolic form
  • Physical locations where coded information can be stored and retrieved
  • The algorithm’s series of coded instructions to direct the hardware
  • Initial values stored for when the algorithm starts
  • Some means to cause the algorithm to stop running when finished

Leonard Read’s famous essay, “I, Pencil,” showed that the everyday pencil is anything but simple, standing upon the past and present work of tens of thousands of people worldwide. Likewise, a software algorithm can be designed and implemented only after myriad other tasks are already accomplished. For software to be written, someone first has to design the language, i.e., the syntax, grammar, and semantics for the symbols used. Someone has to write the interpreter software that retrieves the instructions from storage, then decodes the instructions to carry them out. There must also be error detection and correction procedures built-in. These are rigorous, intelligence-intensive tasks.

Software Follows Planning, Designing, and Foresight

The closer we look at the creation of software, the larger the project becomes. At the base of any concept of software is the code system, with the encoder and decoder methods to manipulate the symbolic instructions and data. As Animal Algorithms points out, creating and using a software system requires a known purpose, a concrete plan, and the engineering of the elements. All of these elements require foresight, i.e., anticipating and setting up for future situations and actions. Before the army ants can build any bridges, all of the software has to be written and all of the initial data provided. And the ants must have their previously-designed hardware ready to be directed by software, all working near flawlessly to receive, decode, and execute the instructions from the algorithm to build bridges when needed.    

The Quanta piece promotes a notion that software algorithms are “simple,” perhaps to intimate that undirected natural forces acting upon inert matter would be enough to develop biological software. The piece recites the materialist mantra: “Evolution has seemingly equipped army ants with just the right algorithm for on-the-go bridge building.” To the contrary, dissecting the army ants’ behavior reveals a system it would take an army of human engineers to develop.

Richard Stevens

Fellow, Walter Bradley Center on Natural and Artificial Intelligence
Richard W. Stevens is a lawyer, author, and a Fellow of Discovery Institute's Walter Bradley Center on Natural and Artificial Intelligence. He has written extensively on how code and software systems evidence intelligent design in biological systems. He holds a J.D. with high honors from the University of San Diego Law School and a computer science degree from UC San Diego. Richard has practiced civil and administrative law litigation in California and Washington D.C., taught legal research and writing at George Washington University and George Mason University law schools, and now specializes in writing dispositive motion and appellate briefs. He has authored or co-authored four books, and has written numerous articles and spoken on subjects including legal writing, economics, the Bill of Rights and Christian apologetics. His fifth book, Investigation Defense, is forthcoming.

For Ants, Building a Bridge Is No “Simple” Task