For starters, let's look at ants. Drop a piece of sugar on the floor and wait. Sooner or later, one or two straggling ants scoping the area will stumble upon it. What happens next is perhaps one of the most elaborate orchestrations of organized teamwork in nature.
Ants aren't clever little engineers, architects, or warriors after all--at least not as individuals. When it comes to deciding what to do next, most ants don't have a clue. "If you watch an ant try to accomplish something, you'll be impressed by how inept it is," says Deborah M. Gordon, a biologist at Stanford University.
How do we explain, then, the success of Earth's 12,000 or so known ant species? They must have learned something in 140 million years.
"Ants aren't smart," Gordon says. "Ant colonies are." A colony can solve problems unthinkable for individual ants, such as finding the shortest path to the best food source, allocating workers to different tasks, or defending a territory from neighbors. As individuals, ants might be tiny dummies, but as colonies they respond quickly and effectively to their environment. They do it with something called swarm intelligence.
Once the food source is detected, more and more ants begin to march toward it. At first, the patterns of their movements may appear random and erratic. However, as each ant gathers and exchanges more information with its neighbors, their collective intelligence grows. They begin to orient and self-organize. They become more stable. They form a swarm.
A swarm represents agents that interact with one another, exchanging information among themselves. The goal: to be better organized, collectively smarter and more effective at performing tasks as a group.
One key to an ant colony, for example, is that no one's in charge. No generals command ant warriors. No managers boss ant workers. The queen plays no role except to lay eggs. Even with half a million ants, a colony functions just fine with no management at all--at least none that we would recognize. It relies instead upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.
USC Viterbi PhD candidate Hana Koorehdavoudi has studied swarms the past three years. Under the mentorship of Paul Bogdan, an assistant professor in the Ming Hsieh Department of Electrical Engineering, Koorehdavoudi has developed a series of algorithms that can actually quantify the degree of complexity within swarms.
Koorehdavoudi's calculations measure the interactions within complex systems. These calculations are unprecedented because they allow scientists to identify and evaluate the types of exchanges that result in certain forms of collective behavior -- and that could help scientists engineer specific outcomes by simply tweaking the interactions that exist within a network.
The inspiration behind this unique approach was the "energy landscape," which represents all the possible dynamic formations of agents within a swarm.
"The energy landscape helps provide an understanding of how the dynamics of the swarm evolves through time," Koorehdavoudi said. "This lets us identify and extract how the agents relocate themselves with respect to others in the system."
In addition to the energy landscape, the team also used the concepts of emergence, self-organization and complexity to describe the evolution of the swarm:
- Emergence occurs when a more complex system is created as a result of the growing number of interactions or interdependencies between individual agents. Imagine the ant swarm described earlier.
- Self-organization goes hand-in-hand with collective intelligence. The more self-organized a system, the higher its collective intelligence. This is because self-organization emerges through increased interdependencies and interactions, which is fundamentally how information is shared across networks.
- The last construct, complexity, is the product of emergence and self-organization together. Higher emergence and self-organization mean a more complex system.
Research and Development
Bogdan and Koorehdavoudi believe that the algorithms can have an impact on scientists' efforts to understand, optimize and control complex networks. Examples of the applications are advancing research and development in the areas of robotics, urban planning or even cancer treatment.
The research by Bogdan and Koorehdavoudi could also help solve everyday problems such as traffic. Their work might even inspire new approaches for controlling the spread of cancerous cells -- both scenarios involve individual agents that interact and move together (i.e., automobiles and cancer cells).
Ultimately, the phenomenon as explored by Bogdan and Koorehdavoudi is universal.
"You can also take these formulas and apply them to the brain and see how brain organizes a thought," Bogdan said. "You can model the thinking process."
Indeed, the possibilities are endless, and all due to the wisdom of crowds -- in this case, the humble ants, pigeons and bacteria.