Complexity: More than the sum of its parts

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ASU’s interdisciplinary approach provides a perfect environment for exploring the complex systems underpinning life, society and advanced technology.

By Erin Barton

Aug. 12, 2016

“I can calculate the movement of stars, but not the madness of men.”  – attributed to Isaac Newton

“What Newton was saying was that I understand complicated phenomena–physics–but I can’t understand complexity.” –David Krakauer, president, Santa Fe Institute

When birds fly together in a flock they seem to move as one—a great serpent in the sky, twisting about as if controlled by one brain. Then again, aren’t individual humans made up of a vast number of cells, each with its own life? Maybe that flock in the sky is a serpent of sorts, and all the birds are its “cells.” Perhaps people are cells in the “organism” of a city.

The science of understanding such systems of interdependent parts is aptly named “complexity.” The field is young, highly interdisciplinary by necessity and so cutting-edge it is often off the edge of the map. But many of the challenges we face today involve complexity. Arizona State University is transforming the way we study the dynamics of these interconnected social and natural systems.

What is complexity?

The Santa Fe Institute, an independent research and education center that addresses complex problems, defines complexity as “the evolved order inherent in the living world.” This order occurs in “ubiquitous patterns that repeat throughout living nature: networks, conflict and cooperation, distributed decision-making, the structured flow of energy, and elements of invention and novelty. These patterns are found at all scales, from the molecular, through tissues, individuals, technology, the economy, and cultures.”

In 2015, ASU and the Santa Fe Institute launched the ASU-SFI Center for Biosocial Complex Systems to advance understanding of problems that stretch across complex systems. It is one of four centers under the umbrella of ASU’s Biosocial Complexity Initiative. The others are the Center for Social Dynamics and Complexity (CSDC); the Simon A. Levin Mathematical, Computational and Modeling Sciences Center; and the Center for Behavior, Institutions and the Environment.

Michael Barton, director of the CSDC, likes to use the metaphor of a watch when introducing the concept of complex systems (CS).

“If you dismantle [a watch] and study the parts separately, it would be very difficult to understand what it does or how it does it,” says Barton, a professor of anthropology in ASU’s School of Human Evolution and Social Change.

The pieces of a watch working together and creating a timepiece is an example of emergence, just like cells creating higher-order life forms. An emergent system can’t be broken down into individual parts, because the system behavior does not arise directly from the individual pieces.

Emergence is one of the characteristic properties of CS. Others include dynamic interactions and hierarchical or nested groups–often represented using networks. These shared characteristics are the best way to identify a CS and the closest thing to a definition the field has.  

Many of the complexity programs around the country, which are rare, emphasize computation and networks—sort of the pure physics of complexity. ASU is a leader in exploring complex adaptive systems in the life and social sciences.

A complex adaptive system (CAS) is a type of CS. A watch is a CS, but not a CAS. If it is submerged in water or if one of the gears breaks, it cannot adapt to the situation. It simply ceases to function. A CAS, however, will react to changes in both its internal and external environments.

“Although most of the universe is not a CAS, a lot of the universe that we are most concerned with is a CAS. This includes all life, all society (that emerges out of living CAS), and the technological systems that we depend on (that emerge out of social CAS),” says Barton. “Trying to understand these important systems as a CAS gives us better insight into how they work, how they break down, how they can be improved and how they change.”

Adaptive insects

One example of a CAS is a colony of social insects, such as ants, bees, wasps or termites. Yun Kang, an associate professor of mathematics in ASU’s College of Letters and Sciences, is developing a network model of division of labor in social insects to examine how adaptive complexity arises within colonies.

She collaborates with Jennifer Fewell, a behavioral ecologist in ASU’s School of Life Sciences, and Dieter Armbruster, an applied mathematician in the School of Mathematical and Statistical Sciences, on the National Science Foundation-funded project.

The researchers combine their modeling approach with empirical tests of how differently sized insect colonies regulate the division of labor. They have discovered that small colonies invest more resources into colony growth, directing more worker ants toward riskier jobs like foraging. In larger colonies, more workers perform safer tasks inside the colony. The team also found that “social interactions among different task groups play an important role in shaping task allocation, depending on the relative cost and demands of the tasks,” according to Kang.

Social insects make excellent research subjects for studying complexity.

“They live in intricately governed societies that rival our own in complexity and internal cohesion,” says Kang. “For example, ant and bee colonies can select the best among several nest sites that differ along multiple dimensions by a consensus-decision-making process that resembles a voting procedure.”

In 2009, ASU biologist Stephen Pratt explored this decision-making process among ants and found that the insects can actually make more rational choices than humans.

“This paradoxical outcome is based on apparent constraint: most individual ants know of only a single option, and the colony’s collective choice self-organizes from interactions among many poorly informed ants,” explains Pratt, an associate professor in the School of Life Sciences.

ants with rainbow dots on them

When choosing where to nest, each ant typically visits only one site. An ant will rate a site’s suitability and return to the colony to “advertise” its value. The more she likes it, the more she tells other ants about it. They then visit the site and might come back and advertise it more.

Anyone who uses the Internet is familiar with this kind of positive feedback loop. Google ranks websites in part based on how many other sites link to them. Many sites promote their “most popular” content, leading even more viewers to look at it. Social media sharing has similar effects.

But what’s popular on the Internet isn’t always the content with the highest quality or accuracy. The fact that ants don’t make comparisons between options actually helps them to make more rational decisions than people do. When Pratt forced ants to consider multiple options, they made the same irrational decisions that humans make.

Applied ant algorithms

Studying insect societies can enhance our understanding of complex human social and technological systems, with a multitude of potential applications. For example, ASU engineer Spring Berman has enlisted Pratt’s help in developing effective multi-robot systems, or robot “swarms,” that imitate the ways social animals act individually and collaboratively to achieve a common goal.

“I’m excited to be working in swarm robotics because it’s a fairly new and highly interdisciplinary field, with many applications ranging from environmental monitoring, exploration and disaster response to biomedical applications at the micro-nano scale, such as medical imaging and targeted drug delivery,” says Berman, an assistant professor in the School for Engineering of Matter, Transport and Energy.

Kang also envisions many opportunities for applying insect-based models to human problems.

“Research on how information spreads can be applied to looking at how disease spreads and looking at how social insects restrict the spread of disease can be beneficial to disease control in cities,” she says. “Furthermore, balancing efficient movement and communication within the colony with effective nest defense could have military applications.”

Kang and her team have already developed a set of network and multiscale models to test hypotheses on how social groups function and on dynamical behavior. They used empirical tests of insect colonies to help refine their models, which are general enough to be applied to human societies. Armbruster has used similar models to examine social interactions among the Maasai people of Kenya and Tanzania.

They are also interested in the origins of cooperative behavior. The team is comparing two species of ants, one in which queens start colonies on their own and another in which queens work together.

“What causes them to collaborate?” Kang asks.

In Fewell’s lab, normally solitary queens are being paired up to see what types of environments might encourage collaboration to take place. The results of their project could help answer questions about human cooperation.

Cooperation and other interactions that take place between individuals can have a big effect on the behavior of a system as a whole. This goes back to the themes of hierarchy and emergence. If one cog doesn’t fit with another, the whole watch breaks. If one bird flies out of sync, the flock’s flight pattern changes. If one cell goes rogue, the body develops cancer.

Complexity research can help us address local and global challenges from traffic jams to climate change, from local disease outbreaks to the global spread of extremist violence.

“As an overall theme, I think we can argue that [ASU’s complexity research] is about developing a new vision for solving so-called ‘hairy’ problems—problems for which there are no easy solutions because they are too complex, involving too many factors and decisions,” says Sander van der Leeuw, co-director of the Biosocial Complexity Initiative and a Foundation Professor in the School of Human Evolution and Social Change.

At ASU, this work involves research on topics as varied as predicting pandemics, decision-making and collective action, developing more sustainable human-land interactions, predicting and directing innovation, and urbanization, among others.

To address these challenges, ASU has created its own complex network of collaboration and innovation, connecting scholars from many disciplines and creating new paths for modern research.