We use social insect colonies to study strategies that produce adaptive group behaviors. Understanding how and why collective problem solving, task allocation, defense, or the search and collection of resources emerge from individual behaviors provides insight into countless aspects of our own society and engineered systems, and helps us understand the behavioral diversity of social insects and the mechanisms underlying those behaviors.
A social insect colony is a classic example of ‘complexity’, that is sophisticated group-level outcomes, emerging from simple individuals. The main theme of our research is to discover how this is achieved: which organizational strategies do social insects use, and why these have evolved. Answering this ‘why’ question means studying the benefits and costs (i.e. the adaptive function) of different strategies in different social and environmental contexts.
Biological systems are often more noisy than engineered ones; however, they typically surpass artificial systems in their robustness to a wide variety of adversities. Such a trade-off between efficiency and robustness is a central theme in evolutionary biology. We explore how robustness is achieved at the collective level, and why and how it evolves.
All organisms process information, but many ants and bees qualitatively rival any vertebrates in their cognitive skills, including fast learning, trading off exploration and exploitation, evaluation of reliability of different information sources, and using social information. What ecological and social factors lead to the evolution of such abilities? We study what and how information is processed and used in individuals and groups of social insects.
Collective problem solving and complexity
Evolution of cognition
Many social insect colonies cover a foraging range 1000s of times their body size in diameter, despite the fact that typically only 5-30% of the colony’s workers ever leave the nest. How? Do individual scouts use a random walk or systematic search methods? Do they coordinate their searches, and if so, how?
Individuals make mistakes, and social communication can mean that mistakes are multiplied and affect the entire group. How do insect colonies maintain robustness despite individual errors? What failsafes or control mechanisms are employed to reduce the effect of errors, and do they work equally well for all types of errors?
The world, from an insect perspective, can be both unpredictable and dangerous. How do colonies and individuals deal with risks? Do colony or individual status affect how risk-averse social insects are, and under which circumstances may foragers even prefer to invest in high-risk options?
While cooperative inside the colony, many social insects fiercely compete with other colonies of the same and other species. However, the collective and individual strategies used in these contests are poorly understood. What information about enemies is collected, and how? Is individual strength relevant or are fights decided based on colony traits only?
Foragers are often confronted with many options of where to look for food. We know that bees, for example, can use past experience to estimate probable reward; but is information gain important even when there is no difference in expected reward?
Social insect colonies are famous for their intricate division of labor; but many species’ workers are quite generalist, and do not fit neatly into specialist categories. How do these workers decide what to do moment-to-moment? And how well do our theories about task allocation actually work?
Ants meander systematically to search.
As humans, when we search for something (e.g. a lost car key in the grass), we tend to go systematically (e.g. in a spiral or back-and-forth pattern). This is mathematically most effective and has been used by theoreticians and computer scientists as a model for search generally. HOWEVER, this really doesn't work well at all when there are obstacles or navigational uncertainty - i.e. if you imagine a bigger scale, like wandering around a large wood, small mistakes in turning angles will quickly set you on a completely different, and inefficient path, where you miss large areas or cover the same ground twice.
Most literature on animal movement, on the other hand, assumes animals do a purely random (as opposed to systematic) walk/search. Correlated random walk models are standard (correlated just means walking forward more often than turning around, but it's still with random angles and no plan from one step to the next).
Here, we analyze exceptionally long stretches of freely moving animals (ants in this case), and find they actually do something that may be a smart compromise between the two: systematic elements (i.e. meandering left to right, i.e. each step is not independent of the last) combined with randomness (noise in angles and random walking at a larger scale). This might provide a uniquely efficient and robust method of searching a large area and has not been previously described in any animal (there are some cases of well-described systematic search, see introduction in the paper).
Bumble bee workers vary a lot in size - including in the field
That bumble bee workers vary widely in size has been known for a while; however we show here that workers vary in size a lot more than queens or males reared at the same time in the same nest, indicating that worker variation is ‘deliberate’ - ie. adaptive or at least not under the same selection as that of sexuals.
We also show for the first time that this is true for both field and lab colonies, and that workers in field colonies vary in size just as much as those in lab colonies do.
Focus your defense on the assets that have a chance of surviving
Turtle ant soldiers block nest entrances with their disk-shaped head; however, blocking larger entrances, while possible with more soldiers, becomes less effective. If defense has little chance of succeeding because of high level of threat, ants concentrate their defenses and thus give up defense altogether in some sites.
The ant colonies flexible allocation of defenses among multiple nests and nest entrances is shown to agree with optimality model predictions in most cases; however, the lack of global information and coordination among soldiers may also lead to non-optimal allocation, especially when cavities differ in defensibility (entrance size).
Insights about social insects relevant to collective organization, communication, search, and task allocation are being widely applied in computer science to develop better distributed algorithms - i.e. to build better software and hardware running on networks, in cluster computing, and in other contexts. In addition though, distributed problem-solving strategies inspired by ants and bees can and are also being used in logistics, industrial production line management, and other applications.
The particular strength of social insects is that they have acquired and tested their collective strategies over many millions of years.
This means whenever we have a group of collaborating individuals facing a problem that requires both efficiency and robustness to a dynamic environment, social insects may have better strategies already in place than our best managers and engineers can come up with.
Lastly, we believe that we can create a society that is happier and more focused on the beauty in the world if we get adults and kids to be more humble in the face of what we do not know about the world, and to appreciate the intricacy that is represented in every living organism, even in the brain of an ant.
