Social insect colonies solve problems: like any animal, they have to collect food, find shelter, compete, reproduce, and so on. Unlike many animals, they typically solve these problems as a group, and the group-level behavior is self-organized, meaning it emerges from actions and interactions of many individuals, rather than being driven or organized by a single leader or hierarchy.
Here we describe what we have found in terms of which problems are solved, what distributed strategies are used to solve them, and what we know about why particular strategies are used, i.e. the benefits and costs of different organizational or collective problem-solving strategies under particular conditions.
Search: How do a small number of tiny insect effectively search vast areas, often hundreds of meters (or several miles in the case of flying bees) across? Any animal has to search for food, but since in social insects only about 10% or so of the colony's workers actually forage, these individuals have to search much larger areas than solitary insects to gather enough food for the entire colony. In addition, social insects are generally central place foragers, that is, they all start from and return to a single point (the nest entrance). This means there is a lot of potential for wasted time searching areas you or your nestmates have already visited. How do social insects solve this problem?
Communication: The problem of optimal communication in groups, i.e. how and what to communicate, and how to manage information flow through an organization, is faced by all adapted complex systems. In particular communication involves tradeoffs, since communication can be costly not only because of the act of producing a signal can be, but because it can require overhead costs in worker time spent checking/processing information that may be irrelevant; furthermore, it can make groups less flexible. We study such sometimes counterintuitive benefits and costs primarily in the context of search for resources (see also above): how can insect foragers improve their efficiency as a group by exchanging information about spatially and temporally varying resources?
Task allocation: How and why do individuals choose their role in the colony, e.g. their particular work-task (such as brood care or defense), and how does this lead to colony-level efficiency and robustness? This problem appears across complex systems, and maps to many other allocation problems (i.e. allocating any kind of resource, not only workers, but also energy, money, or time, to a diverse set of sites, which could be spatial sites, tasks, or functions). Much classic work on social insects concerns the question of how long-term preferences of individual workers are formed (e.g. larger or older workers often prefer foraging over brood care), but we also study the more dynamic, short-term processes that actually achieve a match between the demands for work in different areas and the number of workers that decide to take on these tasks.
Collective decision-making: Here we study how a group of individuals, such as an ant colony, can arrive at a consensus decision, in other words, a decision where the colony as a whole has to choose a single alternative. This is different from a case where each individual can decide separately, such that the colony divides up over a set of alternatives: for example, in foraging, each forager chooses its own resource to exploit, and this leads to a collective outcome (a certain distribution of foragers over resources) that may be adapted. But some decisions fundamentally cannot be made up of lots of independent decisions, for example the 'house-hunting' decision of a colony moving to a new nest site. If the colony wants to maintain its integrity, all ants in the end have to move to the same site, i.e. a consensus has to be reached. We have studied this process particularly with regard to how these consensus decisions prioritize speed or accuracy, and how the process used by ants closely resembles the decision-making process employed by neurons in our brain.
Many systems in nature, engineering, and human society are 'adapted complex systems', i.e. they consist of many somewhat autonomous interacting parts but are optimized (or should be) to function well as a group. Social insect colonies are one example of this (evolved to maximize colony fitness through individual worker actions), but so are multicellular organisms (evolved to maximize organism fitness through individual cell actions), cluster computers (multiple 'nodes' contributing to a calculation/process), power grids, and companies or organizations (with group-level goals and individual human contributors).
We believe that by understanding how social insects solve the general problems described above, we will get better inspiration for, and applied benefits in engineering/applying the algorithms or behaviors that will lead to better system-level performance.
This idea, bioinspiration or biomimetics, is particularly relevant in the context of complex systems because people are not very good at designing individual-level rules for best system-level outcomes. Despite our high intelligence and creativity, our mind essentially evolved to think of group tasks as best solved by hierarchies or linear processes. Thus, it is actually very difficult for us intuitively understand what group-level outcomes will result from certain individual-level behaviors, and therefore, it is also difficult for us to engineer desirable individual-level algorithms.
While much of our work is thus inspired by the desire to understand social insects, who are beautiful and wonderful in their own way, we also want to contribute to the broad understanding of fundamental principles that govern all of life and much of the human experience: understanding how complex system behavior emerges from individual behavior, and why particular strategies of organization work better than others and in which environments.
More directly, we also work on directly translating biological insights for practical engineering applications.