Research in our lab focuses on the emergence of collective pattern from individual behavior. We are using social insect colonies as a model for complex systems in general, and employ a combination of empirical studies and theoretical approaches. Such studies have implications for our understanding of general principles of distributed problem solving in engineered systems, biological networks, human societies, cellular interactions, etc., but also in explaining the diversity of behaviors in social insects.
Please note that the links below are meant to give a general overview, and are not frequently updated. You can follow us on Twitter - @dornhaus will tweet about new research articles from our group. Please feel free to email me at firstname.lastname@example.org if you are interested in a specific area, and I can send you the appropriate references/papers. If you would like a more accessible summary of research highlights, check out the public media page.
1. Collective problem-solving strategies
The main theme of our research has been to discover which organizational strategies social insects use, and why these have evolved. Answering this ‘why’ question here means studying the benefits and costs (i.e. the adaptive function) of different strategies in different social and environmental contexts. Examples are strategies used in search, communication, task allocation, and collective decision-making.
2. Efficiency, flexibility, and robustness in complex systems
A central theme across evolutionary biology is that efficiency (optimized function under a defined set of circumstances) may trade off against flexibility (robustness to change or noise). Organisms as well as organizations, including insect colonies, thus need to balance efficiency and flexibility, e.g. when employing specialized or generalized workers. We have studied which traits at both individual- and group-levels contribute to flexibility and robustness, and under what conditions they are likely to evolve. Central topics are learning and cognition, specialization and division of labor, and ‘personalities’ and behavioral syndromes.
3. Evolution of complexity
Many biological innovations involve the evolution of increased complexity, generating systems of interconnected parts, sophisticated internal organization, and ecological success. Social insect colonies are one example of such evolution towards complexity. But what factors actually promote the evolution of more complex systems? Central topics here are colony/group size and sophistication of individuals vs groups.
4. Applying insights about complex systems across disciplines
Insights gained about insect colony organization can be applied to other complex systems, from distributed computing to human organizations. Vice versa, concepts and methods derived from computing, economics, systems biology, or ecology can be generalized and applied to understand biological systems, including colony organization.
5. Developing research infrastructure
We are helping to develop and distribute a software that can function as a computer-automated tracking tool for videos of moving ants or other objects, the ABCTracker. We have also developed R packages and articles discussing analysis methods, and developed online resources for junior scientists (including students), as well as online resources (including specific curriculum materials) for Pre-K-12 schools, particularly elementary schools. Finally, please see the teaching page for our aim to improve training infrastructure and quality to better prepare a new generation of professionals and scientists to explain and defend how only rigorous science can produce objective, i.e. true, insights about the world around us.