In early 1987, a study of 145 mitochondrial DNA samples from women representing a variety of populations, conducted by biochemists and geneticists, was published in Nature. Using a complex analytical model based on mutation rates, the authors determined that all living people have a common ancestor, later dubbed Mitochondrial Eve, who lived in east Africa between 140,000 and 200,000 years ago. This was a blow to the multiregional hypothesis promoted by several prominent paleoanthropologists, which asserted that the fossil record showed continuous evolution over the last two million years in widely distributed locations. But recently, a team of geneticists, paleoanthropologists, and other scientists collaborated to develop a new model. And their approach has important lessons for those of us who manage teams of knowledge workers with diverse specialties.
Acknowledge Biases and Assumptions
Every well-developed knowledge specialty has its own culture, models, methodologies, favored data sources, and assumptions. Consequently, practitioners have biases that reflect their specialty. The scientists in this interdisciplinary team, led by archeologist Eleanor Scerri, wanted to avoid letting their professional biases lead to “cherry picking across different sources of data to match a narrative emanating from one [field].” So, the team met for three days to review each other’s work—challenging assumptions, noting accomplishments and problems, and learning to communicate effectively with their colleagues in other specialties. This process led to a coherent view, goodwill, and mutual respect. Lesson learned: many of our biases arise from deep knowledge in our specialty and confronting them early can facilitate cooperation and team building.
Develop a Common Vocabulary
Paleoanthropologists, geographers, geneticists, and environmental scientists have very different ways of talking about their work. Each field has its own jargon, buzzwords, and acronyms. Scerri noted, “[Our] understanding of findings tends to be influenced by the models and paradigms we have in our heads, which tend to … [affect] how we process new information.” The team had to pool their knowledge in a way that let them share data, methods, and models in a way that didn’t leave anyone out. This required them to adapt their communications to use terminology that was meaningful to the entire group and avoid a dependence on jargon. Lesson learned: time invested in establishing a common vocabulary facilitates understanding and leads to real progress.
Become Accustomed to Conflict
The researchers were able to reconcile their different theories into a cohesive story that accounts for the complexity of the different data points and leaves room for the abundant ambiguity still present. Scerri noted, “Insights from different models can help to shed light on the answers we look for … it’s all about incremental steps and changing perspectives.” Lesson learned: conflict can often be resolved, but even when it can’t, the root of the conflict is often based in some ambiguity. Acknowledging that ambiguity is a step toward a tentative agreement, pending eventual resolution of the ambiguity.
Scerri and her colleagues recognize that, like humanity itself, their model is still evolving. New data and new ideas will inevitably lead to future refinements, and they are fine with that. And that might be the most important lesson of all: you don’t need to be absolutely certain in order to deliver something of immediate and future value.
And if you’re curious, here’s a link to their paper.