Embracing Complexity

We consider many of our data driven decision making to be complex because of the interactions and co-evolution of natural and human systems. Many of our current and emerging resource problems are interconnected and cross boundaries, domains, scales, and sectors. These boundary-crossing problems are dynamic, non-linear, and are often interconnected with other issues.

To address these persistent resource problems, we need to start by acknowledging the limits of what we can and can’t know and the role of uncertainty, contingency, and complexity in our actions. We need to recognize the disconnect among values, interests, and tools, as well as problems, policies, and politics. Scientific and technological solutions are desired for efficiency and reliability but need to be politically feasible and actionable. To understand, explain, and manage these dynamic and coupled systems, we engage a diverse set of perspectives, methodologies, and tools from complexity science and negotiation theory involving multiple disciplines and knowledge systems. We begin with the following premise and propositions:

  1. Complexity of access, availability, and use of resource problems arise from the coupling of natural and human systems. For these problems, observation-based technical (what is) cannot be easily separated from the value-based socio-political (what ought to be) plurality of the problem description.
  2. Solution space for these complex problems – with interdependent variables, processes, actors, and institutions – can’t be pre-stated. Duality of representations (numbers or narratives; facts or values; objective or subjective) for these problems are inadequate.
  3. Complexity is emergent. It is neither generalizable nor specifiable.
  4. Differentiate complexity from deterministic certainty and statistical uncertainty & identify conditions (not cause) for emergent patterns.
  5. Explore and adapt shared vocabulary and methods from appropriate disciplines and domains that are rooted in and bounded by scientific rationality and interpretive plurality.
  6. Use rigor of scientific methods as the principle to derive facts while adherence to a negotiated application of sustainability and equity as guiding values to design and implement pragmatic interventions.
  7. Focus on identifying and implementing societally relevant technological solutions given the context, constraints, and capacity of a given system.

D3M@Tufts provides and nurtures a collaborative and inspiring space to advance understanding and implementation of data driven decision making to address complexity of resource access, availability, and use problems with actionable outcome.