This is the fifth post in a six-post series on impact by ACS Associate Will Nielsen.
Spoiler Alert: Because the world is exceptionally complex, spending large amounts of resources to calculate a specific number for an impact generated may not be feasible or desirable. Instead, understanding change measurements will often be more beneficial for decision-making, such as how a system moves and how variables grow and shrink.
Dynamics and Complexity
Uffda. So as Herbert says, our puny minds can’t handle much. But, the economy - and more generally the world we live in - is highly dynamic, dependent on many nonlinear and interacting factors. A dynamical system consists of a set of variables moving forward in time, influencing and creating the complete environment. In the study of system dynamics, a primary interest is understanding the qualitative features of solutions, not necessarily the specific quantitative values. In other words, we do not necessarily need to seek out precise numerical value; but rather, understand how certain factors behave through time. For example, it is not always necessary to try to assign a specific dollar value for social and environmental impacts that are inherently poorly understood, although this can still be used in cases to support a message. It may, in cases, be more appropriate to understand how quickly the measurement changes and in which direction. Relatedly, it may be more informative to discover what variables contribute most significantly to the changes you are seeking to track. Sounds easy right?
Complexity forces us to analyze problems with a myriad of constantly evolving methods in order to better understand the existence of causality amongst the various parts a complex system. Garrett Hardin described it well:
In order to understand impacts, it is critical to understand the initial states of systems. Any attempt to determine where a system is headed will inherently depend on where it began. An inaccurate depiction of a system’s starting point (it’s initial state) will only further erode the accuracy of any estimate of its trajectory. For example, an economic model that makes predictions based on rational expectations misrepresents the starting point by claiming humans are rational beings. Most researchers agree that humans often make decisions based on little, if any rationality. Thus, any prediction on future trajectories is likely going to be wrong based on a flawed initial understanding of the system.
In addition to the need for understanding a system’s initial conditions, we also need to recognize the scope of the initial conditions and the ability of a system to scale. Different conditions and different initiatives will result in different potentials for scale. Understanding how scaling could occur and the patterns that will be visible from different perspectives improves the understanding of future impact.
In sum, we’ve got a bunch of complexity and dynamics feeding into our systems of interest. We’ve realized multiple systems and factors are interconnected, but they are difficult to understand thanks to the perpetual issue of imperfect information. That about sums it up. Successfully tackle this challenge—and we will know all impacts about everything. I can’t wait to get started. So what to do? One option is to throw some money at it. That’s right, Part 6 is about investing for impact.