Let’s consider policy feasibility
I was once consulted by a semiconductor equipment maker who wanted to reduce their service maintenance costs. The client team offered a variety of ideas, some of which were workforce-related and could be implemented fairly rapidly, while other options involved product improvement and would require several years of R&D investment. The SD modeling project ended up focusing on workforce changes rather than R&D, because senior leadership wanted to see improvements in the near term, and the R&D approach could not be feasibly accelerated to achieve that goal.
In 1977, my teacher, SD pioneer Ed Roberts, wrote an important paper called “Strategies for Effective Implementation of Complex Corporate Models”, based on his years of consulting experience. When it comes to policy recommendations, he said, “the organizations’ ability to absorb the associated change must be assessed…The model-builder and the organization both profit from implementation of moderate change proposals leading to some successful results; both lose from grandiose plans which fail to be moved ahead.”
This advice is easy enough to follow when the client tells you directly which policies should or should not be considered, as my semiconductor client did. But in some situations, and very often in the public sector, one finds a variety of voices arguing for diverse policies, and no obvious way to know which policies will find broad acceptance. A robust carbon tax may be the best way to reduce greenhouse emissions, but many countries have found it difficult to move forward on it. Similarly, a single-payer health system may be the best way to get universal coverage and reduce healthcare costs in the U.S., but single-payer proposals face a steep uphill battle with a powerful insurance industry in opposition.
Typically, SD modelers have not worried much about policy feasibility in the public sector. Following the lead of Jay Forrester, we have reasoned that if the politicians currently lack the vision to do what is best (based on what a good model shows), then we should take our case directly to the people—in the form of books, editorials, and other forms of advocacy.
But I’ve started to wonder if we could do better. Much of the debate these days on energy policy, health policy, and in other areas is not so much about what is ideally best as what is politically feasible. This debate is passionate but generally uninformed about the relative magnitudes of potential impact and the likelihood of enactment. If one knew these factors, a more rational approach would be possible taking both into account.
Of course, it’s important to keep getting the message out about which course of action has the highest potential impact. We should advocate for carbon taxes and single-payer coverage, if they have the greatest leverage, regardless of their current political feasibility. We shouldn’t limit our advocacy of the best, which might allow opposed vested interests to gain even more advantage in public discourse.
At the same time, we in SD should acknowledge the realistic limits of our influence on the political landscape. Given such limits, shouldn’t we take political feasibility into account when advising decision-makers about how to spend their limited resources?
Without too much extra effort beyond what we already do in modeling, we could assess policy feasibility by studying the political situation and its dynamics, looking at trends in public opinion surveys and other indicator data, and talking to experts. We could then calculate the “feasibility-adjusted value” of a policy by multiplying its potential impact (as determined by our SD model) by its likelihood of enactment.
Let’s make our analyses more broadly useful by estimating not only the outcomes of the various policy options available but also their realistic feasibility.
(Thanks to Tom Fiddaman, Rogelio Oliva, Wayne Wakeland, and Bobby Milstein for providing valuable feedback on earlier drafts of this blog post.)