Falling in love with the problem, not the solution

Guest blog by Kyle Novak

This is a blog series written by the alumni of the Implementing Public Policy Executive Education Program at the Harvard Kennedy School. Participants successfully completed this 6-month online learning course in December 2020. These are their learning journey stories.

“Fall in love with the problem, not your solution.”  It’s a maxim that I first heard spoken a few years ago by USAID’s former Chief Innovation Officer Ann Mei Chang. I’ve found myself frequently reflecting on those words as I’ve been thinking about the challenges of implementing public policy. I spent the past year on Capitol Hill in Washington, D.C. working as a legislative fellow, funded through a grant to bring scientists to improve evidence-based policymaking within the federal government. I spent much of the year trying to better understand how legislation and oversight work together in context of policy and politics. To learn what makes good public policy, I wanted to understand how to better implement it. Needless to say, I took a course in Problem Driven Iterative Adaptation (PDIA), a framework to manage risk in complex policy challenges by embracing experimentation and “learning through doing.”

Congress primarily uses legislation and budget to control and implement policy initiatives through the federal agencies. Legislation is drafted and introduced by lawmakers with input from constituents, interest groups, and agencies; the Congressional budget is explicitly planned out each year based on input from the agencies; and accountability is built into the process through oversight mechanisms. Congress largely provides the planning and lock-in of “plan and control” management based on majority political party control and congruence with policy priorities of the Administration.  But, it is difficult to successfully implement a plan-and-control approach when political, social, or economic situations are changing.

Take the problem of data privacy and protection. A person’s identity is becoming largely digital. Every day each of us produces almost a gigabyte of information—our location is shared by our mobile phones, our preferences and interpersonal connections are tagged on social media, our purchases analyzed, and our actions recorded on increasingly ubiquitous surveillance cameras. Monetization of this information, often bought and sold through data brokers, enables an invasive and oppressive system that affects all aspects of our lives.  Algorithms mine our data to make decisions about our employment, healthcare, education, credit, and policing. Machine learning and digital redlining skirts protections that prohibit discrimination on basis of race, gender, and religion. Targeted and automated disinformation campaigns suppress fundamental rights of speech and expression. And digital technologies magnify existing inequities. While misuse of personal data has the potential to do incredible harm, responsible use of that data has the power to do incredible good. The challenge of data privacy and protection is one that impacts all of us, our civil liberties, and the foundations of a democratic society.

The success of members of Congress are often measured in the solutions they propose, not the problems that they identify. Because lawmakers and their staffs want to stand out from their peers by advocating unique solutions to problems, there is an incentive to quickly move from problem identification to solution generation. Likewise, there is an incentive in creating legislation to gravitate towards proposing a solution rather than fully defining the problem. And because legislation is at least to some degree political (and because all politics is local), there is a tendency to frame the problem from a more narrowly defined political lens. Indeed, leaping too quickly to a candidate solution rather than spending more time to better understand the problem might focus on some aspects of a problem (perhaps symptoms or other easily measured data) rather than looking at other possible root-causes it. It may be easy to focus on technology such as facial recognition systems themselves as being the problem instead of these systems exacerbating and automating the real underlying problems of mass surveillance and racial bias. A policy framework instead needs to examine how civil liberties and racial equities are being protected or undermined by any technology. The government (perhaps because it tends to be bureaucratic and procedures-based by design) often looks at future problems using the lens of past solutions. This framing is a form of survivorship bias, in which past successful interventions might be attempted to be replicated without fully understanding why and in what context those interventions worked. All of this demonstrates why falling in love with the problem and not your solution is essential in complex challenges.

Indeed, Matt Andrews has said “complex challenges cannot be solved, they can only be managed.” Complex challenges are characterized by unknowns within an interconnected system, unknowns that involve risk. Leadership in the face of such challenges is about taking informed risks. It is about using available information and a diversity of ideas to characterize the problem. Ronald Heifitz has argued that we don’t need leadership when we know what to do, we need it in the face of a challenge. Complex challenges have many stakeholders with many competing interests, and managing such challenges is about the distribution of loss and about mobilizing and enabling others to take purposeful risks with you. Through this social framing we encourage others to invest themselves in the challenges that we care about. The U.S. Congress is now more politically divisive than it has been at almost any point in the past one hundred years. Convincing others that any policy challenge is important enough to devote time and resources requires political capital. Lawmakers must use leadership in compromising good politics to achieve good policy, by supporting a solution that cannot possibly satisfy every competing interest of diverse stakeholders, and by doing what Marty Linkski’s has called “disappointing your own people at the rate that they can absorb.”

So, how exactly does the PDIA toolkit help you to fall in love with the problem, not with your solution? The framework, developed by the Building State Capability program at Harvard, rests on four principles: local solutions for local problems, experimentation and positive deviance, experiential learning with evidence-based feedback, and engagement through multi-agent leadership. Multi-agent leadership mitigates risk by sharing it and building legitimacy by connecting with a broader array of stakeholders. Multi-agent leadership is also necessary when systematically deconstructing the problem through root-cause analysis, which reframes the problem as a series of “why” questions. Root-cause and change-space analysis help identify entry points for experimentation.  For example, when thinking about the problem of data privacy and protection, we identified a set of policy experiments that fit into the outcomes of the broader challenge that can be performed independently:

  • Workers data privacy protection focuses on broadening and modernizing existing laws regarding the use of an employee’s data. This is especially important as we see a shift in work to independent contractors due to greater automation, digital revolution, gig economy, and the pandemic.
  • Federal data stewardship focuses on establishing unified standards across the different federal agencies on how personal data is collected, used, and shared.
  • Transparency in data brokering laws ensure that everyone knows and understands what personal data they are sharing using mobile apps and other digital technologies.
  • And, algorithmic and data labelling laws empower individuals by making them aware of how that data is collected and used in decision-making algorithms. For example, FICA scores are fairly transparent allowing for a degree of oversight, but new generations of machine learning algorithms being used to make decisions often obfuscate latent and systemic biases through proxy variables.

The PDIA framework helps multiple stakeholders become smarter on complex issues over time and it allows them adapt to changes in technology and the political environment. Finally, the iterative learn-and-adapt process helps ensure that the policy continues to get attention as it matures and gains legitimacy.

For more information on the PDIA, check-out the Harvard Building State Capacity webpage.

Learn more about the Implementing Public Policy (IPP) Community of Practice and visit the course website to apply.

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