How Organizations Plan, and Why It Shouldn't Be Institutionalized Advice

by Kent Aitken

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The last couple posts have been working through a question about how organizations plan. The long story short:

• We've institutionalized oversimplification (a symptom being the "Elevator Pitch")
• Correcting that oversimplification is likely an unrealistic goal
• The solution (according to Charles Lindblom) is letting policy decision-makers throw rational planning out the window and "try stuff"
• To avoid unfairness, policy proposals will be closely watched by an ecosystem of stakeholders: groups responsible for related goals in government, lobbyists, NGOs, and think tanks

On the surface, this sounds like the zeitgeist: experimentation, innovation, and collaboration. However, the "try stuff" here refers to large-scale national policy, not pilots: "trying stuff" on, say, tuition subsidies has a massive impact on people's lives. And the role of that "ecosystem of stakeholders" isn't collaboration: it's recommendation, or advice.

Recommendation-Based Governance

In the last post, I linked to Yves Morieux, who breaks down the economics of multi-stakeholder decision-making: where one person owns the decision, but not the inputs required to make it. He paints a portrait of a car manufacturer, in which the lead designer must satisfy the organizations' experts in noise reduction, fuel efficiency, repairability, safety, and much more. It's easy to imagine how fuel efficiency and safety could be at odds: do you make a car lightweight, or an urban armored personnel carrier? So we have a designer, whose bonus but not core salary depends on performance pay that is based on competing goals decided by 26 different people. Which makes their incentive to care about any individual one of those goals very close to zero.

Recommendation-based systems do nothing to address the asymmetry between the incentives of those involved. Put simply: recommenders don't get paid to contribute to the best outcome. They primarily get paid to promote the variable they represent, as loudly and voraciously as possible. They do not get paid to look for compromises, concede when others make valid arguments, or even to develop long-term relationships and credibility. In the above example, the safety expert's concern is chiefly to understand the optimal outcome from a safety perspective; it's the designer's job to worry about how to square that with fuel efficiency. 

Why is this? It's partially innocent bias: people care about what they know about. I'm sure far more than 50% of the population thinks their expertise is of above-average importance. But more so, it's that the people that hold recommenders to account are a step removed from the decision space themselves, and likewise rely on oversimplified elevator pitches for setting goals. It doesn't help that recommenders rarely receive any feedback about the results of their role in the decision.

Recommendations exist in a partial vacuum, whereas decisions exist in an ecosystem. 

So what's the solution? Morieux proposes six elements (paraphrasing):

1. Ensure that players in the ecosystem understand what the others do
2. Reinforce integrators
3. Remove layers
4. Increase the quantity of power so that you can empower everybody to use their judgment
5. Create feedback loops that expose people to the consequences of their actions
6. Increase reciprocity, by removing the buffers that make [people] self-sufficient

In other words: make people meaningfully responsible for the outcomes of their work, make people responsible for collaboration, and make sure they can see and understand the ecosystem.

No amount of communication or planning can solve this issue entirely - the change has to come in how power is distributed and used.

On the diversity of perspectives

by Nick Charney

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I'm the pen on a fairly substantive report at work; it's interesting work and I'd give you the details but can't due to the terms of the contract. As one would expect, the work entails ingesting a tremendous amount of information culled from previous reports, project documents, presentations, etc. But it also entails conducting, recording and synthesizing approximately 40 in person interviews up and down the food chains of multiple organizations. While I've generally always understood the implicit value of gathering diverse perspectives, this recent undertaking has driven the point home pretty hard.

What I've learned

A document review is one thing; documents are sanitized, on message, vanilla.People are something together different; messy, wandering, visceral.

You learn a lot more about a given situation by speaking to people than you do by reading what they've written. Yes, reading the latter may help inform your approach vector with the former, but if push came to shove I'd take 40 people over 40 pages any day of the week.

Interacting with people allows you to scratch beneath the surface, identify zones of disagreement, and pick apart people's perceptions of what worked, what didn't and why. You'd be surprised how views differ and how reconciling those views provides you with a picture that is far closer to an objective reality than any individual account.

What's the bigger lesson?

We spend a whole lot of energy on producing products, moving them up the line, and across organizational boundaries. But maybe if we were really interested in unlocking a deeper understanding of what we do, how we do it, what works and what doesn't, we'd be better off spending less time pushing paper through it and more time talking to people about how they experience it.

Rational Planning or Muddling Through

by Kent Aitken

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Last week's post was about how organizational language, culture, and processes encourage the oversimplification of both problems and solutions (see: Boundaryless Problems and the End of the Elevator Pitch). It makes it easy to ignore the context of problems, and hard to appreciate the indirect or long-term benefits of any action.

(I've written in the past about how this hampers innovation and restricts collaboration, and proposed strategies to overcome it. Further back, I wrote a deeper dive about its adverse effects. I'll stop hammering on this theme soon.)

But after writing the post, I kept wondering if the idea was remotely useful to on-the-ground public servants. So, we tend to oversimplify things. Is that a necessary shortcut? Especially given the competing demands on our time? Or a lens that can help improve our planning? I'm not sure.

There are a few possible scenarios for this "ecosystem of problems and solutions" lens:

1. It's false
2. It's true, but useless
3. It's true, but only useful in some situations
4. It's true, but requires a particular response to be useful

No plan survives contact with the enemy

I want to dig into 2 and 4, starting with 2. It's true, but useless. Yes, there's an ideal state, in which we tackle a given problem exactly the way we should. But day-to-day, there are multiple problems, approaches, and solutions competing for our time and attention (see: Idealism and Pragmatism for Organizations). Maybe an 80% effort is less than ideal for a given problem, but best for the portfolio of problems we're facing.

Paul Wells led us to an interesting possibility for 4. It's true, but requires a particular response to be useful in his book The Longer I'm Prime Minister. He pointed to Charles Lindblom's The Science of Muddling Through, the long story short of which is that yes, public policy is impossibly complex (zero hyperbole), so the only way of understanding one's own preferences is actually to choose a direction and run with it. It's ten pages long, and I highly, highly recommend reading it - first for the above, and second to note that the verbiage of "complex public policy problems" is not a new phenomenon based on modern global finance, terrorism, or digital interconnectedness. It fits as easily in this 1959 paper.

Lindblom's take would be that there's much merit in experiential knowledge and large-scale experiments in the form of jurisdiction-wide policy changes. Skip the theories, frameworks, and mutually-agreed upon goals. If you think big enough, everything can be an experiment (e.g., the 10-year tax breaks in the US).

Oversight and Results

However, I think that Lindblom's solution is insufficient. One, governments have a certain responsibility towards fairness, even at the cost of efficiency. The human impacts of experiments cannot be ignored. For instance, in both the UK and Greece, social scientists have linked austerity policies with increased suicide rates (see: this post on the importance of good public policy). And in the age of transparency, governments cannot just make backroom trades of fairness for effectiveness (see: The Social Contract). 

Two, Lindblom suggests oversight in the form of multiple actors with competing interests: watchdog groups, lobbyists, and other responsibility centres within government. However, as Yves Morieux has pointed out, when someone has multiple people lobbying them, the marginal cost of ignoring any particular one of them is pretty low. Worse, none of those lobbying are paid to lobby for good systems overall; they're paid to adamantly recommend the solution that maximizes the variable they represent.

So where does this leave us? If I'm to be believed, both rational planning and experimentation and oversight are flawed approaches to public policy, which is not particularly inspiring. But I'll  let it hang for today and pick it up again shortly.