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Climate adaptation planning: How to be approximately right rather than exactly wrong


“It is better to be approximately right than exactly wrong.”, claims 19th-century author Carveth Read in his book on logic and reasoning.


It is a dated quote, but it still holds sound wisdom for taking any policy action.

Although more than ever science is providing us with abundant and detailed descriptions of reality, when we’re asking the wrong questions or collecting irrelevant data, exactness or the detail just doesn’t matter.

Overwhelmed with the information, we tend to focus on irrelevant details and invest energy in the solutions that fail after circumstances change.


Most times, we can recognize the details and information that matter only after we have created our end goals.

As sketching a scene before painting helps the artist to distill the scene to its essence and figure out how to communicate that essence, understanding the big picture helps to be “roughly right” in policymaking.

Seeing our cities as complex adaptive systems (CAS) can guide us to ask better questions and pick relevant information for bringing out good climate adaptation policies.

The rest becomes uncomplicated as we only need to fill in the gaps required to follow the big aim guided by the right questions.

And it is only at this time that we need to pay attention to the details.

The “essence” in climate adaptation planning: Complex adaptive systems thinking

The year 2020 taught us well that the world in which we live is inherently uncertain.

Any attempt to control it as a system with distinct and stable causalities is worthless because of almost unlimited complexity and the interrelatedness of its parts.


Treating our cities and regions as complex systems means taking unpredictability and uncertainty as a crucial part of city planning, so our policies create conditions to adapt and even thrive in any new development.

Exploring those conditions and how to enable them is a key application of complex adaptive systems (CAS) thinking.


Complexity scientists suggest that adaptive systems migrate to a state of dynamic stability, where the components of a system never quite lock into rigid order, and yet never quite dissolve into chaos.

They are free enough to change after disturbance but stable enough to stay recognizable.

These systems are made up of many individual actors. Like in a swarm of bees, a flock of birds, or a trade market, individual actors make choices about how to act based on information in their local environment.

These agents don’t act randomly. They share common purposes, are subject to common laws like gravity, thermodynamics, or local policies, and gather information from a common environment by using common perception systems, like vision, scents, scientific method, or stories.

The agents have the freedom to create patterns of connectedness. Such systems can exchange information very fast, experiment with numerous possible responses if they encounter a roadblock, and rapidly exploit solutions when one is found.


The capacity of such a system to learn, self organize, and invent novel new patterns when presented with a disequilibrium-inducing obstacle, is much higher than of the ones that are locked in rigid order, like a machine-robot, totalitarian regime, or a top-down control bureaucracy.


So how do we create the “complex adaptive” cities and regions?


“Being approximately right” in climate adaptation policies


Instead of fixating on returning or jumping forward to what we understand as stability, we can create a city’s infrastructures, governance, housing, economy, or environment systems, that self-organize and thrive during heatwaves, floods, droughts, migrations, and other climate impacts.


Asking the following questions and collecting corresponding data based on CAS thinking, raises the odds to be “approximately right” in our climate adaptation endeavors:


Right question 1: How to build a system’s adaptive capacities?

There is either no necessity or ability for any city system to result in fixed “optimal” structures.

That means moving away from strategies to keep permanent conditions, towards those who allow systems to fluctuate between specified thresholds, and build capacities for adapting to new circumstances shaped by climate change.

When facing disturbances, gathering information that helps plan for optimal diversity and redundancy of actors or solutions, efficient connectivity between the resources, continuous monitoring and learning about changes in the system, and managing feedbacks, and enabling effective participation is “no regret” way to start any adaptation strategy.


Right question 2: What are the site-specific vulnerabilities?


Focusing on data about place-based vulnerabilities to climate change impacts, like geographical or infrastructure exposure to harm, the degree to which system can respond to change, and concerning affected groups, will allow to bring out “short-path” policies, relevant to local conditions, which will give more rapid results.

Shedding light on vulnerabilities also safeguards from policies that can’t give tangible results. Aiming for smaller measurable outcomes directly answering local vulnerabilities is a better bet than going for larger, but uncertain benefits.


This is how the essence, that is, CAS thinking, can help us to seek the right answers and collect meaningful information for creating good climate adaptation policies. The ones that will not let new developments easily come in the way of our end goals.



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