Becoming Resilient: Scanning the Environment

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This blog originally posted on the Community & Regional Resilience Institute 

One of my favorites among the myriad definitions of resilience is a generalization of ones developed by Brown, and by Adger:

Resilience is positive adaptation to perceived change.

I like this definition because its few words hint at so much more – just as a shell held to our ear echoes the vast ocean from which it came. This simple definition implies change – or what else is adaptation? While the definition indicates that the adaptation is an improvement, it does not state an endpoint; thus, resilience can be seen in coming back from disaster whether or not the Old Normal is reached. The definition is ecumenical – the perceived change may be a threat or an opportunity. In either case, the definition requires that the change and its potential consequences are recognized, and that that recognition leads to action.

When we’re talking about adaptation we often come across the phrase a learning organization. It refers to this process of recognizing change in the organization’s environment and acting to either take advantage of that change or to mitigate its impacts. In this first of a series of posts on becoming more resilient, I want to concentrate on that process. I’ll couch this in terms of threats, because a threatening change generally requires a faster response; however, the process is exactly the same for seizing an opportunity. I will take a community perspective, but experience indicates that the process is generally the same for an individual, a business, an institution of higher education or a country – only the threats and the responses are different.

The transition from perception to action can be thought of as a three-step process:

  • Threat (or opportunity) detection – scanning the community’s environment and alerting the community’s leadership to a possible or actual change.
  • Transforming data to information – interpreting the data to understand what it means.
  • Transforming information to action – winnowing the various options to find those possible actions most likely to be effective.

In this post, I’ll concentrate on the first step: threat detection. It is a truism of modern life that we are all enveloped in a fog of data; we are constantly assaulted from all directions with demands for our attention. We don’t know where the “data” is coming from, we don’t know whether we can trust it, and we certainly don’t know whether we can act on it. It is all too easy for a community to get lost in this fog, and to be unable to pick out those data that are truly meaningful and can point the direction the community should follow.

More resilient communities employ “sensors” to help them get through the fog to what is meaningful. In technical terms, a sensor is something that detects changes in its environment and sends an output signal. Sensors have several attributes that make them particularly relevant to communities scanning their environments.

  • Selectivity. We want sensors that are able to detect the kinds of changes we are interested in, and – ideally – only those types of changes.
  • Sensitivity. We want sensors that can detect changes of a magnitude that matter to us.  However, we don’t want our sensors to be so sensitive that they respond even to trivial changes.
  • Output. We want our sensors to provide an output we can read.
  • Accuracy. We want our sensors to provide an accurate picture of our environment – neither signaling change when none occurs; nor failing to inform us of change.
  • Cost. We don’t want a Cadillac if a Chevy will do.

I’ve shown this first step of the process from perception to action in the following diagram.

Data-Collection

Resilient communities select sensors on the basis of what might significantly impact the community.  In general, communities are interested in three types of changes that may signal a potential threat.

  1. Financial / economic. A smaller community might want to detect changes in the financial health of its largest employer. A regional community might want to know about an economic slowdown in a particular economic sector that could impact many companies in the region. Any community would want to know about the potential that a large business might want to move into its area. I’m sure Long Beach, MS, would have liked to know before it was announced that Oreck was going to bail on them after Katrina so that they could have had a head start on finding a revenue stream to replace the $300,000 in taxes Oreck paid every year. Sensors for this domain usually are trusted sources of financial information or of impending changes in the business community.
  2. Physical. Beyond the obvious desire to know about impending natural disasters, a community might want to know about the condition of its infrastructure  How many lives might have been saved if Minneapolis had place sensors on the I-35 bridge before it collapsed? Sensors for this domain range from physical sensors indicating the condition of infrastructure (e.g., strain gauges on a bridge) to severe weather warnings issued by the National Weather Service.
  3. Human / social. As I write this, the Holmes trial is beginning in Colorado. There were data out there indicating his ultimate explosion, but there was no sensor warning Aurora officials that this might happen. Many college campuses have established “warning systems” to detect anti-social behavior; these trigger engagements by behavior intervention teams to evaluate the situation and to take action. It is unfortunate that UMass-Dartmouth did not have something similar in place when Dzokar Tsarnaev was a student there. Sensors for this domain range from other humans observing behavior to sophisticated communication scanning systems (a la the NSA) looking for patterns of terrorist activity.

A resilient community will have at least some idea about the threats it faces. Communities will almost always recognize the climatic threats they face, e.g., severe storms or flooding. Too often they will have little grasp of financial or economic threats, and even less – except in a generic sense – about human and social threats (To its credit, Memphis looked at what happened to New Orleans during and after Katrina and recognized the disquieting physical and social similarities between the two cities.  To Memphis’ discredit, it has done little to address them.). Thus, self-knowledge is the beginning of resilience – it is what allows the community to give meaning to its sensors’ signals.

A resilient community will use its self-knowledge to choose the sensors it needs – sensors for each type of threat if faces.  And because communities face diverse threats, a resilient community will deploy a diverse group of sensors. For more likely threats, it may deploy more – or more expensive – sensors. A resilient community will also ensure that its sensors are connected – a sensor whose signal doesn’t reach a receiver is useless. The arrows in the figure indicate that need for connection to the community’s leadership – those who will instigate and initiate action.

In my next post, I’ll look at the second part of this data to action process. In particular, I’ll look more closely at the second step in the process – transforming data to information. Thus, I’ll discuss the necessary connections to the sensors and the barriers that need to be crossed to give meaning to their signals.

 

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