Tag: resilience

Poised on the edge of the AI Knife

Successful people recognize crisis as a time for change. ~ Edwin Louis Cole

…And so do successful communities.

Many of our communities are already in crisis. In our larger cities, too many men have given up, spending their days in drugs and isolation. There are too few opportunities for them – even for college graduates – to begin or to restart their careers. Many cities are being hollowed out by companies and residents fleeing grime, crime and poor governance. Falling birth rates (“Peak population”) and corporate flight have left behind empty homes, schools and commercial buildings.

For some cities, this flight has become a vicious cycle. People leave because of a declining quality of life – increased crime, deteriorating schools, limited opportunities. The local government’s tax base shrinks; business revenue becomes spotty and some businesses leave or go under. This causes more people to leave; and the cycle continues until there’s no one left who can leave.

In my last post, I looked at the potential impacts of AI on individuals. In this post, I look at the potential threats to communities inherent in AI. I’ll also suggest steps a community can take to prepare for AI’s impacts.

Impacts

Communities with large job losses due to AI will see losses in all of their community capital portfolios. These will be similar to those already being experienced in our larger cities. Local government will see immediate loss of income and sales tax revenue. Since many of the displaced workers are high earners, the tax revenue lost will be greater than the fraction of jobs lost. Businesses – especially small businesses like restaurants and shops – will struggle. Eventually property taxes will also fall, leading to less funding for schools and ultimately a poorer quality education.

The diminished need for commercial real estate may lead to a deteriorating downtown. Most seriously, the community’s social fabric will begin to unravel. More anti-social behavior and crime; more drugs; fewer volunteers for those things that enrich and enhance a community’s character. And on top of it all, an increased demand for those services that make up a community’s safety net, especially mental health services.

One other somewhat more speculative impact: AI may accelerate the drop in birth rate. It appears likely that more men will lose their jobs than women. Women – especially college graduates – are already having problems finding suitable partners; it is a recognized cause of the falling birth rate. This potentially could be the most severe impact of the AI revolution on our communities and society.

But let me quickly add that some communities will be relatively unaffected, or at least will not feel much pain for years, if at all. Communities whose economy is concentrated on “high-touch” jobs are unlikely to see much impact. Communities whose economy is reasonably diversified may be “safer” as well – there will likely be some jobs available in non-AI impacted industries to buffer some of the AI job displacement.

Communities whose businesses tend NOT to be early adopters of new technologies may also fare better. As with any new technology, AI’s first impact is to destroy old jobs. However, history suggests that eventually new jobs are created. Organizations which wait to adopt new technologies can then move their most valuable assets – their employees – from the old no-longer-needed jobs to the new ones. Those communities that will fare best are those that prepare. If well-prepared, they may even prosper.

Diffusion of AI

The job losses at software firms and others immersed in the “digital economy” have been well-publicized. Communities with clusters of these firms are already feeling AI’s impacts. However, according to the National Bureau of Economic Research, overall AI is primarily being used as a tool to support normal business processes, i.e., a productivity enhancer.

Despite strong substitution at the task level, overall employment effects are modest, as reduced demand in exposed occupations is offset by productivity-driven increases in labor demand at AI-adopting firms.

Thus, most communities have not yet had to face AI’s impacts, but just how much time do they have to prepare? Answering that is a little complicated – there are a few factors to consider.

  • Knowledge. As I noted in my last post, massive amounts of data are often needed for to develop an AI “bot” to replace a human. As I know from my own experience, the necessary data are often stored in various forms and various places, not least of which is in workers’ minds. And sometimes the data is contradictory. Extracting, rationalizing and codifying all of this can be a daunting, time-consuming – and expensive! – task.

  • Implementation and other costs. Often lost in the hype, there are real costs associated with using AI to replace humans. The most obvious is the cost of developing the AI “bot.” It has been relatively easy to develop AI to replace programmers and some others in the digital world: the AI developers have had access to virtually all of the data they needed and know the business. Development of algorithms is simple when you’re expert in the domain.

    But when you don’t know the business, it becomes much more costly and difficult to implement an AI solution. There is a translation step, converting domain-specific knowledge and jargon to something the AI developer can use. And the AI bot will also have to be able to translate its knowledge back into domain-specific jargon to be useful to humans. This requires at least some human involvement, and specialized expertise probably not available in most organizations.

    There must also be extensive testing to ensure that AI is not “faking it to make it.” For example, a recent news report spotlighted a legal case in which a lawyer had submitted an AI-prepared legal brief which had references to non-existent precedents. Anthropic is being honest when it says “Claude is AI and can make mistakes. Please double-check responses.”

    Another important cost is the loss of capital when turning people out the door. Last year, American companies spent over $100 billion on new employee training, about $1000 per new employee. We are seeing AI reduce hiring of new employees, but more experienced employees are being retained. These people have not only been trained to do a job, but also to do it in a way that “fits” the rest of the company. Procedural knowledge can be gained to develop a bot to do a job (at a cost!) but cultural “knowledge” is much more difficult to capture.
  • Return on investment. In the short term, the benefit of AI is a reduction in cost. Salaries and benefits are always an organization’s major cost; reducing them helps an employer’s bottom line. But that return may be fools’ gold if AI cannot do everything the human could.

    In the longer term, AI may begin to pay larger dividends in terms of developing more effective corporate strategies. However, this requires that an organization’s AI becomes more attuned to its corporate structure and culture. We still don’t know how rapidly this can happen.

Suggestions for communities

Vulnerability. The most important first step for a community is to develop an understanding of possible impacts. Tufts University working with Digital Planet have developed a useful resource for this (https://digitalplanet.tufts.edu/ai-and-the-emerging-geography-of-american-job-risk-page/). They have determined vulnerability to AI by job, by industry and geographic region. The report itself has several interesting graphs and tables, and links to an interactive map. However, communities should concentrate on the Excel data sheet (e.g., there are some transition errors between the spreadsheet and the map).

But don’t use the report blindly. The spreadsheet has three different projections based on how rapidly AI is penetrating a community’s economy. If the community is already feeling the “AI Monster’s” bite, it would be wise to use the Median or Fastest Time data. Communities should also temper its projections based on local knowledge. For example, my community is becoming a center for cyber security study and practice. The Excel sheet predicts a 5% loss of jobs due to AI (Median time progression) but doesn’t consider the jobs that are coming in. Thus, my best guess is that AI will have relatively little negative impact on my community’s workforce in the next five years and may boost productivity.

Response and recovery planning. If the community believes it’s highly vulnerable, it should treat this as a Wild Thing – an extreme event similar to a natural disaster, or a pandemic, or a Recession. That should prompt emergency planning for response and recovery.

For extremely vulnerable communities AI’s bite can become an existential problem. A Whole-of-Community problem requires the entire community to solve it. The first step in planning should be to get all of the stakeholders working together. These may include:

  • The business community. Some businesses need to be involved because they are laying off people; some because layoffs will impact their business; and some because they may be able to hire those laid off. The latter are especially important: the specific qualifications they’re looking for should be articulated.

  • Educators. One of the most important unknowns right now is how to retrain and re-employ those laid off. This is a very different mission from traditional education of the masses (This is summarized nicely in https://dcjournal.com/to-prepare-for-a-future-with-ai-we-must-educate-differently/). Institutions of higher education that are in close contact with local industry are probably best equipped to build bridges between workforce reskilling and the needs of local businesses who may be able to hire those displaced by AI.

    Educators can also act as a source of information about best practices. Many of them are tuned into professional networks and have personal contacts spread across the country. They can provide innovative ideas that the community can include in plans.

  • Local government. As noted above, local government has a great deal to lose to the AI Monster’s bite. It should act as a convenor of meetings and facilitator of plan development.
  • Workforce representatives.
  • NGOs. Those laid off will eventually have to rely on the community’s safety net. NGOs are a large part of that in every community. However, many communities have NGOs that can play a part in reskilling or that can help the displaced to find new jobs.

Strengthen the safety net. Early on, vulnerable communities must determine what “safety net” services are going to going to see increased demand from displaced workers. Once known these communities need to determine what they can provide. And if they find insufficient capacity to meet demand, they should prepare for the consequences.

Education becomes an important of this safety net. This requires rethinking our approach to education; moving from a “once-and-done” model to life-long learning. I’ve written a lot about education over the years: it is essential that we prepare our kids to be able to learn and to want to learn even after they finish their formal schooling. Even before the advent of AI we were told that new workers were going to switch jobs several times over their working life, putting a premium on old dogs’ ability to learn new tricks. AI’s coming provides a crisis that spotlights the need for this change to happen.

I don’t know enough to go beyond this … and I’m afraid no one else does either. But what I believe is that every community can and must determine its own vulnerability and then develop its own plan. What we’ve seen from the federal government and some state governments is erection of barriers – a lot like old King Canute ordering the tide not to come in. And about as successful. But I also believe that a community that works together can weather the storm of layoffs and eventually reap the benefits of the new jobs AI will create. Successful communities will recognize the need for change and adapt to what’s coming. Some others will muddle through. And – I hope only a few – some communities will not recognize the approaching monster until they are devoured.

The AI monster will get you if you don’t watch out

All we need to do is make sure that we’re in a position to benefit from uncertainty and volatility instead of being harmed by it. ~ Nassim Taleb

If you’re confused about the impacts of AI, you’re not the only one. Some predict that AI will destroy civilization as we know it; some predict that AI will revolutionize our lives. Some say that our jobs will be gone; others that we’ll be able to do those jobs better, and there will be lots of new jobs we haven’t even conceived of yet. The one thing each of these seems to have in common is the impact that AI is currently having on the speaker, i.e., the impacts are seen only through the eyes of the beholder. Certainly the impacts of AI are both highly uncertain and highly personal.

As both Niels Bohr and Yogi Berra have said (in almost identical words!), prediction is difficult, especially about the future. So I won’t try to predict AI’s ultimate impacts. Undoubtedly they will be profound, and compounded of good and bad. But I will provide some facts to help think about AI’s impacts, and how to adapt to them. For one thing is certain: the more adaptable we are, the better our chances of avoiding being devoured by the AI Monster.

Impacts on individuals

As a few of you know, I was involved in some of the early attempts to deploy AI in the real world, in the 1980s. At that time, the practical emphasis was on development of “expert systems.” These required gathering the knowledge and experience of experts in a given field, and converting it to a set of rules along the lines of “If some condition, then do this.” And the results were sometimes impressive. For example, a psychological diagnostic system was developed that was as good at dealing with psychological trauma as human therapists.

A lot of the current effort in AI is still this: replacing the “man-in-the-loop” for foreseen situations with a set of rules. The major difference is that our increased computing power and the advent of Big Data are allowing developers to vastly increase the scope of what is “foreseen” and our ability to act swiftly. As a result, we have self-driving cars with accident rates below those of human drivers. We have “apps” that write computer code more rapidly than human programmers, and (if not now, eventually) with fewer errors. AI has growing capability to diagnose diseases, to dig through case law to find relevant precedents, even to point to promising areas for research.

This is due to the evolution of AI from task-based – if-then rules based on a defined body of knowledge trained to act; to generative – systems able to change themselves in response to new knowledge. This is often called “machine learning.” App-adaptation might be a more apt term.

This makes it seem like the doom-and-gloomers are right. The AI Monster will devour us all. Well, not quite; it’s complicated. And if you’re going to escape from the AI Monster [maybe] lurking in your closet, you need to look at your situation through several different lenses.

• Current employment. The most quoted unemployment rate in the US is currently at 4.3%. Personally, I find labor force participation more meaningful; for 25-54 year-olds it’s about 84% (pretty high). Conversely, it is in the 60% range for those 16-24. This implies that level of experience is important. If young people aren’t being hired to do a job, or, worse, those with experience are being laid off, that’s a sign that the job is in jeopardy.

• The job itself. It’s almost become a truism that AI won’t displace those in the “high touch” professions (e.g., family services, sales) for a very long time. But even there, many of the more routine tasks in health care (e.g., reading x-rays, billing) are being taken over by AI. On the other hand, many knowledge workers are at extreme risk. It seems like every week thousands are being laid off by one of the Big Tech companies. Whatever the field, jobs requiring flexibility, especially those developing policy or strategy, are likely safe for a very long-time.

• Cost-benefit. Generative AI systems are expensive to develop. They require massive amounts of domain-specific data, tremendous computing power, and extensive testing. And lots of electricity and air conditioning as well! The greater the complexity of a given job, the less attractive it becomes for AI – the costs may outweigh the value of displacement.

I asked both Claude and ChatGPT (two of the leading AI apps) “What will be the impact of generative AI on future jobs in the US?” Both gave lengthy responses. Claude’s was better written, and I found more generally useful:

The pattern with previous automation was that it tended to displace routine physical work — assembly lines, data entry, basic bookkeeping. Generative AI breaks from that pattern in an important way: it’s targeting cognitive and creative work first. Paralegals, junior analysts, radiologists reading scans, software developers doing boilerplate code, copywriters, customer service agents, financial advisors doing routine portfolio management — these are all in the crosshairs in the near term.

Jobs with the highest short-term resilience tend to share one or more of these traits: they require physical presence and dexterity in unpredictable environments (plumbers, electricians, nurses doing hands-on care), they depend on genuine human relationship and trust (therapists, certain kinds of sales, clergy), or they require highly contextual judgment in novel situations (senior executives, certain researchers, emergency responders). …

The most likely scenario is not a clean “AI takes jobs, new jobs appear, equilibrium restores” story. What’s more probable is a bifurcation: a class of highly-educated, adaptable workers who use AI to become extraordinarily productive and well-compensated, and a much larger group of workers displaced faster than they can retrain, in a demographic environment that offers fewer natural correctives than past transitions. The demographic plateau softens the raw unemployment numbers, but doesn’t address the human cost to specific workers and communities.

ChatGPT, on the other hand, gave a similar answer with two interesting twists. First it provided more detailed impacts for important sectors (e.g., tech and IT, government, education, manufacturing). ChatGPT also looked at the impacts of AI over time. In the short-term, it predicted job losses, some increases in joblessness and reduced hiring of new college graduates. In the medium-term (3-10 years), ChatGPT saw restructuring of existing jobs (skill in using AI becoming embedded in many jobs) and the rise of new categories of jobs (e.g., “prompt engineering,” data governance, AI trainers and testers). In the long-term (10+ years) ChatGPT weaseled out of prediction and instead fuzzily pointed toward a bifurcated future.

From all of this, let me offer the following guidance to all of you worried about becoming a victim of the AI Monster.

  1. No matter your profession or vulnerability, become familiar with AI tools. AI is here to stay. And it’s getting better at doing routine and non-routine tasks. If you can use it, it will increase your value to your employer/customers/clients. More importantly, using AI tools can actually increase your job satisfaction.
  2. Work to redefine your current job to require more non-routine problem-solving. This will help you to further develop AI-complementary skills: learning, planning, creativity and decision-making.
  3. Continually increase your knowledge about how your job/profession plugs into everything around you. Most importantly, focus on your own personal value proposition. But be brutally honest with yourself: if you’re mired in the routine, your value-added may not be high, i.e., you may be vulnerable.
  4. Increase your value. Certainly, taking on more responsibility is one obvious way. Perhaps more importantly, increase the complexity of what you do, especially if your work is largely repetitive or routine. If you can’t, best brush up your resume, and your skill set (see #1). If you can, find a way to apply your skills in a “high touch” environment.
  5. An important way to do both #2 and #3 is to expand your own personal network. Try to determine how people with your skills are used elsewhere. Perhaps more importantly, become a linchpin: a conduit for useful information. Involvement in professional societies can help you do this.
  6. Develop a “Plan B” for yourself. Identify where your current skill set might be of use. Continue to expand your skill set (again, see #1).

Impacts on communities

Claude’s response quoted above points out that AI will impact both individuals and communities. In my next post, I’ll explore potential impacts, and suggest things communities can do to better buffer themselves against potential harm.

Global Hints about Community Resilience

Long is the road to learning by precepts, but short and successful by examples. ~ Seneca the Younger.

I am an unrepentant data geek. One facet of my geek-ness is that I am autodidactic (I actually had a Professor call me that – I had to look it up) – I seldom accept others’ conclusions; I have to see for myself (actually, the Professor said I had to learn from my own mistakes, which I sometimes do). About 10 years ago, I first stumbled over FM’s Resilience Index. Now in its 12th year, it is a composite of 18 different indicators.

I posted about it at the time; lots of graphs, but I didn’t really put them into a useful context. In this post, I want to take a look at hints that they may have for those of us trying to understand a community’s resilience, in particular factors that we should consider in the resilience indices so prevalent in the literature and in use in the US.

The variables. FM is an insurance company. So “resilience” has to do with physical phenomena – natural hazards and climate change, as examples. It bins the 18 variables included in the Index into two categories: Physical factors and Macro factors. The Physical factors, in effect risk factors, rely on FM’s experience in each country, except for the cybersecurity data. The Macro factors might be considered as those attributes related to recovery from a natural disaster, i.e., resilience factors. If you’re interested in the data sources and methodology, follow this link.

Whenever possible, the data are averaged over a five-year period. This is something that is generally not done for most (any?) of the US resilience indices. The advantage of this is that it smooths out some of the inevitable noise in the data while maintaining evidence of a significant trend.

All of the Macro factors that involve money are adjusted for Purchasing Power Parity (PPP). The intent is to remove cost-of-living differences from comparisons. For the most common resilience indicator systems in the US, this has not been done. Thus, California counties (or other units) are indicated as more resilient than they really are because important data such as median household incomes are not adjusted for the very high cost of living (CoL) in CA. Using poverty values not adjusted for CoL, the number of people living below the poverty line in CA is less than the US average. However, once the value is adjusted, California has the highest fraction of its population living in poverty of all the states. In this context, it’s not surprising that it’s taking so long to rebuild Pacific Palisades!

Physical factors

Climate risk exposure – the portion of the country’s economically productive area exposed to climatic risks today.

Climate change exposure – the portion of the country’s economically productive area exposed to climatic risks in 2050.

Climate risk quality – enforcement of building codes for wind (90% of the indicator), and mitigation of flood and wind impacts.

Seismic risk exposure – the portion of the country’s economically productive area exposed to seismic risks.

Cybersecurity – commitment as shown in action (80%), and risk reduction relative to risk.

Fire risk quality – enforcement of fire codes (80%), and risk reduction relative to risk.

I haven’t seen the proportion of economically productive area to determine exposure to hazards used before. In the US, we either don’t include exposure data in our resilience indices, or else use something like the HAZUS code to calculate hazard losses (as is done for FEMA’s Community Resilience / National Risk Index). We certainly don’t include projections of risks in the year 2050. We also don’t include fire risks to the built environment as is done here, nor effectively give credit for mitigating actions.

Macro factors:

Control of corruption – perceived amount of corruption (public resources used for private gain) as well as “capture of state by elites and private interests.”

Education – average of expected years of schooling and the mean of actual schooling.

Energy intensity – energy consumption divided by the adjusted gross domestic product.

Greenhouse gas emissions – emissions divided by the adjusted gross domestic product.

Health expenditure – mean expenditure on health per person, both public and private, adjusted for PPP.

Inflation – annual rate of inflation.

Internet usage – fraction of the population using the internet.

Logistics – how easy it is to export to a target country in terms of the quality of infrastructure, the quality and availability of logistics activities, and public sector bottlenecks; based on survey data.

Political risk – perceived likelihood that the national government will be either destabilized or overthrown, either unlawfully or by violence.

Productivity – GDP (adjusted for PPP) per capita.

Urbanization rate – on an annual basis.

Water stress – freshwater withdrawn as a fraction of available resources.

Each factor was statistically massaged so that they were on a common scale (0-100). The resilience index for each country is then the mean of the 18 values. In contrast, in FEMA ‘s resilience index, the exposure (calculated via HAZUS) is divided into the Macro factors.

I took this data and mapped each factor against the resilience index and against each other. I won’t clutter this too-long post up any further with a bunch of graphs. The results are summarized in the following table where I’ve looked at correlations among the variables. R2 is a measure of how well two variables are linearly correlated. I’ve arbitrarily chosen an R2 value of 0.5 as the threshold indicating a strong relationship. All of the strong relationships are listed in the table below. If anyone wants the complete set of correlation just let me know.

Strong relationships R2 ≥ 0.5
Resilience indexControl of corruption0.76
 Climate mitigation0.74
 Productivity0.70
 Education0.70
 Logistics0.66
 Fire mitigation0.65
 Health expenditure0.57
 Internet usage0.57
Productivity (GDP per capita)Control of corruption0.65
 Logistics0.60
 Climate risk mitigation0.57
 Health expenditure0.53
 Education0.52
 Fire risk mitigation0.51
 Internet usage0.50
Health expendituresClimate mitigation0.56
EducationInternet usage0.68
 Climate mitigation0.57
 Fire mitigation0.52
 Urbanization rate0.51
 Control of corruption0.51
Political riskControl of corruption0.55
Control of corruptionLogistics0.66
 Climate mitigation0.54
Urbanization rateInternet usage0.52
LogisticsFire mitigation0.63
 Climate mitigation0.57
Climate mitigationFire mitigation0.73
Climate risk exposureClimate change exposure0.50

The strongest correlation was between the resilience index and control of corruption. This factor is not considered in any of the commonly used resilience indices. In effect, we are ignoring the community’s governance/institutional capital as a factor in its resilience. The impact of official corruption on recovery from disaster is obvious. The news from Gaza bombards us daily with a reminder of how much corruption hinders recovery. And apparently misuse of $100 M in recovery funding is another factor hampering the Pacific Palisades recovery. The only index that considers this factor is Arup’s resilience index for the 100 Cities initiative. Based on its strong relationship to a country’s resilience, this factor deserves more attention. (As an aside, I compared FM’s “Control of corruption” data with the Corruption Perceptions Index from Transparency International. The two are determined rather differently; however, they are highly correlated R2 = 0.96, i.e., they apparently are reflecting the same thing!).

Logistics, internet usage and fire risk mitigation are all important factors strongly related to both resilience and productivity. None of them are currently included in common resilience indices. I have often said that resilience is a manifestation of a community’s strengths, not its vulnerabilities. Intuitively, the ability to move physical assets where they are needed is an important strength related to recovery. In a similar sense, internet usage facilitates movement of information across the community. More generally, this emphasizes the importance of dispatchable capital.

One surprise: exposure factors weren’t correlated with the corresponding “quality” factors, i.e., mitigation wasn’t related to exposure. While the two climate exposure factors were correlated, none of the exposure factors were correlated with any of the resilience factors. Similarly, greenhouse gas emissions were not correlated with any of the other variables.

This is the first time that FM has included cybersecurity. It doesn’t make any difference to the resilience index, and is not correlated with any of the other factors. It seems to be irrelevant to both resilience and natural hazards and fires.

There is a lot more that can be extracted from this data, but this post is long enough already. FM has provided a rather different window on resilience, pointing out the importance of variables not often considered when we look at our communities. I hope that those working to make their communities more resilient will include all of the community’s capital portfolio in their efforts – its logistics systems (physical capital), its information systems (social capital), and above all, how the community makes and implements decisions (governance/institutional capital).

Winning Tomorrow

Yesterday is not ours to recover, but Tomorrow is ours to win. — Lyndon Baines Johnson

As many of you know, I have spent much of my later-career years focused on communities, especially community resilience. The “resilience” that I and my colleagues have talked about goes beyond the conventional “bouncing back” from adversity – survival – to include seizing the opportunities inherent in any “change” whether adverse or not, i.e., thriving.

But this put us in a somewhat awkward position: virtually all of our funding was coming from sources most interested in “surviving” crises. If you think of the Chinese ideogram for crisis, it is made up of the symbols for “danger” and “opportunity.” Our funding tended to focus us on mitigating “dangers” and on recovery when they overwhelmed communities, with much less attention to seizing opportunities.

Now that I’m self-funded (ahem), I’m trying to bring balance back into my own thinking and writing. As a part of this, I’ve struggled to find something that better captures our conception of “resilience.” If the purpose of a community is to provide the quality of life that its members want, then a community should be continually striving to meet or exceed that goal. I was searching for a way to express that idea when I stumbled across the quote for President Johnson. 

Winning Tomorrow – the American Dream for communities! The American Dream is the essence of what makes America exceptional. It is inherently aspirational. It is built on a belief that anyone – even the poorest among us – can rise above even the humblest of beginnings to achieve a better life with hard work and persistence. Just as we as individuals work to make the American Dream a reality for ourselves and our families, our communities should work to make themselves better, more livable, places to work, play, and raise a family – they should aim to Win Tomorrow.

Certainly, achieving that purpose is complicated by the sea of changes in which our communities are immersed. Winning Tomorrow means that the community will continue to move forward no matter what challenges they face in the future. Communities are open systems. People are moving in and out of them continually. Today’s acceptable quality of life may not satisfy the community’s residents 10 years from now. Neighboring communities will also change. The community may be struck by a Wild Thing, resulting in loss of life, in damage to infrastructure, or to businesses closing. The state or federal government may enact new regulations altering community processes. And, of course, the community’s infrastructures and dispatchable capital will degrade over time if not maintained.

A community Wins Tomorrow if the community’s quality of life steadily improves over the long term. The community successfully adapts to its stressors before failure occurs. If the community fails (e.g., if it is devastated by a Wild Thing), it rapidly recovers, and regains its upward momentum.

It takes self-investment to Win Tomorrow, but that doesn’t mean mountains of money. It does mean institutional capital to make decisions and to implement them; human capital to take action; and social and cultural capital to sustain the effort.

Some of you cynics may scoff at this: “Mine is a poor community with few resources.” The American Dream doesn’t care where you start, or how poor you are. If you work hard and smart over the long haul, you can create a better life. In the same way, even the poorest of communities can Win Tomorrow, using what they have to take small steps that become bigger steps that ultimately become transformative.

In that sense, Winning Tomorrow is a journey, not a destination. It is not a one-time exercise but rather a continuing effort to make Tomorrow better than Today for the entire community. Efforts to Win Tomorrow should last for decades – ideally never ending. Winning Tomorrow mostly consists of incremental changes to individual community systems.

I know this may seem like what my good friend Warren Edwards calls the “Square Root of Ether” – an intellectual exercise with little practical merit. In my next post – It Began with a Bull – I’ll tell the story of a dirt-poor community who started its journey with nothing but a leader who cared about his community and the community is still working to Win Tomorrow.

Competence and character

Trust is a function of two things: character and competence. Character includes your integrity, your motive and your intent with people. Competence includes your capabilities, your skills and your track record. Both are vital. – Stephen Covey

In response to my last post, one of you asked a really good question – “How do we get competence AND character in our elected officials?”

I [tried to] provide a brief answer in the last post:

If what you see doesn’t match what you’re being told – by either the politicians or the media – then suspect you’re being lied to. Dig at it until you get at the truth – and then act on it. Most importantly, don’t vote based on loyalty, or to just go along – vote for who is going to do the best job. If they don’t live up to your expectations, vote them out. And if none of that works, then vote with your feet – leave.

So let me personalize this. I vote for the candidate that I most trust to do the things that need to be done. Too often, our elections become a referendum about one candidate or the other (arguably, 2020’s Presidential election was a referendum on Trump). But elections actually are choices. A vote against a candidate is a vote for their opponent. If we vote for someone because we dislike/loath/hate their opponent we may well get what we deserve – an incompetent with little integrity.

That skeletal bone “the things that need to be done” demands a bit more meat. The first duty of community office is to maintain or improve the community’s quality of life. Ideally, that means finding people who will “do the right things right the first time.” Finding people who will not only work to solve Today’s problems but are also focused on Winning Tomorrow – a sustained effort to improve the community’s capacity and its quality of life (I’ll talk more about Winning Tomorrow in my next post). But we’re all flawed; there are too few of these paragons around.

Thus, one way to get competence and character in office is to urge those we believe approach this ideal to stand for election. I judge a candidate’s competence based on

  • The candidate’s past. Does the candidate meet the requirements for the position (e.g., age, experience, education)? If the elected position requires working with a bureaucracy, does the candidate have any relevant experience? Has the candidate handled difficult situations before in an acceptable manner?
  • The candidate’s positions. Is the candidate focused on solving what I believe to be the community’s problems? Which of these are the candidate’s highest priorities? Is the candidate offering likely solutions, that won’t have any obvious “unintended consequences?” Is the candidate driven by ideology or by observation of the community’s conditions?
  • Tenure. Has the candidate (or the candidate’s party) held the office for longer than a decade? If so, what does the candidate propose doing differently to solve the community’s problems?

I judge a candidate’s character based on

  • The candidate’s past. Any scandals, or anything unsavory? If it’s something said or written in the candidate’s youth, has the candidate learned and moved beyond the immature transgression? Conversely, are there laudable actions or statements (e.g., serving one’s country)?
  • Confidence. Is the candidate confident – neither cocky nor projecting mock humility? When confronted by those who disagree, does the candidate “keep their cool?”
  • Trust. Do I trust the candidate to do their best in the position? This is a personal thing: the person’s confidence plays a role, as does the candidate’s respect for those who disagree. Ultimately, I’m looking for that person who, if necessary, will “rise above principle to do the right thing.”

The crucial element is information. Correctly judging a candidate’s competence and character requires accurate – and often nuanced – information. As a result, I spend a great deal of time before an election seeking information about the candidates in the races I care about. I try to glean information from several sources to construct the best picture I can. As an aside, the consolidation of the media often makes that difficult. We often overlook that the news media are both reporters and curators. If they choose not to cover a story (e.g., Hunter Biden’s laptop; JFK’s infidelities) then we as citizens are denied the ability to factor it into our decisions. The rise of the “New Media” such as the Free Press (left of center) and the Daily Wire (right of center) is helping to restore balance at the national level. But while there may be multiple sources of information in some of our big cities, in many locations – especially smaller cities – there often is only one.

Once I’ve collected the information, it’s crunch time. Remember, elections aren’t referenda, they’re choices. And since we’re all flawed, it’s highly likely that each candidate has pluses and minuses. I look for the candidate best able to get the things done I believe desirable under the circumstances.

We can’t always have both competence and character. In one of the first elections in which I voted, I was faced with the choice between a competent (possible) crook and an (apparently) honest fool. I voted for the crook because he seemed best able to make and implement the hard decisions demanded by the times.

If we want both, we have to urge competent people of character to run for office. But – like us – our information will always be flawed. We will make mistakes. The easiest is to simply vote straight party tickets, as if one of the parties has a monopoly on mendacity and the other lives close to the saints. If we vote for the candidate and not the party; vote for what we believe is needed for our communities to survive and thrive; and dig for the information we need to do these – we likely won’t go too far wrong … at least not very often!

Ecological vs engineering resilience

The goal of resilience is to thrive. – Jamais Cascio

Both Claire Rubin and James Brooke were kind enough to forward to me a short piece from The Conversation, by Prof A A Batabyal of RIT (nice that someone is looking out for me!). Although the short essay started by looking at “sustainability,” it was really focused on “resilience.” In particular, Batabyal contrasts “ecological” resilience against “engineering” resilience.

He uses a lake and a bridge as exemplars: the former for ecological resilience (as defined by Hollings) and the latter for engineering resilience (as defined by Pimm). The bridge has only one stable state; the lake has more than one stable states. As the Prof points out, Hollings’ definition boils down to how much stress an ecosystem can withstand before it restructures. Pimms’ definition of resilience relates to how fast a system can return to equilibrium.

The Prof then points out that most socioeconomic systems – such as communities – “exist” in multiple states. Thus, Hollings’ definition should be favored. I disagree, for several reasons.

First and foremost, Hollings’ definition and the panarchic framework it leads to is not very useful for a community trying to become more resilient. The definition requires us to observe a system under stress and then watch it change. The amount of stress needed to force the system to change is its “resilience.” If I’m a community professional, in essence this implies I have to let the community fail before I can gauge its resilience! Of course this is nonsense – but it does point to the difficulty of predicting a community’s resilience using this approach.

One of the biggest stumbling blocks is knowing whether a community has restructured. Take New Orleans after Katrina as an example. There were several differences in the Before and After:

  • The city’s population dropped by a third.
  • Several new civic organizations were put in place.
  • There were measureable changes in the performance of important community systems (e.g., student performance improved).
  • Much of the sleaze in the French Quarter disappeared.

Did these indicate a change in structure?

Then there’s the “resilience-to” problem.In practical terms, we know that a community generally doesn’t have a single “resilience.” Rather a community’s resilience depends on

  • The stressor. A community may be able to deal with a great deal of economic stress, but fold like a house of cards in the face of a pandemic.
  • The speed of stress. A community may be able to adapt to a high level of stress spread over time but unable to tolerate the same stress experienced as a rapid shock.
  • The amount and type of damage, and the resources available for recovery.

Pimm’s concept of “engineering resilience” has the advantage of seeming more like what people think of as resilience. As the result of a Wild Thing – some sort of extreme event – a community loses capacity or functionality. Over time, the community recovers from the Wild Thing and regains its capacity. The time required to regain its functionality is the community’s resilience. Bruneau et al’s concept of resilience is very consistent with this idea.

From a community’s standpoint, community systems are either functional or failed – they either do or don’t meet the community’s demand for their function. After the damage wrought by a Wild Thing, the community at large doesn’t really care whether the health care system, or the system providing electricity are structured the same as before. They only care whether they can obtain the same (or better) health care as before the Wild Thing. They only care whether they can get light when they flip the switch, or air conditioning when it’s hot outside. Community professionals are most concerned with determining how soon after a Wild Thing the health care system is functional; how soon the lights can come back on after power is lost.

The stress testing approach* that Jennifer Adams and I have developed provides community professionals with a way to gauge this type of resilience. To summarize, community professionals postulate a particular Wild Thing – type, intensity, timing. This leads to a prediction of the damage the Wild Thing will cause. This in turn leads to a prediction of which community systems will fail. The resilience of each system is then determined by the use of dispatchable capital over time. The resilience of the community is inferred to be the resilience (time to recovery) of the last system to recover.

Community professionals and communities themselves want to know how resilient they will be to Wild Things before they occur. Simply put, Hollings’ approach to resilience may be useful in explaing what happened to a community as a result of a Wild Thing after the fact. It’s not very useful to community professionals trying to determine their community’s “recoverability” before a Wild Thing strikes. There is a certain inevitability to the “ecological” resilience approach when applied to communities. If sufficiently stressed, they will fail and restructure. When and how and to what is unanswered. Measuring the “engineering” resilience of communities using stress testing methodology gives community professionals answers they can work with, and is more intuitive. The approach can indicate paths to reduce damage and community system failures. It can also point to which additional resources could speed the community’s recovery from a Wild Thing. Ultimately, it can make recovery surer and more rapid – and communities more resilient.

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* M. J. Plodinec, “Stress Testing of Community Resilience to Extreme Events,” Journal of Homeland Security and Emergency Management, 18(2), 151-176 (2021).

Five easy pieces

“The community stagnates without the impulse of the individual. The impulse dies away without the sympathy of the community.” ~ William James

Purpose of a community

Quite simply, the purpose of a community is to provide its members with the quality of life they want. If what the community provides either drifts or jumps away from what they want, people leave. We’ve seen this response to “defunding the police” in too many of our great cities – LA, San Fran, Portland, New York…. This mismatch between what people want and what the community provides leads to hollowed out cities. Those who can (esp the middle class), leave. The community’s remnant is a powerful elite and a struggling underclass. Some expect unearned benefits. All are crushed by the taxes and fees and other dictates exacted and enacted by the elite.

Business of a community

Quite simply, the business of a community is to increase its “community capital” so that it can maintain, and perhaps improve, the quality of life it provides. When people leave, they take capital with them. If there is a net out-migration, the community has less capital to fulfill its purpose. As we’ve seen with the Rust Belt cities and others such as St. Louis, this becomes a vicious cycle: fewer resources leading to a poorer quality of life, incentivizing people to leave, reducing the resource base even further. Conversely, communities that are growing have more resources – and more discretionary resources – to fulfill their purpose. This can lead to a virtuous cycle. There’s nothing inherently evil in “the rich getting richer;” but those caught in the vicious cycle of a crumbling community may be easily persuaded that it is so.

Knowledge and community decision-making

In 2007, Sarewitz and Pielke wrote an interesting article on “reconciling the supply and demand for science” in policy-making. They focused on research to guide policy formulation. I’ve generalized their work to “knowledge-gathering” to support community decision-making. For example, this would apply to expert advice.

S and P start with an often-overlooked aspect of decision-making – the need to engage the decision-maker when gathering knowledge to inform decisions. The decision-maker has to identify both the drivers for making a decision and the information needed. This led me to a useful (at least to me) set of 3-D cartoons that sort of predict/explain what “works” in providing information for decisions:

• A decision-maker who can communicate information needed for decision.
• A decision-maker engaged with those gathering information.
• The information provided is both relevant and comprehensive, i.e., all of the information available that helps the decision-maker make a good decision.

Each of the planes (Engagement-Knowledge, Engagement-Relevance, Knowledge-Relevance) can throw additional light on what leads to U3 advice (useful, usable and used!). In the following, the blue lines point toward the quadrant most likely to lead to U3 advice.

Human action

For many years, I have looked at communities through the lens of systems. In this age of specialists and technocrats, I’ve found this a very useful way to begin to solve a community’s problems – not just those that are obvious, but those that cascade through the community. Systems thinking lends itself to ferreting out the linkages – the interdependencies and couplings – that are key to avoiding unintended consequences.

Occasionally this approach has been criticized as too mechanical, and that it ignores the “human in the loop.” Actually, human action is at the core of my thinking. I define my systems in terms of people, and a system’s processes in terms of the actions that the people who make up the system take. So, for me, the “transportation system” isn’t roads, bridges, airports and so on, but rather the people who ensure that people and goods can safely and expeditiously go where they are needed. The people in these systems have fixed (infrastructure) and dispatchable (e.g., skilled technicians, maintenance equipment) assets that they use to take action – to achieve the system’s purpose.

This approach provides the context within which human action takes place. Too often we look at results as simply manifestations of the skill (or lack of skill) of those who are taking action. That misses the point that the results are also conditioned by how the system is connected, or wired. Skilled people usually overcome bad wiring, but at the cost of efficiency. The efforts of the less skilled are likely to be negated by bad wiring. Context counts!

Systems thinking also forces me to focus on linchpins and their connections. These are members of one system who link their system to others. In particular, these linchpins are crucial to the success of efforts to transform a community. In fact, my experience indicates that a community cannot positively transform itself without a tight web of linchpins who can work together.

“Wilding” and communities

After almost every plague in history, there has been a period of “wilding” – during which many survivors threw caution to the winds (France after the Terror may be another kind of example.). In the 1920’s, after the Spanish flu pandemic too many people invested money they didn’t have. Inevitably, this led to an economic crash but with socio-political impacts as well.

During the recent pandemic, many local governments intensified the social damage (via lockdowns and fear-mongering) caused by the pandemic which has led to a more severe “wilding” than we saw in the 1920’s. The riots of 2020-21, the public excesses of the LBGTQIA+ … are manifestations of this “– survivors”wilding.” In the 1920’s, the inevitable crash was more economic (reflecting the major mode of “wilding”?). This time, I fear, the crash will be more socio-political, and probably violent. Communities need to prepare for a crash, whether it is the violence I fear or whatever their guts tell them it will be.

Communities’ responses will likely be hamstrung economically, but good leadership can overcome that. The question we must then ask is – have we elected effective leaders?

Is resilience an illusion?

We are not animals. We are not a product of what has happened to us in our past. We have the power of choice. ~ Stephen Covey

Last month, Claire Rubin – knowing my obsession with great interest in all things Resilience – sent me a link to a blog by Professor David Alexander – Resilience is an Illusion. After reading it the first time, I promptly went on vacation for two weeks, still pondering Alexander’s provocative post.

Surprisingly, I agree with much of what Alexander wrote, while disagreeing with his conclusion (obviously!). His view of Resilience is that of Hollings – a sort of Nietzschean eternal recurrence. This was originally focused on ecological systems returning to a stable state after a disturbance. Alexander quite properly points out that Change has become inherent in our lives. Instabilities of many types abound, often coupling with strong underlying trends. He concludes that Resilience “can only be attained by constant adaptation, which is a case of pursuing an ever-receding goal.” Thus, for him, the illusory nature of Resilience. He closes by advocating that we focus instead on vulnerabilities – identifying and reducing them.

Personally, I’m really uncomfortable with the Hollings view of Resilience (and his and Lance Gunderson’s overlying Panarchy concept), especially when applied to communities. I have two fundamental problems with the concepts: time and agency.

Even if this eco-construct is completely accurate over the long-term (e.g., it has been applied to the Roman Empire’s rise and fall), it is descriptive rather than predictive. If I’m a community leader worried about my community’s future, it adds nothing to my understanding of what’s happening next week, next month, next year or even next decade.

Similarly, while the concept is useful in describing the evolution of ecological systems, it seems to assume that over the long-term communities are essentially passive. Awash in a sea of influences, a community thus resembles a ball in a multi-dimensional game of ping-pong, unable to dodge any of the paddles aimed at it.

This ecosystem conception of Resilience when applied to communities (or any type of human society) ignores the fact that they are made up of humans. As implied by the Covey quote above, we think, we dream, we aspire, we create. While we cannot completely control our Future, we can envision what we want it to be and steer our lives toward it.

To put this in terms of the Law of Community Momentum, this ecosystem concept changes the Law from “A community’s trajectory will not change unless some force changes its path” (i.e., trajectory is destiny only if you take no action) to “A community’s trajectory will not change.” Alexander ultimately seems to accept this while calling Resilience illusory.

In fact, I strongly agree with Alexander that community resilience requires – demands – that communities adapt to their changing contexts. Alexander seems to despair of their ability to do so. I don’t. I believe that if a community’s leadership can stare into the abyss of the present clear-eyed and without ideological blinders, they can find a path to a better future. And if they are committed to their communities, they will take it. We have examples of this – Charleston, SC, taking advantage of Hurricane Hugo’s havoc to build a stronger, more livable city. We have Pittsburgh and Charlotte – each reinventing and reinvigorating itself – in the face of crumbling economic foundations. In each of these, we have leaders who cared about their communities enough to step up and act. Each of these a case study for the reality of Resilience. Resilience illusory?  Absolutely not!

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Muddling Through

Life was a damned muddle – a football game with everyone offside and the referee gotten rid of – everyone claiming the referee would have been on his side. ~ F. Scott Fitzgerald

In this series, I am presenting scenarios that represent possible Futures. In the last – The Empire Strikes Back – I laid out a “low resilience” scenario. In that scenario, the Democrats triumph in the 2024 election, and essentially take control of the government. Freedom’s light dims, and communities have little say in their own Futures. I personally don’t expect either of these to be the path we follow. Instead I expect us to “muddle through;” to use John Mauldin’s apt phrase, our country will be a bug looking for a windshield.

This scenario starts with a bang – neither Trump nor Biden win the election in the Electoral College. Kennedy wins enough states so that neither Trump nor Biden have the requisite 270 electoral votes. The election is thrown to the House of Representatives. The incoming House has a slightly larger Republican majority, but it is still close – 27 states for Trump, 23 for Biden.

The new Congress starts similarly as in 2016, rescinding many of the regulations put in place by the previous administration. However, with only a slim majority little else is accomplished. The Sestercentenial in 2026 is rather muted; lost in the uproar over Trump’s decision to forcibly expel illegal aliens from the country. Although the vast majority of the country is initially in favor of the policy, the videos of the use of force and the heart-rending separation of families turns the tide of opinion against it.

In 2028, Governor Newsom narrowly defeats Governor DeSantis after DeSantis pauses his campaign due to the recurrence of his wife’s cancer. In 2032, DeSantis wins a large personal victory, but, again, the GOP has only a slight majority in Congress. As a result, compromises that “save” Medicare and Social Security are little more than kicking the can down the road. While there is much furor over individual initiatives each side takes, our policies lurch from Left to Right and back again, with no net accomplishments by either side.

In foreign policy, China’s threat to take Taiwan by force slowly recedes as China’s leaders try to stop “peak people.” Its population (already less than that of India in 2024) may decrease by as much as 1% per year. This same niggling problem impacts the entire developed world over the next two decades.

For communities, it is “The Good, the Bad and the Ugly.” To the good, communities are able to plot their own course; ever-changing policies mean that local politicians are only responsible to their voters for what they do. The Bad – polarization leading to migration. Communities in Blue states such as California and New York struggle to deal with a shrinking tax base and blight. They face the same problems unsuccessfully faced by the Rust Belt decades before. Red state communities must strain to provide services to a welter of new arrivals. And the Ugly confrontations between angry populists and arrogant technocrats proceed apace.

Under these conditions, communities that forge lasting coalitions between local government, local business, NGOs and higher education are likely to be the most resilient. Because of their extensive connections outside the community, they are most likely to take advantage of any opportunities and be able to leverage state resources. These coalitions are also likely to be flexible – they can “stop on a dime and give you nine cents change.” They are likely to recognize that positive change is incremental, and have the patience to accept incremental progress.

* This roughly mirrors the current makeup of the House. However, the outcome could easily be different depending on the votes for House members. The Republican will probably firmly control the delegations of 26 states; they currently have control of the North Carolina delegation by only one vote. I’ve assumed that the GOP will gain hold serve there to nail this down. Conversely, the Dems firmly control the delegations of 20 states, with very slim control (one vote) of Michigan, Nevada, Pennsylvania and Virginia.

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Some of you may be interested in a new paper I have written that is now available online (abstract below). Published in the Journal of Critical Infrastructure Policy, it’s entitled “Making Policy for Complex Adaptive Systems.” Liesel Ritchie made the connection between myself and the journal’s editor, Rich Little; I’m most grateful to her!

Abstract:

We have come to rely on a variety of systems – social, economic, environmental – in our modern world. All of these systems are made up of people, working together, to carry out an important function. All of these systems are complex and adaptive. In the face of change, they each may react in different ways, often unpredictably. If they are unable to react to the stress caused by change rapidly enough, they may fail – no longer providing the product or service we’ve come to rely on.

Unfortunately, many policies are being enacted that do not recognize the nature of these systems. Though often well-intended, policies made that do not consider the systems that they impact can lead to failure of those systems. We use the rolling blackouts that began to afflict California’s electricity consumers in 2020 as an example of this type of failure. We conclude with lessons learned to help policy makers “embrace the complexity” of these systems.

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Five Pillars of Community Resilience

Optimism is a strategy for making a better future. Because unless you believe that the future can be better, you are unlikely to step up and take responsibility for making it so.

Noam Chomsky

The other week, Claire Rubin sent me a link to an article from Beaumont resilience training – Here’s How to Use Resilience to Move out of Your Comfort Zone. The author – Laura Ponting – focuses on the personal growth of the individual, but her “five pillars” seem to fit well with my own conception of community resilience as a springboard for growth – development or strengthening – of a community, i.e., for making a community more Future-fit.

I apologize to Ms Ponting – I’m reorganizing her order to suit my [sometimes – often?] warped logic. I’ve also taken some of her words out of context, to help make my points. I’m also couching this in terms of systems; after all, communities are first and foremost complex adaptive systems (CASs). I’ll also use the Seven Community Capitals as a way to clarify some of my points.

First Pillar: Purpose. Future-fitness – growing in strength and capability – requires not just action, but purposeful action. Action — to strengthen the individual or the community. Since a community is only successful if it provides the quality of life its members want, the purpose of becoming more resilient is to safeguard that quality of life, and to improve it if possible. This is often called a vision; the community’s conception of what that stronger community will be. The community as a whole has to buy into that vision, or else it is likely that it won’t be realized.

In particular, this requires cultural capital. A common language to describe and understand that future state. A common self-confidence that breeds optimism. As Chomsky implies, without that confidence and the optimism it engenders, the community won’t work together to achieve it.

Second Pillar: Self-awareness. “Know Thyself” is not just an inscription on a Greek temple; it is the zero-th step for any community plan. For the community as a system, self-awareness means knowing who its members are. It means knowing how they are connected – or not. It means knowing the “balances” in each of its capital accounts, and the constraints or limits on the use of each capital. Most importantly, it means that the community’s leaders know how to make decisions and take action (human and institutional capital).

Third Pillar: Mindfulness. “Mindfulness” for a community equates to situational awareness. Communities as systems are in dynamic environments, with trajectories conditioned by both internal and external forces. “Mindful” communities recognize not only where they’re headed, but the forces that are driving them. The fine people at ResOrgs in NZ consider situational awareness one of the four enablers of effective crisis strategic planning.

Situational awareness rests on the community’s social capital. It requires “ears to the ground” within the community to gauge the community’s mood and its ability to move. Situational awareness also requires linchpins keenly tuned in to sources of information outside the community. They can warn the community about new sources of stress and alert the community to unexpected opportunities.

Fourth Pillar: Self-care. As individuals, we know we have to take care of ourselves. We exercise (well, some of us do). We have physicals to tell us whether we’re overweight, have high blood pressure, are pre-diabetic or any of the other warning signs the doctor looks for. And if we’re wise, we take action to avoid further damage to our vitality.

The same holds true for our communities. We know that if we fail to maintain our homes or our physical and natural infrastructure, they may be damaged in a severe storm, or even collapse from neglect. But the same holds true for our “softer” infrastructures.
• our community’s culture that, at its best, brings us together and gives us the confidence to act;
• our community’s social networks that enable us to communicate with each other, and – in times of crisis – tell us where resources are needed;
• our community’s economy that provides us with the financial capital to take action.

And just as we as individuals have physicals to point out where action is indicated to strengthen us, so too should we in our communities be aware of those signs that point out that action is needed. More frequent maintenance of physical systems, rising crime, a fraying social fabric, or growing poverty each are indicative of the need for “self-care” for our communities.

Fifth Pillar: Positive relationships. Ms Ponting couches this in terms of finding people to support us, esp. as we strive to better ourselves. The same holds true – in spades – for communities. It is simply a reflection of the economy of scale. The more resources we can bring together, the more we can do. If we work smartly (after all, two heads are better than one) we can make our communities more functional and better places to live for all of us.

But there are also traps for the unwary in this. First, “working smartly.” If we let ideology overrule reality, in other words if we don’t couple Purpose and Mindfulness/Situational Awareness, then we may actually harm our community. The debacles that so many of our big cities have become – crime, filth in the streets, the ugliness of the hopeless homeless – are monuments to failed ideologies not rooted in reality.

The second trap seems to be endemic in our age of “engagement.” Even the best of ideas can die the death of a thousand cuts in a committee. When egos are engaged, everyone wants to see a little of themselves in what’s done. That leads to inefficiencies and sometimes even alters the idea so much that it no longer supports the Purpose.

A third trap is the difficulty in overcoming the distrust and mistrust that seems to be endemic in our not-so-civil civil life. It sometimes seems that no one has the authority to act but everyone has veto power over any action. Relationships ultimately are grounded in trust. In our age, however, Trust has become a rare commodity. Thus, building positive relationships particularly in our polarized polity is not for the faint of heart.

Five Pillars for strengthening us as individuals; Five Pillars to move our communities toward a better and more secure future. That should be our Purpose. Achieving the Purpose has to be grounded in self- and situational-awareness, so we can set a realistic path from today to that better tomorrow. As we advance upon that path we must maintain those strengths we rely on to move forward – self-care. And if we can find willing partners to support us, these positive relationships can help us to advance more rapidly.