Managing Complexity

The past six months has provided us an education in epidemiology, virology, and human behavior. While we didn’t sign up for these lessons, they became prerequisites for making sense of living during a pandemic and predicting what’s next. How will our kids be educated? Will elections be safe and fair? Will the newly unemployed be hired? Will the entertainment businesses survive?

We are seeing signs of coronavirus outbreaks slowing in Arizona, Texas and Florida. This is likely the result of human behavior changes in staying home, wearing masks, hand washing, and social distancing. We are also learning valuable insight to how our immune systems combats the coronavirus. This new insight may not impact the fall, though it may have a major impact on 2021.

Our National Institute of Health (NIH) Director Dr. Francis Collins highlighted this week new insight on how our T cells fight COVID-19. While the antibodies produced by our immune systems get most of the media attention related to vaccines, therapeutics and testing, the human T lymphocyte (T cell) needs to be added to our vocabulary and discussions.

The findings of a new study reported in Nature, suggests these T cells may protect some people newly infected with SARS-CoV-2 by remembering past encounters with other human coronaviruses. Four of the six known coronaviruses are responsible for the common cold. This might explain why some people get very sick while others are asymptomatic.

This study indicates we need to add T cells to the general publics epidemiology and virology curriculum. T cells may soon become part of our testing, therapeutics and vaccines discussions.

When it comes to managing the complexity of living during a pandemic,
developing predictions based on our understanding of many impacting elements is essential. These predictions will become the foundation for our strategies to new manage the complexity of 2021.

Image: Scanning electron micrograph of a human T lymphocyte (T cell) from a healthy donor’s immune system from National Institute of Allergy and Infectious Diseases/NIH.

When we decide we want to improve our health, transform our business or launch a new product, we develop a plan and execute it. As described in Why We Struggle with Complexity, we often apply logic and analogies of similar circumstances to make sense of these complex challenges and to determine what to do. We know logic (if this, then that) and analogy (like that, except this) decision making can fail miserably with complexity.

Logic becomes ineffective when there are too many variables or when variables are unknown or unpredictable. Analogies becomes ineffective when the context, people and variables are too different.  If you failed to achieve a complex goal after hard work and developing the required knowledge, it is likely due to oversimplifying complexity with logic and analogy. You were likely impacted by one or more of these factors:  

• Too many impacting variables for logic and analogy reasoning methods
• Unknown impacting variables that are difficult to identify
• Uncertain impacting variables difficult to predict how they will act, react, or interact
• Unpredictable outcomes due to change and the combination of the variables

Managing complexity is not new to scientists, inventors, scholars, and system engineers. They learned they need to perfect the process of:

• Understanding and predicting the complex system
• developing strategies that achieve the mission while addressing the complex system
• iterations of adjusting the strategy and model of the complex system

With complexity, a definitive answer based on logic or analogy is not possible. The logic and analogy reasoning that we were taught in school, is no match for too many, unknown, uncertain, and unpredictable variables. The best strategy comes from many iterations until the mission is achieved. Here is a closer look into how they manage complexity.

Scientists such as biologists, ecologists, meteorologists, psychologists, and sociologist model how numerous elements (variables) will act, react, and interact (complex system). Meteorologist use these predictive models forecast rain, wind, temperature and even hurricanes. When their weather predictions miss their mark, they make incremental adjustments to their models of the complex system, change and strategies. This results in a continuous cycle of improvement in forecasting.

Inventor Thomas Edison built a model for managing the complexity of invention. His model included a Menlo Park research laboratory, investor funding and teams of people with diverse domain knowledge. He used this invention framework to make over 3,000 incremental adjustments to his lightbulb creation. He had to address the complexity of finding the right balance of low production costs, durable filaments, brightness, and low energy consumption. While Edison did not invent the lightbulb, he did invent a bulb that fit within his “seven-point program” to be competitive with gaslighting. The seven points included “the parallel electric circuit; the durable, high-resistance light; the improved electrical generator; underground copper wiring; devices to keep the electrical current constant no matter the location of the bulb; safety fuses, insulating materials and usage meters; and lighting fixtures with switches to turn them on and off (1). Since electric power distribution did not exist, Edison had to manufacturer most of the devices. His first light bulb became commercially available in 1879 and he introduced the first electrical power plant and distribution system in New York in 1882 to create demand for his innovation.

Hedge fund firm Bridgewater Associates built a model to predict financial markets and to test their investment theses. To ensure as many plausible viewpoints are considered about the model’s impacting elements (complex system) and investment thesis (strategy), company founder Ray Dalio instituted a corporate framework of meritocracy. Bridgewater hiring strategy ensures a wide diversity of domain knowledge and meritocracy strategy solicits the best viewpoints including from the new hires just out of college. They document the success of employee theories, inputs, and persuasiveness to understand their decision-making process. They then make incremental adjustments to their predictive models, decision making process and investment theses based on what is learned from their multi-billion dollars of investments. Bridgewater used their predictive modeling and culture of meritocracy to grow to manage $150 billion of capital and one of a few organizations correctly predicting the 2008 Great Recession. 

The system engineers of Amazon, Apple, Facebook, and Google show us how to thrive with managing complexity within frameworks with thousands, or millions of incremental improvements. It would be hard to imagine how we managed our lives with the early versions of Amazon (1994), Apple iPhone (2007), Facebook (2004) and Google (1998). These companies have become industry leaders in managing complexity within their frameworks with the use of Artificial Intelligence based on data science and deep learning algorithms. Google search analyzes “short clicks” versus their “long clicks” to continuously make incremental improvements to their solution. If users click on the first search result and does not come back (“short clicks”), the were likely satisfied. If users click on the first search result, spends time there (“long click”), then clicks and become satisfied with the second result, the deep learning neural network may make slight adjustments to the weighting of some of the hundreds of search signals and sub-signals.

Managing complexity is not something most people are taught in school, that is unless they enroll in advanced college degree programs.  Master’s and post-doctorate degree programs study complex systems, such as biology, ecology, economics, meteorology, political science, psychology, or sociology.  While these degree programs teach the complexity of their subject domains, they have little time to study the fundamentals of complex systems or how to address the complexity that fills our lives.

To manage complexity, scientists, inventors, scholars, and system engineers use frameworks to understand their complex systems and predictive modeling of their strategies. The use iterations of incremental improvements to their frameworks and predictive models as they learn. They determine the most effective strategies (forecasts, innovations, theories, and products) through iterations of trial and error, learning and adjusting their frameworks and models until they get it right.

To achieve complex goals, we can apply some of the same complex system and iteration thinking principles used by experts. We learned logic & analogy methods in school in a formal way. We learned complex system and iteration methods in life in an informal way while achieving our complex goals.  We accomplish complex goals by making sense of the complex system, developing an effective strategy and iterations of “learning by doing” until we achieve our mission.

Logic & Analogy	Complex System & Iteration
Few variables	Many variables
Known variables	Known & unknown variables
Certain variables	Certain & uncertain variables
Rules & formulas	Frameworks and models

To be successful achieving complex goals to improve our health, transform our businesses or launching a new product, we need to be efficient and effective assessing the situation (complex system) and developing a plan (strategy) to achieve it. Complex system and iteration thinking can help get you there. To create frameworks of complex goals (complex systems), strategies and to manage iteration like experts, it requires understanding the eight essentials to managing complexity.

(1) Josephson, Matthew, Edison: A Biography. McGraw-Hill, 1959, p229

For the first time since 1962 and 1963, the United States life expectancy at birth has declined two years in a row.  This has been mostly attributed to higher death rates among young and middle-aged Americans with a 21 percent increase in fatal drug overdoses and the death rate doubling from synthetic opioids like fentanyl.  This overshadows the progress made on reducing deaths from seven of the ten leading causes of death (heart disease, cancer, chronic lower respiratory disease, stroke, diabetes, influenza and pneumonia, and kidney disease) over the past year. The three of the ten leading causes of death that contributed to the decline in life expectancy were unintentional injuries, Alzheimer’s disease, and suicides.

We have added 38 years to life expectancy over the past 150 years, though our progress has seemed to hit a wall.  We learned how to address and eradicate infectious diseases with immunizations, reduce deaths from infections with the discovery of antibiotics in the 1930s, reduce heart attacks through education, medicines and treatments, reduce cancer deaths with smoking awareness and inventing chemotherapy and radiation treatments, as well as improving health outcomes by identifying maladies earlier with the advances in medical imaging.

Unless we invent something as profound as antibiotics or immunizations, it is unlikely we will make much progress without a clear mission and new approach. Here are the areas that need improvement:

1. Managing Complexity – To get better with improving the top ten leading causes of death.

Complexity – the unpredictability of many impacting elements (people, communities and realities) that operate, interact and react in both certain and uncertain ways.

We must go beyond managing the complexity of medical treatments. We must address the 80-85% of health determinants (impacting elements) that contribute to health outcomes that are outside the health care system. These impacting elements include behavior, health literacy, environment, socioeconomic and other social determinants. To get better with managing the complexity of health, we will need to improve our understanding of how these thousands of certain and uncertain elements are impacting our individual health.

2. Mission – Develop clearly defined missions.

Our mission probably should be to improve health span (years without functional limitations) rather than live span. Who owns improving the health span mission? This is obviously a mission beyond any one organization. Yet we can agree it is a worthy mission. Therefore, we need to link the many missions that could contribute to achieving this mission.  The improvement in seven of the leading causes of death (heart disease, cancer, chronic lower respiratory disease, stroke, diabetes, influenza and pneumonia, and kidney disease) was due to many organizations with missions to improve health outcomes without anyone owning the entire mission. By linking the missions together, we can identify areas of impacting elements (health determinants) that are not being addressed.

3. Strategies – that make progress toward the mission and that address the impacting elements

An example of this is the four strategies offered by Dr. Joshua Sharfstein to address the increasing death rate from opioids in an article in Journal of the American Medical Association.

Addiction Treatment – The use of the opioid agonists methadone and buprenorphine reduces overdose, illicit drug use, crime, and transmission of infectious diseases.

Criminal Justice – It is now recognized by many across the political spectrum that the arrest and jailing of millions of Americans for their addiction has complicated efforts to address the opioid epidemic.

Health Care System – There is now broad understanding that the overprescribing of opioids has contributed to today’s opioid epidemic.

Looking to Evidence – On opioids, it can sometimes seem that there are 3 bad ideas for every good one. Public officials have supported limiting the number of naloxone resuscitations and afterwards letting people die, requiring drug testing before enrolling in Medicaid, and launching stigmatizing public relations campaigns that can reduce the chance people will seek treatment.

Managing these strategies to reduce the death rate from opioids will not be easy. These strategies could benefit, as well as the ten leading causes of death, from an overarching strategy to address the impacting elements (health determinants) that are common across each of the leading causes of death.

4. Solutions – interventions that get us closer to our mission and address the impacting elements

It would require understanding how to cost-effectively manage the complexity of the many interacting and uncertain elements that impact health outcomes.  It may require thousands of different medical and non-medical interventions that can be measured for cost-effectiveness. If the solutions are not cost effective, they cannot be sustained.

5. Measures – to measure progress

This includes measuring the progress toward achieving the mission and the understanding of the impacting elements. We must also measure the performance of strategies and solutions to determine what works.

The approacch to measure could be similar to what Google does with its search algorithm, Facebook does with engagement improvement and Amazon does with moving merchandise. They have created frameworks to manage complexity by using data to provide insight on opportunities to drive incremental improvements.

While genetics plays into many of our chronic diseases that limit our function (health span) and contributes to early deaths (life span), we also need to understand how the non-biological factors (impacting elements) impact and influence gene regulatory functions.  This will not be easy. It will be a worthy journey rather than a destination.

There is no doubt artificial intelligence will help us in the future with managing the complexity of improving health span. Yet, for artificial intelligence to help us get over the complexity wall, we will need a framework and massive amounts of data for it to be effective like it is with Google, Facebook and Amazon. To create this framework and data, we need to address the five areas listed above as soon as possible. This will provide insight on how to make incremental improvements every year to health span and life expectancy like we have done for most of the past 150 years.

Why do good ideas fail? Why don’t business strategies achieve their mission? Why doesn’t expert career advice work for us? Why hasn’t proven weight-loss methods delivered our desired weight? Why wasn’t knowledge and hard work enough for success? The surprising answer to these questions is likely the same. It’s our struggle with complexity.

To achieve something meaningful, we must manage the uncertainty of complexity, yet we’re not taught that in school. Complexity involves many interacting certain and uncertain variables, that impact our health, intimate relationships, transforming organizations or finding cancer cures. We are taught in school to use known and predictable variables, logic and formulas to find answers. We learned through literature, history and science how people navigated complex challenges and to identify relevant similarities to infer analogies to help guide us though our challenges.

While these approaches work in school, they often fail miserably when applied to the complexity of transforming our health, careers, businesses and nation.

Complexity – the unpredictability of many impacting variables (known and unknown) operating, reacting and interacting in both certain and uncertain ways.

Scientists, inventors, scholars and system engineers have studied complexity for centuries to make sense out of biology, ecology, economics, politics, psychology, social science, weather and creating new products. They have long known that managing complexity requires complex system frameworks to understand the impacting variables and the use of an iterative process of incremental improvements to be successful.

When faced with the uncertainty of complex decisions, experiments by psychologists Amos Tversky and Nobel Prize winning Daniel Kahneman demonstrated that we use mental shortcuts, cognitive biases and bypass facts. They found we use logic based on a small subset of the impacting variables while ignoring many important factors. They observed how our decision are influenced by our cognitive biases based on analogies with relevant similarities.

While the logic and analogy thinking we learned in school is effective with most everyday decisions, its effectiveness diminishes with each increase in complexity. It contributes to our struggle with complexity when there are too many variables, unknown variables and unpredictable variables. While logic and analogy thinking has improved our standard of living, it has limits in getting us to the next level in our modern day of expanding complexity. To manage complexity, we can learn from how scientists, inventors, scholars and system engineers do this every day.

When people break into banks, they don’t tend to deposit money. Which explains why JPMorgan Chase and Wells Fargo spend billions of dollars each year on artificial intelligence, physical security and cybersecurity to prevent financial and identity theft.

When Cambridge Analytica compromised 87 million Facebook users’ data, they deposited millions of dollars into Facebook’s bank account. Which may explain why Facebooks’ massive, world-class team of Artificial Intelligence professionals in Building 20 were not working to prevent bad actors from purchasing ads and stimulating users emotions with sensational material (a.k.a. click-bait).

Cambridge Analytica claims they have 5,000 data points on American voters. They leveraged Facebook’s artificial intelligence powered capability that predicts how each of their two billion users will behave, think and purchase. Yes, if you are a Facebook user, they may be able to predict your future behavior better than you. Advertisers have found this insight to be so valuable that they helped drive Facebooks net income to $15 Billion in 2017.

What is Artificial Intelligence?

Artificial Intelligence is the machine’s (computer system) ability to perform tasks that normally require human intelligence and the capability (machine learning) to improve its performance without humans explaining how to do it.

The artificial intelligence buzz, or internet click bait, has promoted fear that artificial intelligence may become the supreme ruler of mankind and that it will one day eliminate your job. Word travels fast when Elon Musk states AI is more dangerous than nuclear weapons and when McKinsey predicts that 73 million jobs in the U.S. will be eliminated by 2030. There are few views, shares and likes for another McKinsey study which predicted AI will drive as much as $5.8 trillion of new economic value across nineteen industries after they analyzed four hundred AI use cases.

Most AI Resources Focus on What Humans Can’t Do

The popular stories that go viral include AI beating champions of chess, Jeopardy and Go, yet quietly Facebook’s AI experts tune their machine learning models to predict how each of their 2 billion users will think, act and buy.  We are inundated with AI-powered autonomous vehicles stories that forecast the elimination of millions of driving jobs, while Goggle’s AI team behind the scene are tuning their machine learning models to you, your context and what you my buy online. We are no longer surprised by stories of how AI is able to detect cancer on medical images as well as radiologists, yet we don’t hear how Bridgewater Associates’ AI team is effectively turning their AI frameworks to predict financial markets for their investors of over $150B.

We mostly hear about the small fraction of AI investment going to what humans can do, such as play games, drive cars and diagnose cancer. We hear very little about the objectives of most AI investments, which is to get machines to perform that haven’t ever been effectively done by humans.  Even if humans can predict the human behavior of a single person from Cambridge Analytica’s 5,000 data points, could humans effectively do that with 87 million users? Could humans effectively make sense of billions of data points processed by Google’s and Bridgewater’s machine learning models that predict what people want and the financial markets?

AI Investment is Going to Managing Complexity

Complexity is the unpredictability of many impacting elements (people and realities) operating, interacting and reacting in both certain and uncertain ways. To achieve a complex challenge, we must manage the uncertainty of complexity, yet we’re not taught that in school. Complexity involves many certain and uncertain elements that interact, and get in the way of our health, relationships, career goals, transforming organizations or finding cancer cures. Yet, we are taught to use certain known elements and formulas to find answers, which doesn’t help with complexity. While proven formulas and calculations may work in school, they fail to address the complexity of predicting human behavior, what people want and the financial markets.

This explains why Facebook, Google and Bridgewater abandoned their predictive algorithms and replaced them with Artificial Intelligence fed by machine learning models. Facebook CEO Mark Zuckerberg sees AI as the only way to address many complex challenges.

Zuckerberg referred to AI technology more than 30 times during ten hours of questioning from congressional lawmakers Tuesday and Wednesday, saying that it would one day be smart, sophisticated and eagle-eyed enough to fight against a vast variety of platform-spoiling misbehavior, including fake news, hate speech, discriminatory ads and terrorist propaganda. (Washington Post)

Mark Zuckerberg cited AI as the solution for many of the most complex challenges.

Moderating hate speech? AI will fix it. Terrorist content and recruitment? AI again. Fake accounts? AI. Russian misinformation? AI. Racially discriminatory ads? AI. Security? AI. (The Verge)

How Does Facebook Manage Complexity with AI? 

The AI technology of Facebook, Google and Bridgewater have operated in the shadows behind protective proprietary walls while driving massive shareholder value. For Facebook, the shadows began to fade when stories emerged about bad actors using Facebook during Brexit and Presidential elections. Then came the Cambridge Analytica story, which resulted in Facebook losing $35B in shareholder value. Then details of the activities inside Facebook’s Building 20 started to emerge.

Facebook uses an artificial intelligence-powered prediction engine called “FBLearner Flow” that self-improves by inputs from its several machine-learning models. The machine learning models process billions of data points from its two billion users’ activity to predict thousands of things including: users in a photograph, what people want in their data feed and what is likely to be spam. Inside documents state that the AI system can predict your behaviors.

Rather than focus on replacing humans, Facebook uses AI to augment their human engineers. “We’re able to do things that we have not able to do before,” he says Facebook Chief technology officer Mike Schroepfer.

Humans can’t cost-effectively interpret massive data sets and make sense of thousands of certain and uncertain elements, that interact and react in unpredictable ways.  Software developers can’t create computer algorithms for each Facebook user nor can they develop algorithms that can interpret how thousands of certain and uncertain elements, will interact and react.  Artificial Intelligence, fed with machine learning models, are beginning to do what humans or computer algorithms can’t do to. They interpret what we want in our Facebook feeds, what we are searching for with Google and what the financial markets will do.

Lesson From Facebook

1. It’s What Humans Can’t Do – AI is not simply cost-effective automation that replaces what humans can do, it is a way to address complexity in way that wasn’t possible before AI.
2. Algorithms Can’t Do It – Predictive algorithms are limited and become less effective as the number of data elements grow. Facebook, Google and Bridgewater moved to machine learning models that can improve their performance without humans programming how to do it.
3. You’re Missing Data Elements – You will need data elements that you don’t have today. Facebook and Google capture data outside their applications to help their machine learning models. If you need more on this, asks members of Congress who were briefed by Mark Zuckerberg on internet cookies.
4. You Need More Data – Machine Learning requires massive amount of data to be effective. Most AI professionals work for companies that have massive data sets like Amazon, Netflix, and Apple.
5. You Better Start Now – Machine Learning Models take many years to develop. They need to go through many iterations to get the data elements, their associates and weighting right to be effective. It is not an algorithm that can be written, it could take 3 to 5 years of tuning machine learning models before they may be effective.
6. You Need to Know All The Elements That Impact Your Mission – When Facebook, Google, and Bridgewater didn’t predict human behavior or financial markets correctly, they found they were missing elements that impact human behavior or financial markets. You need to invest time and resources upfront to identify all the impacting elements or your AI system could drive poor business decisions.
7. You Are Never Done. When managing complexity, you need substantial resources to continue working to address change and the unpredictable elements. The machine learning models will need to be adjusted based on the thousands of changing realities and new insight.
8. Protect Your Data. It costs almost nothing to share digital data. It can cost $35B in shareholder value if your data ends up in the hands of organizations that use the data like Cambridge Analytica.
9. Add Digital Product Miss-use to Your Risk Mitigation Program. Don’t wait until your company is trending on Twitter before using AI to address mis-use of your digital systems like Facebook.
10. Artificial Intelligence Professionals Are Very Expensive. If you want to hire AI resources, you will need to compete with Google, Facebook, Apple, Amazon and Uber. Top AI talent are commanding one million dollar salaries even at non-profits.

There are few goals in life more complex than living a healthy lifestyle. A recent study found that only 2.7% of the United States population are successful with diet, exercise, obesity and smoking. There are thousand of determinats of health (Impacting Elements) and thousands of interventions (medical and non-medical) that can address health determinants. We are beginning to see progress with a new approach, modern-day health coaching.

For more, see Could This Be Our Modern-Day Equivalent of Discovering Penicillin? 

The simple answer is, complex challenges are complex.

While that’s a correct answer, it’s not helpful in improving our abilities to manage complexity.  With complexity, we know simple solutions usually fail miserably. That’s unless the solution to a complex challenge fits on a bumper sticker and you are a politician.

Whether it is improving our personal health, eliminating homelessness or reducing income inequality, we know there are not simple solutions to these complex challenges.

What is Complexity?

The unpredictability of many impacting elements (people, communities and realities) operating, interacting and reacting in both certain and uncertain ways.

If we want to lose weight and adopt a healthy lifestyle, we know we need to eat better and increase our physical activities. While these simple solutions works for some people, it has not been successful for the 71 percent of United States adults who are either overweight or obese.

Mission success is affected by the many impacting elements; the people around us (families, friends, co-workers), our communities (work, social, neighbors, city) and many realities such as behavior, beliefs, culture, economic, education, environment, health, income & work, residence, resources, responsibilities, science, social, support & theories.

We know that choosing a mission to lose weight and adopting a healthy lifestyle is often not be enough to get us past the McDonald’s drive thru after a stressful day at work or amidst transporting young kids in the early evening to their many activities.

We Were Never Taught How to Manage Complexity

To achieve a complex challenge, we must manage the uncertainty of complexity, yet we’re not taught that in school. Complexity involves many certain and uncertain elements that interact, and get in the way of our health, relationships, career goals, transforming organizations or finding cancer cures. Yet, we are taught to use certain known elements and formulas to find answers, which doesn’t help with complexity. While proven formulas and calculations may work in school, they fail to address the complexity of achieving what we want or for transforming our lives, businesses and nations.

With complex challenges, achieving success requires managing numerous iterations of the solution rather than finding the solution at the start.

To make progress with complex challenges, it requires:

1. Mission – If the mission isn’t clear, it’s difficult to focus your limited time and resources to achieve it.
2. Impacting Elements – Understanding the many elements that could impact achieving the mission and how these elements will impact the mission and potential solution.
3. Solution Iteration – With complexity, it is virtually impossible to get the solution right without going through numerous iterations. This is due to impacting elements being uncertain and their reactions to the solution being unpredictable.

Managing Complexity Framework Example

Tim Reilly offers us an example of how Google Search manages complexity in his book, WTF: What’s The Future and Why It’s Up to Us. Google Search, the Facebook activity feed and Amazon suggestions are solution interations that are improved every day. Google Search analyzes the “short clicks” versus the “long clicks” continuously to adapt their solution. If users click on the first search result and don’t come back, the were likely satisfied. If users click on the first search result and spend time there (“long click”), and then come back to click on the second result, they were somewhat satisfied. If users click on the first result and quickly come back to click on the second response (“short click”), they were not satisfied. If the user eventually has “long clicks” on the third or fourth search response, after “short clicks” on the top results, the lower ranked results may be more relevant. If this is done by one person, it may be an accident. If it is done by millions of people, the Google Search solution must be adapted.

Google search uses a framework of over 200 signals and an estimated 50,000 subsignals (“impacting elements”). Based on feedback from each solution iteration, they may adjust the search ranking algorithm, the ranking of a subsignal or add additional subsignals.

Our complex challenges don’t require us to build a framework as robust as Google, yet we can learn from Google the power of a clear mission, understanding the impacting elements and continously improving with each solution iteration. The complexity of health, relationships, career goals, transforming organizations or finding cancer cures often requires hundreds or even thousands of solution iterations to find the right answer. Something they didn’t have time to teach in school.

 

A mother of three young children is ten minutes into her thirty-minute drive home after a challenging day at work. She will not remember anything about her commute. The prefrontal cortex of her brain that controls executive functions is consumed with thoughts of her kids, husband, parents, boss, clients, friends and other commitments. The basal ganglia of her brain that coordinates automatic behaviors is managing the driving on this ordinary trip home.

In our modern day, we want much more than work and family. The prefrontal cortex is fully consumed managing our five to ten complex goals across our life domains:

• Family – being a good parent, taking care of a sick parent
• Relationships – maintaining an intimate relationship, having a social life
• Career & Education – ensure career supports lifestyle and provides some purpose,
• Creation – creating something like a book, side business, or building a home
• Achievement – run a marathon, lose weight, save money for college tuition or retirement
• Community – coach youth sports, volunteer for a worthy cause or help someone in need
• Lifestyle – maintain a healthy lifestyle or a meaningful retirement

This may explain why cases of depression, anxiety and stress are rising. When innovations free us from physically demanding and routine activities, we typically replace it with more complexity.

Complexity – is the unpredictability of many impacting elements (i.e., people, communities, and realities) operating and interacting in both certain and uncertain ways.

To achieve something meaningful, it requires managing complexity. We must align with the uncertainty of people and communities (i.e., family, work, organizations, local communities or governments). While it can be hard to interpret how our realities (i.e., behaviors, beliefs, environment, health, social, technology) are affecting us, it is even more difficult to predict how realities affect others. We cannot predict how people, that impact our lives and goals, will act or react.

We may not realize how complex our lives have become while consumed with our daily activities. To comprehend how complex human life has become, we need to fit in some time to reflect on a time when life was less complex. It was though, physically demanding.

For much of human existence, until farming was invented 12,000 years ago, our ancestors were hunters and gatherers. The prefrontal cortex of the human brain evolved over millions of years to help us survive within these lifestyles. About one hundred and fifty years ago our world began to rapidly change. The evolution of human prefrontal cortex has not the time to keep up.

Robert Gordon illustrates how physically demanding living in 1870 was his book The Rise and Fall of American Growth. While people had their share of uncertainty, such as farmer with rain, insects and commodity prices, there wasn’t much they could do about it. There were fewer diet books, sources of entertainment and people complaining their work wasn’t meaningful.

Much of the available descriptive literature on living conditions of 1870 portrays a dismal existence of week-long household drudgery for housewives and dangerous, back breaking working conditions for husbands.

While steam engines, cotton gins, railroads, steam ships, telegraphs and rudimentary agriculture machinery helped ease the burden of human labor, it was still a physically labor-some environment as it was throughout most of human existence. In 1870, there were no automobiles, electricity, indoor plumbing, central heating, radio or telephones. Life expectancy was forty-five-years old. Seventy-five percent of the people lived in rural areas and almost half of the population worked in farming.

Diets – were mostly what farmers could raise on their own land like vegetables and pork preserved by being smoked or salted. Summers brought a variety of vegetables.  The winter consisted of food that could be preserved like turnips, beans and potatoes or derivatives of corn and wheat like cornmeal and flour.

Clothing – was made at home with needle and thread. Most adults had one or two every day sets of clothes. It was a very labor-intensive effort to make clothes for family members and to mend the cloths that required repairing. Ironing required heating a metal iron on the kitchen stove and being careful not to scorch the material.

Home Heating – there was no central heating. The only rooms with heat required chopping wood, hulling wood or coal, keeping the fire alive, removing the ashes and waking up to extreme cold.

Indoor Flush Toilets – were nonexistent. The urban dwellers relied on chamber pots and open widows and had to empty them in the back yard. The rest of people we lucky enough to have access to an outhouse.

Dark hours– there was no electricity so nighttime required a set of candles or kerosene lamps. The candles, wax lamps, and kerosene lamps required filling, cleaning, emptying and wick-trimming. A one-hundred-watt electric light bulb produces almost one hundred times the lumens of a candle. This required them to fit more work into daylight hours, making winter months even more challenging.

Cooking – involved fresh wood and coal carried into the house and ashes carried out. It required carrying 50 pounds of fuel for cooking each day.

Transportation – travel by rail, steam ships and horse drawn carriages was too expensive for most people. Few families could afford owning a horse. It wasn’t easy for those who had horses or hired them, there were few roads and they were pitted with ruts and pools of mud after rain. In the fall of 1872, horses throughout the northeast caught a strain of flu and could not be used for work. Cities came to a standstill.

Mail and Deliveries – were still two decades away in rural communities, where seventy-five percent of people lived.Sending or receiving letters required traveling to the nearest village with a post office and waiting in line for a mail clerk.

Purchases – It was a labor-intensive effort to go into town to purchase anything. The country stores had limited products and could charge high prices. Catalog buying came later in the century. Consumer spending went almost entirely for three necessities of food, clothing and shelter, with virtually nothing left for discretionary spending.

Laundry – they carried water into the house eight to ten times per day. Washing, boiling and rinsing a single load of wash required fifty gallons of water. They used washing tubs, washboards and had to carry away the dirty water. A typical North Carolina housewife walked 148 miles and carried thirty-six tons of water each year from a well, stream, or creek.

Bathing – this required lugging a heavy wooden or tin tub into a bedroom or likely into the warm kitchen. It required several trips to the well, stream, or creek to carry enough water. The water was boiled on the kitchen stove to be emptied into the tub. After the bath, the water had to be carried outside to be emptied.

Communications – newspapers were available mostly in urban areas. Magazines came later in the century. The newspapers and railroads used telegraphs, though it was not affordable to most people. Most communication was letter writing or showing up at someone’s home and waiting for them to come home.

 Life Expectancy – was only 45 years old due to infant mortality, poor sanitation and contaminated food and infectious diseases. Human and animal waste contaminated the streams and lakes used for drinking water. Most of the improvement in life expectancy after 1870 was in reduction of diarrheal diseases, typhoid, tuberculosis, and diphtheria from clean water supplies.

Work & Income – Eighty-seven percent of the workforce worked at jobs that were hazardous, tedious, and unpleasant. Forty-six percent of people worked in farming, 8% were domestic servants and 33% were blue collars workers doing routine and physically demanding work. Weekends off, the forty-hour work week and child labor laws didn’t come till the next century. Over 50% of the boys aged 14 -15 were in the labor force, this doesn’t even include those who worked on their family farm.

In addition to farming, many people were employed in the horse transportation business. Horses consumed one-quarter of the nation’s grain output and most transports were pulled by horses. Occupational misery were the jobs in horse manure removal.  The average horse produced twenty to fifty pounds of manure and a gallon of urine daily. This required a lot of labor to dispose of five to ten tons per square mile in cities each day.

Retirement – men “worked till they dropped” due to death or a disability, the concept of retirement didn’t exist. In 1870, only 34% of the population lived past their 65th birthday. Eighty-Eight percent of the people aged 65 – 75 worked in the labor force.

Entertainment – except for a few traveling musicians or circus performance, most entertainment was limited to playing cards and home board games.

Modern Day Complexity

While 1870 may sound dull, boring and physically demanding, it was much less complex. If almost every waking hour was consumed with securing basic needs of food, clothing and shelter, there were less complexity to manage. It is much different today.

More impacting people – social in 1870 was with family, now it is with the world via social media, globalization. You may need to manage interactions with five to ten groups of people for just one child. School, teachers, sports, organized activities, doctor, dentist, friends, playdates, and even more if you volunteer to help. Any of your five to ten complex goal likely requires addressing the uncertainty of many people.

More impacting communities –This includes informal and formal communities like industries (Banking, Information Technology, Medical), organizations (Companies, Governments, Non-Profits), locations (Towns, School Districts, Neighborhoods), Groups (Social Circles, Facebook Friends, Book Club) and families. You could be impacted by the communities in your and the communities in the lives of the people that impact you.

More impacting Realities – culture, economic, education, environment, globalization, health (nutrition, exercise, medicines, treatments options), institutions (like governments, education, healthcare – their costs go up, while their outcomes don’t improve), political (slow to adapt to the modern day), rules (more laws, regulations, social norms), science (scientific knowledge doubling every few years), security (now cyber threats), social and technology (artificial intelligence, robotics, nanotechnology).

More Changing Realities – any good plan is subject to the accelerating change within our modern day. We must constantly be managing the complexity of adapting to change.

More choices – While we want more options, they add complexity after we factor in the impacting many people, communities and realities into our decision making.

More infrequent decisions – these are complex decisions that require a lot of learning to make a unique decision based on the immediate circumstances. Most of these decisions didn’t have to be made in 1870 or were made less frequently. They include new jobs, job skills, technology, healthcare options, health insurance options, retirement saving options, or helping our teenagers figure out their futures.  Infrequent decisions can be consuming trying to learn enough about the impacting elements to make good complex decisions.

In school, we are taught how to use formulas to calculate the answer with certain elements. This doesn’t help much with complexity as there are often too many uncertain elements. We may not even know how we will react to the activities involved with achieving our complex goals like losing weight or living a healthy lifestyle. We can’t possible expect to know how other people, that could impact or goals, will act or react.

The basal ganglia will continue getting relief as self-driving cars and automation replace routine activities. This will free us up to add even more complex goals to our lives and more demands on the prefrontal cortex.

It is more likely that human life will continues becoming more complex than less. Our future well-being will be related to how well we manage complexity at home and at work. As routine work gets replaced by automation, artificial intelligence and robots, our income will mostly come from non-routine work.

We will need to improve our abilities to manage complexity. This will require developing frameworks designed to achieve complex goals while addressing the many impacting elements (people, communities, realities). We will need to processes within these framework to help us manage the incremental improvements until we get it right. Just like Thomas Edison did over three thousand times until he invented the lightbulb.

Source: The Rise and Fall of American Growth, Robert Gordon, Princeton University Press, 2016

Economist Richard Thaler won the Nobel Prize in economics yesterday for his work in Behavioral Economics. This is a field of study of the complexity of human decision making. He is second person to win a Noble Prize in economics for Behavioral Economics. Psychologist Daniel Kahneman won the same award in 2002.  The field is a bridge between economics and psychology in decison making.

The mainstream model of people making decison as rational-utility maximizers, was upended by the studies by Thaler, Kahneman and others. They introduced us to many concepts that come into complex decision making including:  “endowment effect“, “loss aversion“, “status quo bias“, and “winner’s curse”.  People are complex. Behavioral Economics helps us understand this complexity.