Managing Complexity

Interesting new Alzheimer’s research that looks beyond amyloid plaques buildup in the brain.

Researchers conducted the most extensive analysis on the genomic, epigenomic, and transcriptomic changes in Alzheimer’s patient brains. By analyzing over 2 million cells from 400 postmortem samples, they offer insight into the interplay of four areas to help treat Alzheimer’s disease. 

Transcriptome – RNA-sequencing to analyze the gene expression patterns of 54 types of brain cells. They found impairments in the expression of genes involved in mitochondrial function, synaptic signaling, and protein complexes needed to maintain the structural integrity of the genome.

Epigenomics – The chemical modifications that effect gene usage within a given cell. The found they occur most often in microglia, the immune cells responsible for clearing debris from the brain.

Microglia – brain cells that make up 5 to 10 percent of the cells in the brain. They clear debris from the brain, are immune cells that respond to injury or infection and help neurons communicate with each other. They found as Alzheimer’s disease progresses, more microglia enter inflammatory states, the blood-brain barrier begins to degrade, and neurons begin to have difficulty communicating with each other.

DNA damage – during memory formation, neurons create DNA breaks. These breaks are promptly repaired, but the repair process can become faulty as neurons age. They found that as more DNA damage accumulates in neurons, it gets more difficult to repair the damage, leading to genome rearrangements and 3D folding defects.

The hype and doom of generative AI models such as ChatGPT often leave out the work ahead before their predictions can happen. While these new large language models will be transformative, they will have to address numerous technical, social and economic challenges.

Arjun Ramani and Zhengdong Wang describe many of these challenges in Why transformative artificial intelligence is really, really hard to achieve on the site The Gradient.

They discuss the challenges of AI creating productivity gains in areas such as healthcare, education, and construction that is labor intensive. The fine motor control of robots are not advancing as fast as large language models that still require millions of humans to annotate and train these AI models. They remind us that a lot of process knowlege (that AI models will use) is not written down anywhere citing Michael Polanyi saying “that we can know more than we can tell.”.

There are many AI challenges including data quality with common ontologies, securing data quanity, data sources (local vs. industry), model reproducibility, caputuring inputs and outputs that might not be digitized, privacy, data rights, trust, transparency and transformation of current processes. I have no doubt these will get addressed. How they get addressed will determine the promise and perils of AI.

Navigating the rules of the health insurance can be complex.

A majority of insured adults (58%) say they have experienced a problem using their health insurance in the past 12 months – such as denied claims, provider network problems, and pre-authorization problems.

This is from a recent Kaiser Family Foundation (KFF) survey of 3,605 U.S. adults with health insurance. It also found:

Nearly half of insured adults who had insurance problems were unable to satisfactorily resolve them, with some reporting serious consequences.

Yet the study found most viewed their health insurance favorably. Those with good health rated it higher than those reporting poor health.

 

Ben Dickson sheds light on what is behind ChatGPT and Microsoft’s Bing Chatbot named Sydney. These large language models (LLMs) will likely find their way into our normal routines. It is important for us to understand their strengths and weaknesses before is gets your coffee order wrong and eloquently explains to you why it was right.

His well-written post “To understand language models, we must separate “language” from “thought” describes what these LLMs, created through machine learning of massive data sets, do well and what they struggle with. He cites a recent paper Dissociating language and thought in large language models: a cognitive perspective that found:

LLMs show impressive (although imperfect) performance on tasks requiring formal linguistic competence, but fail on many tests requiring functional competence.

Leadership can be a lonely place. Especially during the Covid-19 pandemic if you are restaurant owner, school superintendent, healthcare leader, or wedding planner. It is impossible to process the complexity of the many knowns, unknowns and uncertainties to forecast the future and satisfy the impacted people. Yet, that is what we ask them to do.

As an example, here are 15 Covid-19 forecasts healthcare leaders must get right. See more.

The global pandemic has forced a curriculum change to understanding the complexity of our modern life. We were auto enrolled in immunology, epidemiology and forecasting lessons that are prerequisites to make sense of therapeutics, vaccines, and virus transmission based on human behavior. This understanding will help us forecast R value infection rates, vaccine timelines, eventual herd immunity and a new date for the family reunion.

The brilliant and talented Atlantic writer Ed Yong provides a ‘must read’ lesson on immunology and COVID-19, The Pandemic’s Biggest Mystery Is Our Own Immune System. Ed is a great explainer. If you like this article, I would strongly recommend you read I Contain Multitudes: The Microbes Within Us and a Grander View of Life. He explains the complexity of science in a fun and easy to read way. It is one of my favorite books.

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.