Applying economic methods to medical selection frameworks and genetic engineering advances
Medical selection frameworks have been rapidly evolving. Those frameworks are needed to govern rapidly advancing genetic engineering techniques including gene therapy and gene editing technologies such as CRISPR. [i] Medical selection frameworks help make decisions about which diseases and which individuals will be selected for genetic engineering interventions. These are the frameworks that "play god" in a way formerly reserved for nature. But before the gene trade thesis is discussed, let's explore the assumptions.
-------A word about the assumptions-------
Via medicine and medical technology, this article assumes:
The ability to change individual genes via gene therapy or gene editing already exists.
The ability to permanently change genomes via stem cell-based germ-line manipulation is very close. The CRISPR system is the closest to the ability to edit stem cells that will eliminate genetic conditions for an individual's heirs. Germ-line manipulation is akin to impacting humanity in a manner formerly reserved to natural selection.
The ability to change human genomes is rapidly advancing and improving. It is possible by the time this article is read, some new technology has already changed the ability to permanently change genomes.
This article addresses questions of medical selection to make heritable genetic changes via genetic engineering as if it is already available. While it is not readily available when this article was written, it is close enough that it is worth considering the implications. This article is forward looking - but only slightly.
We explore both sides of the market for gene-altering interventions and medical selection. We also explore the market environment. The market includes:
The demand side: Those being selected for gene-altering interventions, and
The supply side: Those making the selection and performing the gene-altering interventions.
The environment: The rules, habits, traditions, and agents governing the gene-altering market.
Like any healthy market, both supply and demand derive benefits by trading with each other. In the earlier graphic, we show "the medical selection trade" describing the mutual benefit, as well as some of the environmental agents, impacting the trade value. Our economics-grounded approach explores the promise, power, and challenges of genetic engineering.
Demand side: The first four sections explore the demand side of the gene-altering and medical selection market. Beginning with natural selection and biological information systems, this article explores a natural and cultural history perspective to address today's medical selection questions. These are questions of reconciling an individual's fitness to their environment. These questions had traditionally been resolved only via natural selection processes. Natural selection is nature's way of rewriting our genetic code in response to environmental needs. Today, humanity has the power of genetic choice. This choice includes defining environmental fitness in the context of our improving ability to rewrite an individual's genetic code. We explore how the choice environment is a powerful influence on medical selection.
Supply side: In the final three sections, we explore the supply side of the gene-altering and medical selection market. We address the incentives and environment of achievement for the scientists and entrepreneurs pursuing genetic engineering technology. We conclude by exploring curiosity in the context of scientists and medical researchers advancing genetic engineering. It is our intention to provide a balanced treatment of the benefits and the risks associated with those environmental incentives and our naturally occurring curiosity. We observe the strange self-referential conflict that those making medical selections about others' genetic destinies are themselves subject to similar genetic and environmental influences.
Environment: The gene-altering and medical selection market environment is addressed throughout this article. We explore why the environment may cause significant and unintended decision influence upon gene-altering and medical selection. Think of the environment as a collection of well-intended organization agents, like the government, hospitals, non-profit research organizations, non-profit advocacy organizations, and more. As part of the environment, we explore the leading medical selection decision framework called the "triangle of limits." In section 1, we discuss applying game theory to our genes and the environment defining our humanness. It is the "rules of the game" for gene-altering medical selection that will have an out-sized impact on our evolution.
This article provides a picture journey. The goal is to tell our gene story with infographics and a supporting narrative. These infographics aspire to be a "picture that is worth a thousand words." This gene story has the following sections:
Introduction
• Assumptions and exploration approach
• Standing on the shoulders of giants
• Our genetics as our superpower
Demand Side sections:
1. Our biological information system
2. The history and impact of genetic selection
3. The promise of genetic engineering
4. Where do you draw the line?
Supply Side sections:
5. The choice challenge
6. The growth and implications of gene-based medical selection
7. Curiosity exploration and the impact on the medical selection
Appendix:
Reconciling the biological information system, a mathematical model
Standing on the shoulders of giants: We have many to thank for our genetic learning over the last few centuries. In many ways, Charles Darwin was the first to break through with the theory of natural selection. Natural selection is nature's way of rewriting our genetic code in response to random influences and environmental effects. Even more controversial figures, like Francis Galton, contributed much to the statistical mechanics of natural selection. Galton was at the forefront of the random character of natural selection and the application of statistical theory. Galton was also well known for founding eugenics, a race theory supporting Nazi Germany and Jewish persecution. Ironically, Galton was Darwin's blood relative. More recently, scientists and authors across disciplines have provided interesting work to connect the dots between genotypes and phenotypes. They also connect the dots between genetics and epigenetics. It is encouraging that many of these thinkers come from diverse disciplines.
Author's note: I am a behavioral economist, banker, and data scientist. The article takes the tools of my trade and applies them to genomics. In the notes section is a listing of resources and people of science contributing to this genetics article. They helped me contextualize the vast genomic corpus. I appreciate standing on the shoulders of these giants... the view is amazing!
Our genetics as our superpower: As your read this article, you will notice that sometimes the word "disease" is used to describe a genetic-based condition. Alternatively, sometimes the words "non-standard genetic conditions" are used to describe a genetic-based condition. Also, sometimes those with genetic-based conditions are described as a "patient," "person," or "human." The point is, because of the universally random interaction between our genome and environment, all people have some genetic-based condition. This outcome is generated by chance as found along a bell-shaped continuum. Some people have genetic-based conditions closer to the average than others. Our phenotype is a unique and individual expression of our genome and environment.
Our uniqueness is a function of the randomly mutated genome we received from our biological parents. Our uniqueness is also a function of our day-to-day life environment, especially the environment of our developmental years. Think of this environment as the "womb lottery" we "won." In section 1, the environment is more thoroughly defined. For now, consider the environment into which we were born as having randomly selected characteristics that may randomly change. It is this uniqueness that often provides the catalyst for our individual success [ii]. Together, your genetic-based condition and environment are your unique superpower. It helps to treat it as such.
Temple Grandin is an accomplished biologist and large animal research scientist. She is also autistic. Dr. Grandin's genetic-based condition is much further from the average than most. Her words speak to the power of genetic diversity:
“What would happen if the autism gene was eliminated from the gene pool? You would have a bunch of people standing around in a cave, chatting and socializing and not getting anything done.”
- Temple Grandin, "Thinking in Pictures: My Life with Autism"
I hope you enjoy the journey as we walk through the promise, the power, and the challenges of genetic engineering.
1. Our biological information system
This graphic shows how our genes act as a dynamic blueprint. A blueprint that may be rewritten with input from the environment and chance. The genetic blueprint is very different than an endpoint blueprint to build a house. The genetic blueprint requires time and energy to unfold. The connection between biology, genomics, and physics is strong. The theory of relativity for quantum mechanics is relevant to how our genome and environment interact. Just like photons are understood through the probabilistic understanding of energy and location, biological information is understood through the probabilistic lens of genetics and environment.
In the context of system design, the biological information process is considered a balancing loop. Commonly occurring genotype/phenotype process controls have evolved to be self-balancing. The interaction between genes, organisms, and the environment keeps the biological information system in balance. By contrast, cancer is an example of a reinforcing feedback loop. Cancer cells break free of the balancing controls associated with the biological information process. Cancer cells will continue to grow until energy resources are consumed, which may result in the death of the host organism.
Game Theory and Evolution - In the appendix, we provide the mathematical construction for a minimax-based saddle point. This mathematical approach reconciles the biological information system by providing a 2 agent interactive model. The agents interacting include the random-based environmental selection process as one agent. The other agent is random-based genetic operations. This math construct uses the same game theoretical model provided for related evolutionary and social systems. This is a game theoretical approach to finding the optimal balance for both the genome and the environment.
Game theory is successfully used to describe the means by which stable outcomes are achieved in many social and biological systems. Robert Axelrod is an economist, political scientist, and game theorist from the University of Michigan. Dr. Axelrod used a game theory tournament to determine that a simple "Tit for Tat" strategy was the best approach to finding the optimal game theoretical point for many 2 agent games. The finding was startling because of its simplicity and consistency. [iii] In evolution theory, the optimal game theoretical point is known as an Evolutionary Stable Strategy or "ESS."
Tit for Tat is simple because it only has one rule. In a 2 agent game model, one agent simply follows what the other agent did. If one agent breaks the rule, they will immediately be punished. In a "Multigeneration biological information" game presented earlier, one could imagine the Tit for Tat strategy playing out between the genome and the environmental agents. A simple strategy is compelling because nature often searches for the simplest rule-based approach to resolve complex (dynamical) environmental conditions.
Defining 'Environment:' For the purposes of this article, “environment” includes the descriptive characteristics of the habitat into which individuals are born. Environmental characteristics include family, friends, government system, education system, the extent to which books are part of childhood, religious influence, health care, access to capital, access to transportation, and many others. Earlier, we describe the environment as the result of the “womb lottery” because one’s development environment occurs by luck.”
Next is the framework for a Tit-for-Tat strategy in the "Multigeneration biological information" game context. You may follow this on the biological information system graphic presented earlier:
The genome as an agent: The genome-positive (agent growth) outcomes present via gene-building RNA and the genome-negative (agent punishment) outcomes present from proteins that regulate the organism.
The environment as an agent: The environment-positive (agent growth) outcomes present via organism building via proteins and the environment-negative (agent punishment) outcomes present from proteins and RNA that regulate the genome.
Thus, the Tit for Tat game strategy provides for simple growth and punishment symbiotic mechanisms to manage the biological information flow:
Genome growth in exchange for organism growth
Genome punishment in exchange for organism punishment
These growth and punishment collaboration mechanisms propel natural selection.
Please note, the Multigeneration biological information game is considered a cooperative game because both growth and punishment may occur by each agent. Growth and punishment are a means to communicate the needs of one agent to the other agent.
The minimax saddle model provided in the appendix specifies the mathematical foundation describing the Tit-for-Tat ESS strategy.
2. The history and impact of genetic selection
The relativity between genome and environment is on display when tracking the history of selection methods. Since the mid-1800s, our ability to influence selection methods has been growing far quicker than in previous centuries. The eugenics experiment, punctuated by nazism and the Jewish persecution, was certainly a wake-up call and an on-point warning for medical selection enabled by genetic sequencing technologies. The natural insatiability of scientific and economic achievement must be balanced with the greater good of society. The Nazis showed how it was possible to go down a selection path with horrific outcomes. Medical selection is the first selection method that transforms unfit genomes to fit genomes, within the same being. Gene sequencing is transgenic. Instead of waiting for multiple generations to sort genetic fitness across biological populations, it has the potential to accelerate evolution within the same generation and within the same being. As was pointed out in the assumptions: "The ability to permanently change genomes via stem cell-based germ-line manipulation is very close. The CRISPR system is the closest to the ability to edit stem cells that will eliminate genetic conditions for an individual's heirs."
3. The promise of genetic engineering
There is great promise with medical selection. The well-intended desire to treat the most challenging genetic diseases like Huntington's Disease or Cystic Fibrosis is admirable. Genetic technologies are improving rapidly to make this promise a reality. This graphic shows the tradeoff between patient comfort and genetic information. For example:
"Medical Deselection" seeks to "treat the symptom" in the service of reducing suffering. As an intended outcome, "Patient comfort" is rated high. However, evolutionary information may be lost in the process. This occurs when the treatment enables a person to have children that may also carry whatever genetic condition they have that required treatment in the first place. In the zero-sum genetics world, carrying unfit genetic material forward to future generations is a dilutive information loss to the human genome.
"Eugenic Selection" confounded phenotype-based cultural information with genetic information. This resulted in a loss of fit genetic information that perished with eugenic program participants. Eugenic program participants included the holocaust victims from Nazi Germany. In the United States, eugenics programs were pursued in many states. One of the most infamous examples was the forced sterilization of Carrie Buck. Buck's sterilization occurred under the authority of the Sterilization Act of 1924, part of the Commonwealth of Virginia's former eugenics program. "Patient comfort" is clearly rated low, given the inhumane and forced (no choice) manner in which eugenic program participants were treated.
Moving to the upper right corner of the graphic, where both comfort and genetic information are maximized, is an admirable goal. As we will explore in the next section, medical selection choices are often complex with many uncomfortable tradeoffs.
4. Where do you draw the line?
The challenge becomes one of selection. Where do you draw the line? Today, "The Triangle of Limits" is offered as a leading selection framework. [iv] For guiding principles, the selection framework considers:
penetrance (likelihood of a particular genetic construction presenting in a person),
the degree of suffering, and
noncoerced choice.
For more extreme cases, like Huntington's Disease or Cystic Fibrosis, the selection line is generally more clear. These more extreme cases map to the lower right corner of the medical selection graphic. These more extreme cases have high penetrance and are likely to cause suffering for those with the genetic condition. Next, we explore cases where medical selection is less clear.
What about schizophrenia and related mental disorders? Some well-known thinkers or public figures had mental disorders. Such as:
Author → Jack Kerouac,
First Lady → Mary Todd Lincoln,
Nobel Laureate economist → John Nash.
The ancient greek philosophers appreciated the madness that may accompany brilliance. Aristotle said:
"No great mind ever existed without a touch of madness"
Many creative people have been diagnosed with such mental disorders. Curing them of the disorder may alter their great creative ability. It is their variation from normal thinking processes that likely provided their creative value. Cure the disease and we discourage creative achievement.
What about dyslexia? Dyslexia is a genetic-based condition [v] and impacts an estimated 15% of the total population. A massive number of people have some form of dyslexia. Some very accomplished people are known to have some form of dyslexia... people such as:
Artist and inventor → Leonardo da Vinci,
Physicist → Albert Einstein, and
Entrepreneur → Richard Branson.
Will genetic modifications for dyslexia ever make sense? Is making someone a better reader the answer? Or, perhaps, better-adapting education environments to the needs of the dyslexic is the right answer. Sally Shaywitz, MD, co-director of Yale Center for Dyslexia and Creativity said in testimony before the United States House of Representatives:
"For the student, the knowledge that he is dyslexic is empowering … [It provides him] with self-understanding and self-awareness of what he has and what he needs to do in order to succeed.”
What about Down syndrome? My experience with people with Down syndrome is they seem VERY happy. They have a sweet, loving disposition. The person with the syndrome seems like they experience little suffering. Dr. Brian Skotko is a clinical fellow in genetics at the Children’s Hospital Boston. Dr. Skotko authored a study of 3,000 Down syndrome patients and family members published in the American Journal of Medical Genetics. The study found that 99% of adults with Down syndrome responded they were happy with their lives. Lauren Potter, an actress with Down syndrome said:
“A long time ago when I was very little, I dreamed about being on stage. Some people told me I would never be able to do it, so I only paid attention to those who told me that I could.”
But what about their parents? If the parents knew ahead of time the fetus has Down syndrome does it make sense to abort the pregnancy? This happens regularly today. [vi] So it seems the "suffering" definition extends to the parents or those charged with raising a genetically non-standard child.
Beyond schizophrenia, dyslexia, and Down syndrome, there are many more non-standard genetic conditions that could be considered. This line of inquiry is meaningful for most, if not all, non-standard genetic conditions. This line of inquiry is especially meaningful for those conditions not found in the lower right-hand corner of "The Triangle of Limits."
The next three sections turn the table. Instead of focusing on those that may benefit from genetic disorder treatments, we now focus on those developing the treatments - the scientific and medical community. These sections explore the incentives and environments in which scientists and medical researchers operate. We also explore the neurobiology-based mindsets people bring to developing inventions. We provide examples of past challenges when science moved forward too fast and past challenges causing inertia and a reluctance to move science forward.
5. The choice challenge
The earlier "where do you draw the line" graphic plots genetic conditions along two of the three "triangle of limits" considerations for making medical selections. The third corner of the triangle is a "noncoerced choice" by those with the genetic condition or legally acting on behalf of those with the genetic condition. The idea is that people should act with "free will" when making their own medical selection decisions. Free will is defined as the capacity of decision agents to choose between different possible courses of action unimpeded. Free will is often noted in world religions and governance doctrines, including the U.S. Declaration of Independence. [vii] Noncoerced choice is challenging, if not impossible to achieve. As we will discuss next, the choice environment, including the participating researchers and scientists, likely has a "hand on the scale" of free will.
When choice leads to a social good: Some comfort is provided when a "noncoerced choice" is provided to make the medical selection decision. The University of Chicago Economist Steve Levitt and Yale Legal scholar John Donohue provide a study demonstrating that abortion choice has a causal relationship to crime. [less abortion choice → more crime] [viii] These researchers demonstrate that the unwanted children of parents (mostly mothers) prohibited from making an abortion choice were significantly more likely to turn to crime when they became adults. The parents' choice is a signaling mechanism. In this case, crime is considered a social bad and an indication of a lack of environmental fitness. Thus their abortion choice signals an anticipated misalignment between the unborn child and their developmental environment. In the context of abortion and crime, Levitt and Donohue's evidence shows, on average, the parents' choice is correct.
Choice challenges: But even choice has its challenges. Behavioral scientists appreciate even choice has a way of being greatly influenced by the environment. Behavioral economist and Nobel laureate Richard Thaler said:
"People have a strong tendency to go along with the status quo or default option.... Just as no building lacks an architecture, so no choice lacks a context.
Thaler suggests there is no such thing as "noncoercion" when it comes to choice. Our default choice environment is always a "coercing" part of the choice. For example, there is a cultural, not a medical, reason why female fetuses are more likely to be aborted in countries like China and India.
Even with a selection framework, the choice to abort or edit genomes is complex and involves uncomfortable tradeoffs. The environment in which the patient makes a "noncoerced choice" for gene-altering interventions, by definition, impacts their decision. The environment includes those supplying gene-altering services. The point is not to run from this reality or even try to reduce it. The coercing part of the environment is a reality and carries many benefits. If you were making a decision about gene-altering therapy, wouldn't you want input from knowledgeable researchers? Of course. However, all participants, by definition, have a part of their self-interest not aligned with the patient's self-interest. Support for those in need of gene-altering interventions should include an independent decision process to help the patient make the best decision. The idea is to provide an independent, patient-as-agent decision process that:
Helps curate the good information from the system's participating agents but
Minimizes the influence of the participating agent's unaligned self-interests, and
Helps the patient properly weigh all criteria and alternatives.
It is not just the potential misalignment of agent interests causing best-decision concerns. Also concerning is the patient's ability to objectively process information. Even the best decision-makers are influenced by the pain and trauma associated with unwanted conditions. "Non-coerced choice" should NOT be thought of as the third pillar of an intertwined medical selection decision foundation. Instead, we should think of penetrance, suffering, and all the environmental information as decision INPUTS to a well-defined and independent patient's decision PROCESS. The OUTCOME is a decision recommendation necessary for making the optimal decision.
6. The growth and implications of gene-based medical selection
That which drives us to achieve is that which may make us less sensitive to the risks in the service of that achievement. Our relentless and insatiable drive is part of our human nature. "Human Insatiability" is part of our DNA, including the DNA of those charged with genetic engineering. [ix] I find it troubling that the genetic condition of human insatiability that makes us susceptible to bad behavior is resident in the genetic researchers that could be susceptible to the bad behavior's influence.
Thorstein Veblen was an economist and sociologist. His pioneering work included integrating behavioral psychology and economics. Veblen said:
"Invention is the mother of necessity"
Once people can do something (like an invention) their drive to improve upon the invention becomes like an insatiable necessity.
Music machines and smartphones - invention as an insatiable necessity example:
Over one hundred years ago, music machines were a fringe invention. It was not necessary for anyone. Once music machines became mainstream, they quickly became a necessity. As technology has evolved, the manifestations of music machines have evolved. Most other inventions share this "necessity" tipping point. The smartphone is certainly another example. Twenty years ago, the smartphone was on the fringe, a cool curiosity. Today the smartphone is considered a necessity for most of us.
In the following music machine graphic, reducing entropy is synonymous with increased market value and reaching a tipping point. We chart the "path of necessity."
Those inventors, entrepreneurs, and the market demand considered these inventions a necessity. It was their relentless and insatiable drive that enabled their invention's development. This insatiable need for invention is the source of human achievements. It is what makes people special. It is why humans have come to dominate the earth.
The science of genetics is no different. The allure of scientific and medical achievement is powerful. For many in the scientific and medical community, it is an insatiable necessity. However, genetics is potentially a source of suffering for those inappropriately involved.
Jesse Gelsinger provides a cautionary tale for gene therapy and medical selection. In 1999, seventeen-year-old Gelsinger died after receiving an inappropriate gene therapy via a common virus. Gelsinger had a genetic disease called "ornithine transcarbamylase deficiency." The Food and Drug Administration (FDA) investigated Gelsinger's death. They concluded that the scientists involved in the trial, including the co-investigator James Wilson (Director of the Institute for Human Gene Therapy), broke several rules of conduct. It was also shown that some of the scientists stood to benefit economically from the gene therapy's success.
This begs the question: "What are the incentives and the environment enabling genetic scientists' and entrepreneurs' achievements?" All the while: "How do we simultaneously ensure society is not taking undue risks?" Then, a second-order question is: "What are undue risks?" Any new treatment needs to be tested. If someone is already likely to die from a genetic disorder is the undue risk standard "no chance of harm?" or is the undue risk standard "no chance of harm any greater than their current risk of harm?" As in the prior medical selection graphic, this is part of why it is easier to draw the line for selecting diseases in the lower right-hand corner. The chance of harm is already very high.
A driverless car example - This genetics "undue risk" question has parallels with driverless car testing and safety expectations. Globally, over 1 million people per year die in auto accidents. Plus, many more people are injured every year. Driver error is the main cause of death. Not long ago, a single pedestrian died while a driverless car was being tested. [x] It raised an uproar and concern about driverless cars. This is a case in point for the "What are undue risks?" question. As is the parallel question for genetics, should the driverless car harm standard be "NO ONE dies in driverless car accidents?" Or a more reasonable standard would be "at least LESS THAN the MILLION people that are currently dying in auto accidents." Economists believe that decisions should be made at the margin. So as long as marginal benefit exceeds marginal costs, a decision is economically rational. Thus, an economist believes the undue risk standard should be closer to not exceeding the number of deaths before driverless cars are implemented.
7. Curiosity exploration and the impact on the medical selection
This still begs the question "why?" Why are scientists and medical researchers naturally predisposed to the "insatiable necessity" of genetic engineering and relevant to medical selection? In a word - the answer is "curiosity." Scientists and medical researchers are incredibly curious and are generally encouraged to express their curiosity. Curiosity is an amazing human characteristic. As we will explore next, curiosity is also subject to our addictive physiology.
Curiosity is based on emotion. It is often called the "smart emotion" because it provides an emotion-based signal encouraging knowledge accumulation. This feeling also signals whether we "feel" we are learning and growing our understanding. Curiosity provides an emotion-based signal that motivates us to move forward productively and pursue our curiosity exploration.
Our brains attach biochemical "tags" to sensory information. Those tags are formally known as "neurotransmitters." The tags are additional information that may render an emotional response.
The anatomy of curiosity - a neurotransmitter example:
A lion example: If a lion is quickly bearing down on you, the sensory information will help you identify essential information about the lion - such as its speed, its size, its color, and the estimated length of time until it reaches you. The neurotransmitter tags attached to that sensory information provide very fast response signals. Those signals include fear, a flight response, and even hormones triggering peak physical performance for your escape. You will not remember a decision to flee the lion, it just happens. The neurotransmitter tags are provided in the service of your gene's survival. They are ancient algorithms, having been fine-tuned by millennia of evolution. You may thank your long line of ancestors, all of who were able to "escape the lion" and contribute fine-tuned DNA to your genome.
There are many kinds of neurotransmitters that provide for different emotions. The four primary curiosity-oriented neurotransmitters are dopamine, acetylcholine, oxytocin, and serotonin. These neurotransmitters combine to provide for and enhance our curiosity. [xi]
Dopamine and Acetylcholine are reward neurotransmitters. Acetylcholine relates to the learning process and dopamine relates to the learning outcome. Acetylcholine rewards by providing calm and contentment to enable the learning process. Dopamine-based rewards are activated as an outcome of successful learning and when we receive positive feedback for that learning.
Oxytocin is our tribal neurotransmitter. Oxytocin causes a feeling of love as people connect. Oxytocin is both a neurotransmitter and a neurohormone. We desire to share our curiosity and learnings with others.
Serotonin provides a general sense of well-being. Serotonin stabilizes our mood and is present when we pursue our curiosity.
As shown in the next graphic, curiosity is the result of a reinforcing feedback loop. In section 1, we discussed the biological information system loop in relation to how biological information is subject to balancing feedback loops. A well-behaved biological information system is the essence of human life. An out-of-balance biological information system may become cancer.
Similarly, curiosity-reinforcing feedback loops can be very positive but also have the potential to get out of control. Habits and positive reinforcement will enhance the curiosity feedback loop. The reinforcing nature of the feedback loop suggests it is like a self-fulfilling prophecy. Once initiated, the curiosity feedback loop often becomes a habit. Habits have a way of sticking. Dopamine tends to be the "out of control" neurotransmitter. Some reward is good. It is what helps steer researchers toward the best research solutions. Too much reward may become like an addiction. Over-weighting dopamine may cause the researcher to act like a "hammer looking for a nail." Without the balance of acetylcholine, a scientist or medical researcher runs the risk of having dysfunctional addictive episodes because of curiosity. Habits can be good or bad. It is important to encourage good habits and minimize exposure to bad habits - our brain is listening! From a personality and neurobiology standpoint:
Acetylcholine is described as an "Introvert's pathway," whereas
Dopamine is described as an "Extrovert's pathway."
The challenge is, none of us have windows into our brain where we can see the neurotransmitter-based curiosity weights of dopamine or acetylcholine. Thus, the question becomes, "What kind of qualified decision-maker is best suited to manage medical selection risks?" Given personality predisposition is a balance between these pathways, a scientist or medical researcher will be better suited to make medical selection decisions if their personality presents as an introvert.
Traveling the curiosity feedback loop:
1. It starts with learning, generally via practicing and doing tasks. The mood-stabilizing serotonin neurotransmitter enables the learning discipline.
2. Improvement and mental efficiency then occur as tasks are habituated. The learning process is rewarded by the acetylcholine neurotransmitter. Improvement generally leads to positive feedback from those benefitting from the task execution.
3. That positive feedback is related to the oxytocin neurotransmitter. Positive feedback stimulates reward impulses via the dopamine neurotransmitter.
4. Dopamine causes the good feeling received by a job well done. This reward-based neurotransmitter information tag encourages more learning.
... and the loop continues and leads to continuous improvement and enhancing our smartness.
Conclusion
Gene therapy and genetic engineering hold great promise. They are also potentially dangerous tools. In the wrong hands or inappropriately used, these tools can become dangerous weapons. As one walks back from the more obvious medical selections, including diseases such as Huntington's or Cystic Fibrosis, the selection framework becomes less clear. The environment and culture in which a medical selection choice is made are likely to impact that choice. The incentives and environments available to scientists and medical researchers matter. A breakthrough in a significant disease can make someone's career and produce significant revenue. As in the case of Jesse Gelsinger, those same incentives and environments may create risk and undue harm. Curiosity is an incredible human characteristic. It can also lead to an "insatiable necessity" potentially leading to suboptimal outcomes for scientists or medical researchers. The growth of genetic engineering needs to carefully walk the line between encouraging scientific and entrepreneurial achievement while minimizing undue risk.
Looking into the future - earlier, we presented nature and our genotype as being represented by a bell shaped curve or normal distribution. The appendix demonstrates the von Neumann-based saddle point mathematics to understand our nature and nurture interactions, again, characterized by normality. In nature, normal distributions are the standard, as long as nature is not manipulated by human policies.
Once humans intervene, the statistical outcomes become chaotic. Distributions start to skew and distribution tails become fatter. All this means, with genome-based intervention, the stability of the human genome WILL decline. Just look at the banking system resulting from the financial crisis. We thought our policies were so smart, then the global financial system collapsed in a fiery mess. So the challenge is, as humans scientifically learn to manipulate genomes and germ-lines, the outcomes of those manipulations will be unpredictable and volatile. Clearly, caution and restraint are necessary as humanity walks into the less stable unknown.
Appendix - Reconciling the biological information system, a mathematical model
In section 1, we discuss the biological information system as needing to resolve the individual to the environment.
The genome: Every person hosts a randomly mutated genome. The genome is a function of their parents but contains mutations as per natural selection.
The environment: Provides a randomly constructed environment. An environment impacted by family, educational opportunities, political systems, etc.
The question becomes one of “how.” How do the environment and individual reconcile these two randomly characterized inputs to the biological information system?
The minimax “saddle point” construct is provided as the means to reconcile these two inputs to the biological information system. In mathematics, a saddle point of a minimax function is a point on a surface where the slopes (derivatives) in orthogonal directions are all zero (a critical point), but which is not a local extremum of the function. Think of this model as a mapping model for every person on the planet. Each person is a dual function of their randomly assigned environments and randomly assigned genetic mutations. The next model locates each person on the minimax surface map. The probability a person will be located closer to the saddlepoint is a function of their randomly assigned genome mutations and their randomly assigned environment. As genetic engineering improves, so will the ability to move someone closer to the saddle point.
The minimax was first offered by John von Neumann as a means to solve two-party zero-sum games. [xii] For example, like a game of cards where each person has:
A full understanding of the rules,
An equal understanding of the probabilities involving the risk of losing and the rewards of winning
An equal desire to maximize their chances of winning and minimize their chance of losing.
The parties are independent. They have no ability or incentives to collude.
The saddle point is the point where the 2 players optimize their outcomes.
Regarding evolutionary biology, the saddle point is also considered an evolutionarily stable strategy (“ESS”). An ESS is a strategy (or set of strategies) that is impermeable when adopted by a population in adaptation to a specific environment, that is to say, it cannot be displaced by an alternative strategy (or set of strategies) that may be novel or initially rare. [xiii]
Richard Dawkins relates our genetic destiny via natural selection as a way to achieve an ESS. [xiv] Dawkins refers to an individual as a “survival machine” in the service of delivering its genes from one generation to the next. Dawkins famously quips:
“The chicken is only an egg’s way for making another egg.”
In the context of a two-party zero-sum game, the environment in which we live is one party and the genomes of all the people in the environment are the other party.
Biological Information system construction
A simple 2 variable system is used:
Figure: Saddle 1
z - the curved plane space where an individual human will find themselves. It is assumed all currently living people (approximately 8 billion) will be found on or near this curved space. As an example, In Figure: Saddle 1 we show 3 "people" out of the total population located on or around the map by observations 1,2, and n.
x - the variance of an individual's genome from the genome of the population.
y - the variance of the environment needed to support human genomes in aggregate.
Figure: Saddle 2
Assumptions for using the saddle point construction.
The individuals' genome and environment are separate parties and unable to collude in the short term. Effectively, today's environment is fixed. There may be an available set of technologies that enhance human-environmental compatibility. People may not always be fully aware of the available environmental technology choice set. But whatever they are, the available environmental technology choice set is fixed at the current time.
Genome variance is a minimization functional input to z. The more an individual's genome is similar to the average of all human genomes, then the individual is considered to be approaching the minimum saddle point. Via natural selection, it is assumed individual genomes are mean reverting, but via naturally occurring mutations, some individual genomes will vary from the mean.
Environmental comparability to the human genome is a maximization functional input to z. The more the environment is compatible with the human genome is considered to approach the maximum saddle point.
Individual humans have no ability to influence the environmental system available for their genome over a shorter period of time. This shorter period of time is the decision window for humans to optimize their ESS.
Humanity, via its science and technology, does have the ability to influence the environmental system over a longer period of time. Thus, an ESS likely evolves over time and for future generations.
In general, the further an individual's genome is from the maximum environment point, the more challenging it is for the individual to survive and transmit his or her genes to the next generation.
There is a “zone of compatibility” where individuals are most likely to survive AND transmit their genetic code to the next generation. This would be the first lined zone above and below the saddle point dot in the graphic. (The blue and orange stripes outlined in Figure: Saddle 2). In this graphic, it is assumed the great majority of people born today fall in the zone of compatibility. The question then is how do we expand the zone of compatibility to accommodate even more people? The goal is the dual benefit of:
More people living comfortable, productive lives, while
Improving the transmission of “fit to the environment” genetic information via both natural and medical selection.
Over time, human technology, including genetic engineering, will expand the zone of compatibility space. Thus, more people will fall into the zone of compatibility. Mathematically, this is like a parameter (α & β) expanding over time.
In the pre-genetic engineering world - if:
α = 1
β = 1
As genetic engineering technology and medical selection frameworks improve,
then α < 1
As humans' ability to adapt to their environment through better education, better economic systems, better health care, etc,
then β < 1
When:
α < 1 and/or
β < 1
the zone of compatibility expands. The saddle point space becomes flatter and wider for the same population. A wider zone of compatibility suggests the tales of the x and y functions are narrower. Population kurtosis is lower, (<1) as α < 1 and/or β <1.
In the Figure: Saddle 3 graphic, we show a comparison between a unitary saddle point (α = 1, β = 1) and an expanded saddle point (α = .5, β = .5) leading to a wider zone of compatibility. Please notice the environment and genome space scale has been expanded to (-15,15). This is simply to better show the expanded zone of compatibility. Visually, you can tell the zone of compatibility is a wider space. This means more people are found in a wider zone of compatibility.
The intent of this saddle point construct is to provide a method by which the external environment and the internal human genome may be reconciled in a manner that fits the way the environment and genome interact. The idea is that there are certainly tradeoffs that need to be considered as we discussed in section 4. In order to validate and operationalize this model, the next step is to:
model the actual number of people that exist in the saddle point bands.
model the forecasted impact of technology change to where people fall on or around the curved saddlepoint space.
Figure: Saddle 3
Notes
Resources and authors contributing to this genetics article:
Darwin, The Origin Of Species, 1859
Dobzhansky, Genetics and the Origin of Species, 1937
von Neumann, Morgenstern, Theory of Games and Economic Behavior, 1944
Dawkins, The Selfish Gene, 1976
Grandin, Thinking in Pictures: My Life with Autism, 1995
Bolte Taylor, My Stroke of Insight, 2006
Axelrod, Evolution of Cooperation, 2006
Thaler, Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness, 2008
Mukherjee, The Gene, An Intimate History, 2016
Doudna, Sternberg, A Crack in Creation: Gene Editing and the Unthinkable Power to Control Evolution, 2017
Sapolsky, Behave: The Biology Of Humans At Our Best And Worst, 2017
Rovelli, Helgoland, Making Sense of the Quantum Revolution, 2020
Hiesinger, The Self-Assembling Brain: How Neural Networks Grow Smarter, 2021
Lindsay, Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain, 2021
[i] For more context on genetic engineering techniques, please see:
Doudna, How CRISPR lets us edit our DNA, TED Podcast, 2015
Doudna, Sternberg, A Crack in Creation: Gene Editing and the Unthinkable Power to Control Evolution, 2017
Xu, Li, CRISPR-Cas systems: Overview, innovations and applications in human disease research and gene therapy, National Library of Medicine, National Center for Biotechnical Information, 2020
As genetic engineering techniques are rapidly advancing, a current literature review would also help:
[ii] Success means many things to many people. In this article, success is defined in two ways:
(1) The ancient stoic Seneca defined luck in terms of success. He said, "Luck is where preparation and opportunity meet." This aligns well with this article as preparation and opportunity are functions of environment, genetics, and chance. See our article for more context:
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Hulett, Success Pillars - a life journey foundation, The Curiosity Vine, 2021
(2) With a strictly physics-based definition, success in life may be defined as "Fighting Entropy." This aligns well with section 7 of this article. In the following article citation, we make the case - given our life's purpose includes to "fight entropy" - that using tools and techniques to both measure and reduce entropy is critical to increasing our life's quality.
Hulett, Fight Entropy: Living your best life by using the practical physics of time, The Curiosity Vine, 2022
[iii] Robert Axelrod, Evolution of Cooperation, 2006
[iv] The "triangle of considerations" or "triangle of limits" is described in Dr. Mukherjee's book. To plot the different diseases or genetic conditions, a number of public sources were searched, including those provided by the U.S. National Institute of Health. Just plotting them on the penetrance and suffering dimensions was challenging. The challenge is that disease or genetic conditions are likely to be continuous along many dimensions. Picking two dimensions and drawing a circle around a genetic condition that is better described as "it depends" is a challenge. To some degree, the lower right, more extreme diseases are easier to plot. Genetic conditions like autism have a wider "it depends" circle.
Mukherjee, The Gene, An Intimate History, 2016
[v] Dyslexia, like most non-standard conditions, have a basis in genetic variance.
Schumacher, Hoffmann, Schmäl, Schulte‐Körne, Nöthen, Genetics of dyslexia: the evolving landscape, Journal of Medical Genetics, 2007
[vi] Research suggests that between 61-93 percent of pregnancies diagnosed with Down syndrome are terminated with a weighted mean termination rate of 67 percent.
Editors, Down Syndrome and Social Capital: Assessing the Costs of Selective Abortion, Social Capital Project, 2022
[vii] Free will is generally offered as motivation for the American colonists to secede from the British Empire:
"We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness."
Editors, Declaration of Independence: A Transcription, United States National Archives, 1776
[viii] Next, two citations are provided for establishing the causal link between abortion and crime. The first citation is to the initial paper, published in 2001. The second paper is an out-of-time validation of the original hypothesis.
Levitt, Donohue, The Impact Of Legalized Abortion On Crime, The Quarterly Journal Of Economics, 2001
Levitt, Donohue, The Impact of Legalized Abortion on Crime over the Last Two Decades, National Bureau of Economic Research, 2019
Steve Levitt points out that this research conclusion should help guide policy decisions regarding crime and the power of "unwantedness" in our social environment. His research conclusion is not an advocation one way or the other for more or less stringent abortion laws.
Dubner, Freakonomics interview with Stephen Levitt and John Donohue, Abortion and Crime, Revisited, Episode 384, 2019
I discuss this abortion and crime research in the context of prison privatization incentives in the following article:
Hulett, Raising a loved child and the effect of abortion, crime, and prisons, The Curiosity Vine, 2022
[ix] MIT professor David Autor defines the "insatiable principle" in his article about job automation:
Autor, Why Are There Still So Many Jobs? The History and Future of Workplace Automation, JOURNAL OF ECONOMIC PERSPECTIVES, VOL. 29, NO. 3, SUMMER 2015
Robert Sapolsky provides an in-depth review of the mesolimbic/mesocortical dopamine system in his book Behave. This helps us understand why people are generally insatiable as per Autor’s insatiable principle.
Sapolsky, Behave: The Biology Of Humans At Our Best And Worst, 2017
One of my favorite songs is "Ain’t no rest for the wicked" by Cage The Elephant. The “wicked” is a metaphor for the insatiable humans:
“Oh no, I can't slow down, I can't hold back
Though you know, I wish I could
Oh no there ain't no rest for the wicked
Until we close our eyes for good”
[x] Greenemeier, Uber Self-Driving Car Fatality Reveals the Technology’s Blind Spots, Scientific American, 2018
[xi] Berry, Han, What are neurotransmitters?, Medical News Today, 2022
In terms of learning and curiosity, the reward neurotransmitters focus on different parts of the creative process. We explore introversion (acetylcholine) and extroversion (dopamine) in the following article:
Hulett, Creativity - For Both Introverts and Extroverts, The Curiosity Vine, 2022
[xii] Kjeldsen, John von Neumann’s Conception of the Minimax Theorem: A Journey Through Different Mathematical Contexts, Archive for History of Exact Sciences, 2001
[xiii] Smith, Game Theory and The Evolution of Fighting,1972
[xiv] Dawkins, The Selfish Gene, 1976
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