How to Win at Angry Birds

humanOS presents how to win at Angry Birds, the ancestral paradigm for therapeutic revolution. This course was created by me, Doctor Josh Turknet. I am a board certified neurologist, clinical researcher, author and current President of Physicians for Ancestral Health. Our reviewer for this course was Doctor Tommy Wood. Doctor Wood is an assistant professor at the University of Washington in the Department of Neonatology, as well as the cofounder of the British Society for Lifestyle Medicine. This presentation began as an attempt to answer 2 important questions. First, why have we failed so miserably? And second, why do we keep repeating the same mistakes over and over again? Why do we continue the same approach in the face of overwhelming evidence that it's not working? It was roughly 20 years ago that I decided to direct my interest in the neurosciences towards a career as a neurologist. One of my major reasons for doing so was because I thought, as did so many others, that we were on the cusp of a revolution in our ability to treat diseases of the nervous system based on the prevailing wisdom. I expected there to be major advances in, if not cures, for, many of the most devastating illnesses that neurologists face. That was the promise. Now let's look at the reality. Here is a timeline of the major pharmacological breakthroughs in the treatment of the most prominent diseases of the nervous system. Along with the year those breakthroughs were made, these are the dates of discovery for a treatment that to this day remains a gold standard. Meaning that it has yet to be replaced by a superior treatment. Our first breakthroughs occur around the turn of the twentieth century with the discovery of aspirin for stroke prevention and phenobarbital for the treatment of generalized epilepsy. Around the mid twentieth, century we add a couple of new treatments for Mya senior Gravis along with the discovery of tegretol for partial epilepsy and levodopa for Parkinson's disease. This is then followed by a drought in pharmacological breakthroughs, or what we might refer to as a therapeutic winter until the discovery of Sumatriptan in 1991 for the treatment of migraines, though there are some who would debate whether, given its drawbacks, it should be considered a net benefit or net harm. The ensuing period from 1991 up to the present day has seen no further pharmacological breakthroughs. You may note the absence in this timeline of some of the other major common diseases of the nervous system, like Alzheimer's, multiple sclerosis, and diabetic neuropathy. Unfortunately, this unsettling trend is not just limited to neurological diseases. If we review the history of pharmacological breakthroughs for the major diseases of other organ systems, we find a similar story.

We begin with the discovery of things like digitalis for heart failure and insulin for diabetes 2, treatments that are still commonly used in the mid twentieth. Century we add the first antipsychotic for schizophrenia, the first antidepressant for depression, the first oral antidiabetic agent and the first drug for the treatment of high blood pressure. And then after 1970 we have another therapeutic winter while many new drugs have come out during this period of time. None of them are significantly more effective than the existing options and thus do not qualify as pharmacological breakthroughs. And again here note the absence of any pharmacological breakthroughs for the treatment of obesity, despite the enormity of the problem and the amount of resources that have been devoted to finding a pharmacological treatment. This means that for the vast majority of major health conditions that impact humans. The ones that have placed our healthcare system on the verge of collapse, we've essentially made no significant pharmacological advances in the past 50 years. This isn't because our existing treatments work so well that they can't be improved upon, but because we haven't been able to find anything better. One reason I decided to direct my interest in neuroscience into a career in neurology was because it was widely believed at the time I graduated medical school that we were on the verge of a therapeutic revolution. I vividly remember, as a fourth year medical student asking a prominent Alzheimer's researcher at my medical school how long he thought it'd be till we had a cure for the disease. After pausing for a moment, he said 10 years. Again, this was in line with the prevailing sentiment at the time. The reality is it is now 20 years later and we've made no progress. Not only do we not have a cure, we don't even have a treatment that is incrementally better than what was available at the time he made this prediction. In fact, as you may have seen in some recent headlines, the picture here is now considered so bleak that many drug companies are pulling out of Alzheimer's Research altogether. So clearly we were off in our estimates. And if we continue with this base, perhaps a more realistic prediction for finding a drug cure for Alzheimer's might be the year 4237 Or maybe never. All of this, of course, comes in the face of incredible progress in the field of neuroscience itself. In our understanding of the brain and the mechanisms of disease, along with a massive investment of resources into the search for new treatments, to say this failure has been surprising and unexpected is a huge understatement. And all of this begs the question, what on Earth is going on?

Our failure to make meaningful progress in the realm of medical therapeutics over the last 50 years forces us to confront some difficult realities, and it should lead to a search for explanations. At best, we can say that maybe we were just way off in our time frame, that perhaps we were overly optimistic in our projections, and that finding drug solutions to the diseases of our times is just much harder than we thought. On the other hand, we must also consider the possibility. That our approach, or our guiding paradigm for finding new treatments is fundamentally flawed.

That the reason we failed to find the solutions we're after is because we've been looking for them in the wrong place. And that brings us to the parable of Angry Birds. Imagine an iPhone lands on an alien planet and it's loaded up with the game Angry Birds. Now these aliens don't know iPhones. They don't have computers. And they don't have any games where you hurl birds at Fort building pigs with a slingshot. But they're a curious and competitive species, and they like the game, so they decide to hold a competition. They split into two teams and plan to meet back in a month to face off in the Angry Birds Championships to crown one team champion.

The first team, we'll call them team game level, takes the approach that most of us would, which is to get really good at playing the game. At figuring out the game mechanics, mastering the controls, and so forth, Team two, who will refer to as team source code, opts for a different strategy. Instead of learning to play the game, they decide instead to take the game apart. Believing this will allow them to gain a deeper understanding of how the game works, which they assume will provide a big leg up on their competition.

So they start dissecting how the game is constructed, peeling it away layer by layer. First they discovered that the game is written in programming language, and then beneath that there exists machine language that's written in binary code and that specifies the moment to moment state of transistors in the iphone's processors. At this point, team source code is pretty pleased with themselves. They've cracked the game's code and have found what surely seems like a secret advantage over team game level. The game, Angry Birds, is just an illusion created by the machine language, and so their plan for winning. Is to alter the machine language in real time.

So to review team game level prepares for the competition by playing the game over and over, while team source code prepares by first taking the game apart to reveal how it really works. And their plan is to win by manipulating the machine language in real time. One month passes and the teams meet up for the championships, and as you've probably predicted, it's a massacre team game level having logged hundreds of hours of practice.

Clears one level after the next and posts an impressive score. Team source code, on the other hand, can't even complete a single round without the game crashing. And this is, of course, because there isn't a computer scientist alive who could do such a thing. Nor is there one who could do so much as look at the machine language for any game or software and have any idea whatsoever what the software it codes for will do. Even for the coders who wrote the game Angry Birds, their best strategy for winning the game would be to play it here at Human OS, we believe that number one, an evolutionary perspective is essential for understanding the foundations of human health, and that it is impossible to understand the reasons we thrive or the reasons we get sick without the context of our evolutionary history and number two.

That the fundamental driver of the chronic diseases of modern times are mismatches between the natural ancestral human habitat and our present one. By extension, it logically follows that reducing mismatch is the best strategy for preventing chronic disease. Earlier, I recounted our alarming failure over the past half century in making any meaningful pharmacological breakthroughs in the chronic diseases of our time. What I'm arguing for here. And what this parable is intended to illustrate is that the reasons we failed in finding the treatments we so desperately need is because we've been using team source code strategy for improving health and fighting chronic disease. We've been trying to manipulate the source code when instead we should have been learning how to play the game.

And this is the reason why our incredible advances in our understanding of biology and our massive investment in biological research. Has not translated into any meaningful therapeutic progress. On the other hand, the ancestral health therapeutic paradigm utilizes team game level strategy, which is to learn how to win by playing the game. Prioritizing the game level approach is how we integrate an evolutionary perspective into promoting health and into developing and evaluating new treatments. The concept of environmental mismatch is not only relevant to our understanding of the root causes of disease. It is also relevant with respect to the therapies we choose to how we intervene in human biology in ways that promote health and treat illness.

Because if we neglect to apply an evolutionary perspective and the concept of mismatch to therapeutics as well, we not only introduce all the same hazards and risks as the mismatched environments that led to the very diseases we're treating. But we also overlook the most powerful levers we have for influencing our own health. On the other hand, if we can collectively embrace this new paradigm, we have the opportunity, given our present level of knowledge and technological sophistication, to usher in a revolution in human health and medical therapies, to finally leverage the tools and methods of science so that our treatments and our level of thriving can catch up with other areas of human progress.

So let's explore this distinction between game-level interventions and source code interventions a bit further, particularly as it pertains to therapeutics, in order to further understand the difference between evolutionarily familiar and evolutionarily novel health interventions. Another term for game-level interventions would be naturalistic or evolutionarily familiar with respect to the nature of the intervention. Along with how and where it impacts a biological system, in this case, the human body. By definition, all of the evolutionary forces that have shaped our biology and Physiology are at the game level.

In other words, these forces represent the environmental inputs to which the human body has been responding and adapting to throughout the history of our species. So you can also think of game-level actions as encompassing all of the possible moves that we can make in the game of life. Source code interventions, on the other hand, are evolutionarily novel, representing forces that have not shaped our biology and Physiology and thus to which we have no specific adaptations to. This distinction leads to some very important differences between these two kinds of interventions, first, since they are evolutionarily familiar.

Game level interventions are inherently safer and much less likely to crash the system because they engage the regulatory mechanisms that have evolved over millions of years in response to them. Source code interventions, being evolutionarily novel, are also inherently riskier because they bypass evolved regulatory mechanisms. On some level, we recognize this to be true. We require extensive safety testing of synthetic pharmaceuticals for this very reason. But we don't have similar safety concerns about game level interventions. Like exercise, game level interventions are also inherently more powerful because their locus of action is upstream in the physiological cascade and therefore influence the activity of many pathways.

On the other hand, source code interventions are inherently weaker because their locus of action relative to game level interventions is downstream and their scope of influence is narrower. Game level interventions are thus better suited for intervening in complex adaptive systems like the human body, where the range of effects of source code interventions cannot be predicted. In fact, even with complete knowledge and understanding of human biology, which we are still very far from, we'd still be unable to predict the outcome of source code interventions similar to fixing a typo in the games code. Source code interventions are most appropriate for conditions that are clearly caused by a single identifiable factor, such as the meningococcus bacteria.

That's a cause of bacterial meningitis, and it is no coincidence that essentially all major progress in pharmacological treatment has been in the treatment of acute binary single factor conditions. So to review the differences between game level interventions and source code interventions, game level interventions are evolutionarily familiar. While source code interventions are evolutionarily novel, game level interventions are inherently safer because they engage evolved regulatory mechanisms. Source code interventions are inherently riskier because they bypass evolved regulatory mechanisms.

Game level interventions locus of action is upstream, which gives them a much greater scope of influence. The locus of action for source code interventions is downstream. Giving them a narrower scope of influence. Game level interventions are best suited for intervening in complex adaptive systems, whereas source code interventions are better suited for intervening in complicated systems where the effects of that intervention are predictable. This distinction also matters in terms of how we advance therapeutic knowledge, or the proper way we would go about figuring out whether a game level intervention or a source code intervention works or not.

The present gold standard for medical interventions is the randomized controlled trial. The problem here is that randomized controlled trials are designed to evaluate static, discontinuous interventions that impact a single variable. In other words, randomized controlled trials are an appropriate way to determine the effectiveness of source code interventions. Game level interventions, however, are dynamic and continuous by nature, impacting multiple variables simultaneously. The randomized controlled trial is thus not the right way to evaluate them. As mentioned, they are designed to evaluate interventions of a very different sort.

To help in understanding why randomized controlled trials are inappropriate here, imagine a baseball player forced to learn how to throw a curveball by adjusting 1 variable at a time. Not only would this approach to learning to throw a curveball be extraordinarily time consuming and tedious. It would virtually assure that he or she would never arrive at the optimal solution as it can't assess the contextual dependencies of all the variables. And this matters because one reason why game level interventions are under researched and underutilized in healthcare today is because we do not have a research ecosystem for advancing our understanding of how to best use them.

Furthermore, we created a criteria for validating their use, the randomized controlled trial that is impossible for them to meet. It's often said that the reason diet and lifestyle interventions aren't more widely used is because we need more trials, specifically more randomized control trials, since this has become the de facto standard for validating in new therapy. But the very reason we don't have more randomized control trials for them is because they are the wrong tool for the job.

We've created an impossible standard, the only way to advance game level interventions in modern medicine according to existing criteria. Is to conduct randomized controlled trials on them. Yet by design, randomized controlled trials can't advance our knowledge of game level interventions. So how do we learn to play the game, in this case the game of help? Game level knowledge in any complex adaptive system is acquired by trial and error by playing the game as it's designed. That means we make a move, assess the outcome of that move based on the feedback we receive, modify our approach, and make another move.

The truth is, there are many kinds of knowledge we can acquire that don't require randomized controlled trials. In fact, almost all of the knowledge that you currently possess, including things that you consider unassailable truths about the world, were acquired through this virtuous feedback cycle. For example, we all accept that walking off a Cliff is bad for our health, and yet we've never done a randomized trial to prove that this is true.

And if someone were to argue that, we couldn't make this claim without a randomized trial. Or that suggesting that walking off a Cliff was bad for your health wasn't based on nothing but flimsy anecdotal evidence. We wouldn't take them seriously. We know this should be true for other reasons, from our knowledge of basic principles like gravity and the forces that a human body can withstand, and our prior personal experience and so on.

The point being, there are many forms of knowledge that we can construct with confidence without the need for a randomized trial. Another important implication is that understanding the mechanisms of how an intervention works is not required to utilize that intervention. In fact, not only do you not need to know why a game level intervention works at a mechanistic level in order to use it effectively, but knowing how it works rarely improves gameplay. For example, a pitcher can throw a curveball with no explicit knowledge of the physics involved. And furthermore. Having explicit knowledge of the physics involved doesn't really help to throw a better curveball.

Last I checked, there were no physics professors in Major League Baseball or back to our parable. You needn't know anything about how the game works to win the Angry Birds Championship, as team source code learned, and yet our prevailing approach to finding health interventions over the past half century. Has been to start by identifying the mechanisms of disease based on the idea that this is how we will ultimately win the game. So we have different forms of knowledge, game level and source code, both of which are valuable, both which have their own particular domain of application of where they are most useful. .

But presently almost all of our research dollars and efforts are being spent towards acquiring the wrong kind of knowledge for how to treat chronic disease.

Earlier I said we would attempt to answer 2 questions. The first was why we failed so spectacularly in finding effective pharmacologic treatments to the health problems of our time, and thus far I've suggested that the answer to that lies in our myopic focus on source code interventions rather than in learning how to play the game. The second question was why we haven't changed our approach in spite of this massive failure. Why we've continued to double down on the Search for Source Code interventions in spite of their disappointing track record, rather than rethinking our underlying strategy. There are likely several reasons why, but one is just because, like the members of Team Source Code, we find reductionism inherently seductive.

Because it's a product of science and reason, and because it's a kind of knowledge that's initially hidden from us, it feels like we've peeled back the curtain to discover something deeper. Something more true and most importantly, more useful. Like Dorothy and her friends, it feels like we've found the man behind the curtain, as I've hopefully illustrated thus far, that couldn't be further from the truth. Yet it's hard to let go of this intuition. It feels like intervening at the source code should be better. Interventions that act this hidden or secret level seem like they should be inherently superior.

This appears to be our natural inclination. Stemming at least in part from a bias towards thinking that this hidden level is more powerful, but also by tapping into our desires to find silver bullets and smoking guns, it's much easier to market and sell silver bullet solutions because they feel easier. It is easier to wrap our minds around problems with a single cause rather than ones that have multiple moving parts that interact in unpredictable ways, but because of these natural biases. And because our incentives often push us further away from the therapeutic solutions we need, I think it's important to create safeguards to formalize the ancestral approach to maintaining health, preventing illness and treating disease.

To create a therapeutic framework that integrates an evolutionary perspective that allows us to be systematic about acquiring and sharing knowledge in this area, to have a common language and a compass for orienting our collective efforts to that end. As a means of formalizing this ancestral health paradigm, and as a means of safeguarding ourselves against the lure of reductionism, I'll now walk through a four quadrant model that categorizes the various kinds of health actions we can take according to our new schema. And that, as you will see, provides a clear guiding framework for prioritizing health decision making. So we can categorize any health action we might take into one of these 4 quadrants.

On the vertical is the level of the intervention, the biological level at which an intervention exerts its effect, the game or the source code. And while these are two ends of a spectrum, most interventions tend to fall primarily into either game or source code level. Remember, game level interventions operate at the level of the evolutionary forces that have shaped our biology. Source code interventions operate at the level of biochemical mechanisms interacting with proteins, receptors, neurotransmitters, and so on.

On the horizontal axis is the goal of the intervention or the health action that we've taken. First, we have actions that are supportive. Supportive interventions are those that activate or amplify the body's ability to achieve health, or in other words, that assist the body in something that it's already trying to do. Interventions of this nature are thus directed towards minimizing or mitigating mismatch by modifying our ordinary daily behaviors to increase the evolutionary concordance in our lives. To make the conditions of our present habitat closer to our natural one, as all of the physiological process which we'd like to optimize have been finally calibrated to that ancestral habitat.

So here we're really using environment to move the balance of normal Physiology in a favorable direction. Disruptive or exploitative interventions are those that are intended to disrupt or exploit the default Physiology in an effort to either enhance biological function or to correct defective functioning. In these cases, we're doing so because we believe that the effects of the disruption are preferred over the default Physiology. Exploitative interventions are similar in that we're taking our understanding of Physiology and exploiting those physiologic mechanisms either to enhance health or function or treat illness, again overriding the physiological status quo.

So in the upper left hand quadrant, which we'll refer to as quadrant one, are interventions or health actions that act at the game level. Or are evolutionarily familiar and that support or assist the body in what it is already trying to do. Examples here would be doing things like eating an ancestral diet that removes evolutionarily novel foods, getting adequate sleep quantity and quality, going outside in the morning or avoiding eating at night to maintain circadian alignment, filtering out blue spectrum light after sunset, and so forth.

These are all the things that we do to bring our present environment more in line with our ancestral 1. Or to reduce environmental mismatch. Our aim is to provide the body with the range of environmental inputs that all of our physiological processes are adapted to, as a means of both optimizing health and preventing the breakdown in Physiology that results from mismatch and is the root driver of chronic diseases. In the upper right quadrant are interventions or actions that are also at the game level, but whose impact is disruptive or exploitative. Here we're making a game level move to alter or manipulate a particular physiological process in order to improve health in some way.

Some examples of actions in this category would be nutritional therapies like the ketogenic diet where we're promoting a particular metabolic state to achieve a specific health outcome. Another might be heat therapy like sauna where we're using a game level exposure like ambient temperature. Not as a means of reducing mismatch, but in order to induce physiological responses that we think are beneficial. Another example here would be mindfulness meditation, to improve the health and function of the brain and body In our lower left quadrant, we have interventions or actions that act at the source code level but are supportive in their impact, An attempt to assist the body in something it's already trying to do.

Once again, these are interventions typically undertaken as a means of reducing mismatch. In this category, we'd have things like specific vitamins and minerals that we would have obtained in adequate amounts in our ancestral habitat, but that are commonly insufficient in our present one. For example, we tend to spend significantly less time outdoors in direct sunlight in our modern environment, which is why vitamin D deficiency is so widespread. One way to reduce that mismatch would be to take a vitamin D supplement, the dissociation of our daily rhythms from the rise and fall of the sun.

Can result in degradation of our sleep wake cycle, and one potential way to mitigate the impact of that mismatch would be to take supplemental melatonin. The typical Western diet is deficient in several essential vitamins and minerals, and taking those in purified form would be a means of reducing that particular mismatch. Again, these are examples of mismatches that result in the body not having sufficient amounts of the raw materials it needs to carry out normal physiological processes.

Our last quadrant in the lower right corner contains source code interventions that are disruptive, where again, we're attempting to override a physiological process to promote better health. That may seem like a bold and risky thing to do, so where might it make sense to do so? The primary scenario where we might want to do such a thing is when one of the body's regulatory systems has broken past the point of no return. Injecting insulin for diabetes would be one example. In this case, the system for regulating blood glucose is broken, as the body has lost its primary means of doing so because of disease.

Now the physiological status quo can result in dangerously high blood glucose levels, so it can make sense to alter that with a source code intervention. Another example would be hyper supplementation, meaning taking a particular supplement like a vitamin past the point that's needed to support baseline physiological functions. In order to achieve some kind of medicinal effect, a large dose of magnesium, for example, to relieve Constipation, or large quantities of vitamin C to shorten the duration of an upper respiratory infection.

In these instances, we're using our understanding of biology at the source code level to manipulate a certain part of it to either enhance function health or to treat illness, and so the prioritization schema according to the argument I've just outlined. Would be that interventions in quadrant one would be favored over those in quadrant 2, which are favored over those in quadrant 3, which are favored over those in quadrant 4. Our primary interventions we utilize to promote health and treat illness should be game level and supportive, and our last resorts should be those that are source code level and disruptive.

The only instance where we would deviate from this rule. Would be if we had clear head to head evidence indicating that an intervention in a higher quadrant was superior than an intervention in a lower quadrant.

Another reason I think we've continued to repeat the same mistakes is because source code solutions scale well. And if you want to build a big profitable business as well as solve big public health problems, you need solutions that scale. And drugs and supplements scale very well. Furthermore, our system is currently designed in a way that allows you to create a billion dollar blockbuster drug that isn't superior to ones that already exist.

And that's much easier to do than to create a truly superior drug, as history shows. As a result, this has lessened the incentives for finding superior solutions. In order to progress in medical therapeutics, we need a new model for developing, evaluating and refining game level interventions that leverage the tools of science and that can scale. A model that provides new incredible ways of advancing therapeutic knowledge beyond the randomized control trial and models for how those things can scale.

So that we can create interventions that are profitable and impactful. And I think this is entirely possible that you can take team game levels approach to winning the game, apply the tools of science to get better at every step in the process in ways that can scale. Let's again take the strategy for getting better at any game or acquiring game level knowledge. That process is play or make a move in the game, assess or acquire feedback, modify your approach based on the feedback and make another move.

This creates a virtuous cycle of improvement, one that can be amplified by applying the tools of science at each step, coupled with our own intuitions and experiences at the playstep. Again, we're talking about game level actions of some kind. In many cases, this will be the adoption and maintenance of new behaviors to which we can apply the science of behavioral change, as well as all of the emerging technologies that support it.

In recent years, we've also seen significant growth in the number of technologies that are being developed to assist us in playing the game better, from smart lighting to cooling blankets to sounds for prolonging deep sleep. In the assessment step for any game level intervention, we have various ways of collecting feedback including our own subjective experience along with a host of advanced technologies like lab testing, biometric devices, imaging and digital phenotyping. As mentioned, providing feedback about the effects of game level interventions is where our source code knowledge is best applied. In the modify phase, we then use the feedback we've collected to decide whether and how to amend our game level actions.

Here we can increasingly augment our own analytical skills using the powerful and ever expanding tools of data science and machine learning. Let's take an example. As mentioned, one promising area of game level interventions are nutritional therapies like the ketogenic diet. In this case the primary intervention is the diet itself, which for most people implementing it will mean a significant lifestyle adjustment and so the success of that implementation will depend heavily on the facilitation of behavior change. Various types of feedback can then be obtained, including processed based metrics to monitor adherence to the diet, primary means of effectiveness for the condition of concern. Which in this case would be the effects on fasting glucose and hemoglobin A1C and then a host of potential secondary metrics that may be impacted as well, like blood pressure, body fat, lipid profile and so on.

Based on that feedback, modifications of various kinds can be made from changes in the composition of the diet to changes in the nature and frequency of support to improve adherence and so on. Another example of how this works would be with interventions for enhancing sleep, for example. We know that, as is typical of game level interventions, deep sleep has unique, powerful and wide reaching health benefits, and so just getting a little bit more every night could have an outsized impact on current and future health. It's also likely that there are a great many variables that influence the time we spend in deep sleep, some of which are known, some of which are yet to be discovered.

And then there are promising emerging technologies that interface at the game level that can influence time and deep sleep. So once again, at each step in our virtuous cycle, we have opportunities to enhance our results through the application of technology. Each of these factors of influence can be manipulated, assessed and refined with the potential payoff that's enormous, again far exceeding in scope and impact anything that intervenes at the source code level. And on top of all that, we can then use the tools of systems analysis to look at how all of these different domains interact. To uncover previously unknown interrelationships and contextual dependencies. For example, we know that alcohol reduces deep sleep, and yet we also know that drinking two alcoholic beverages a day is associated with longevity, a relationship that at our present level of knowledge strikes us as paradoxical.

It's hard for us to reconcile that paradox right now because we don't have a research ecosystem for examining contextual dependencies, as we all know. Right now, the world of health contains many contradictions which are a source of great confusion. Yet most of these apparent contradictions would likely evaporate if we were better able to assess the role of context. So to summarize, the myopic focus on evolutionarily novel source code interventions explains our inability to translate advances in knowledge and technology into better treatments. Or it explains our therapeutic winter. The ancestral health paradigm for chronic disease prioritizes evolutionarily familiar game level interventions. Advancing knowledge about game level interventions requires different research tools and methods than source code interventions.

Creating a research ecosystem for developing and scaling game level interventions will bring about a revolution in human health and therapeutics.