 Esteemed colleagues, I am delighted to participate in this psychology and psychiatry webinar. It's the new format. We all have to get adapted to it. No pressing the flesh, no common dinners, no intrigues and conspiracies behind the scenes. That's what life has come to. My name is Sam Vaknin. I'm professor of psychology in Southern Federal University. It was stopped on dawn in Russia. And I'm a professor of finance and psychology in CIAPS, the Outreach Center of CS, Center for International Advanced and Professional Studies. Today I would like to discuss a new concept, boring models from computing theory and more precisely, computer networks theory, from advanced management theory and from sociology and other disciplines, making a multidisciplinary mishmash. We don't always have to talk about practical things, strategies, treatment modalities. Sometimes we can let our minds roam free, which is what Akadem used to be when I was young, where people gathered to think about the unthinkable. So the title, the subtitle of my presentation is The Conspiracy of Symptoms, Mental Illness as a Network, Metaphor or Reality. Network methodology, network concepts are recently being applied across the board to mental health disorders, to psychopathology. So there are scholars who treat symptoms as nodes in a network. All the symptoms are equipotent nodes in a network. They are causally interconnected via biological, psychological and societal mechanisms. This of course raises the first problem in applying such models. In typical networks, nodes are either equipotent or they are assigned some weight, they are weighted. This is extremely difficult to do with psychopathological symptoms. The second thing is that there is a variety of transmission mechanisms in psychopathology. I have mentioned biological, genetic, psychological, societal, epigenetic, etc. How do we appropriately describe and grasp these interconnections? And how do we cope with the synergy between some of these mechanisms? This is an open question which I will try to deal with a bit later, forgive me. Symptoms can become self-sustaining, self-reinforcing. Most symptoms in mental health are integrated in robust feedback loops. If you have anxiety, you are likely to develop depression. This is why we have situations of comorbidity where we have multiple mental health problems and disorders recognized in the same patient or client. We could conceive of this as a network element because feedback loops are network elements. The entire network then becomes chaotic, disordered. The feedback is such, multiple feedbacks actually are such that they destabilize the network. Stable states of network symptoms amount to discrete mental health diagnosis. That is the work of Borsbun. His latest was in 2017, a network theory of mental disorders published in World Psychiatry Vol. 16. So he proposes to consider mental health disorders to be stable states of network symptoms. And this reconception of mental illness as a network of directly and dynamically interacting symptoms is nothing short of revolutionary because it's a reversal of the way we regard, the way we see, the way we discuss, the way we dissect, and the way we cope with. We treat mental illness today. But today we have a medicalized view of mental illness. And by definition, a medical view adheres very closely to lists of symptoms. It's more about taxonomy and classification. So it's very static. It's very static. Similarly, a medicalized etiology assumes some common cause, some latent variable. And so all the models we have today, literally without exception, at least in the West, all the models we have today to describe mental illness, mental disorders, mental disturbances, even issues like identity and memory, all these models are static and they have common cause and they have latent variables. And symptoms are brought on by a single mental health syndrome, a single mental health disorder. In other words, syndromes and disorders in current psychiatry are organizing principles. They are taxonomic and classificatory principles. They don't bring to the table new information, definitely not dynamic information. But they serve like drawers, boxes in which we keep the knowledge we had accumulated about the manifestations, which we call symptoms and signs of disorders. So if I tell you, I don't know, narcissistic personality disorder, which is my field of expertise, then I would give you a list, like in the Diagnostic and Statistical Manual Edition 4, Text Revision. Or I would give you a more dynamic model, like in the alternate model in the Diagnostic and Statistical Manual Edition 5. But even this alternate model is essentially not dynamic. It's not dynamic, it's a snapshot. It's snapshot of how a narcissist looks, snapshot of how a narcissist behaves. I am unable to provide you out of the existing texts, existing scholarship and the diagnostic manuals, including the ICD, the classification of diseases. So I am unable to provide you with a dynamic view, with a video. I can provide you with snapshots. I can provide you with numerous snapshots and you can maybe animate these snapshots. But I cannot provide you with a real video. And so Bring Man and Eronan in 2018, they wrote a brilliant article titled Don't Blame the Model, Reconsidering the Network Approach to Psychopathology. And it was published in Psychological Review, Volume 125. It deals exactly with the issues I've just mentioned. In these nascent models, the network models, the emphasis is on internal psychodynamic etiology. And this is the handicap of these models. The older models, the ones we use today, they are static, admittedly. They assume a common cause, common etiology, admittedly. It's not always true. But they incorporate numerous latent variables. Not a good idea, because latent variables, by definition, are latent. So it's a bit like occult or like, I don't know, astrology or something. It's not very serious. But the new models, they emphasize internal psychodynamic etiology. The network connects symptoms and, by definition, symptoms represent, the ratification of internal psychological processes. These models neglect social and interpersonal interactions as major drivers of mental dysfunction. Indeed, incorporating other people in such diagramatics, in such models, I believe will serve to flesh out the network, materialize it, put on a kind of human face on the network, and connect the internal to the external, as is the case in real life. In other words, I advocate using network models. But I think they should incorporate not only internal etiology, but also not only endogenous etiology, but also exogenous etiology. I think when we describe a mental illness using the network approach, we should pinpoint the symptoms as nodes in the network. We should weigh them to the best of our ability. Then we should describe all kinds of processes, including feedback processes and loops that connect these symptoms. But then we should introduce into the network model other people, culture, society, circumstances, expectations. I mean, we should enlarge, we should expand the network approach to gain and to get a holistic view, rather than a reductionistic view. Interactions with significant others, with strangers, with intimate partners, with colleagues, with family, with friends. These kind of interactions are as symptom inducing as any neurotransmitter. Sometimes you talk to someone after that you're anxious or depressed. It comes from the interaction or from the other. You need to incorporate that other person and the interaction you had with them, with that other person in the network. Indeed, external exogenous factors are often the direct cause for such secretions of neurotransmitters and so on. And for most crucial and relevant network effects and cascades in the first place. Ignoring the outside is imperils and undermines the efficacy and accuracy of network models. As usual, evolution borrowed the best of all possible worlds, models, structural and engineering approaches and action principles. In living organisms and even more so in human psychology, hierarchies combine with networks seamlessly. Evolution didn't say, well, the optimal model is a network so I'm going to ignore hierarchy. I'm going to let go of hierarchy or vice versa. The most most efficacious model is hierarchy, so the hell with networks. Evolution is all encompassing. It's constantly experimenting and it borrows the best of all worlds. So when we look at biological entities like organisms and so on, we see that hierarchies combine with networks and the combination is pretty seamless and the results are pretty optimal, favorable outcomes. In other words, we can safely say that evolution is self-efficacious. Consider the brain, for example. The brain is a delicate balancing act between these two models, hierarchy and network. There are interspersed and interacting stable and stochastic structures. We have pretty randomized chaotic structures and we have stable structures. That's a distinction the Jordan Peterson makes between order and chaos. Exactly like in the twin cases of cancer or viruses. Cancer and viruses are mutative pathologies. No one is disputing this, but they are also evolutionary agents. Cancer and viruses, as we see nowadays with the pandemic, they saw chaos. They destroy the agents of disruption, but isn't this the foundation of evolution? They are evolutionary agents, couriers, vectors of evolution. They bring evolution into the body. So it seems that chaos and order, evolution and devolution, experimentation is the foundation of everything. And if mental illness is intimately linked to biology, if it's intimately linked to genetics and epigenetics, mental illness may be a way to experiment with variations on the themes of mental health. Maybe nature is using mental health disorders, mental illness, just to experiment with alternatives to the current normative landscape. Today we say this is normal. It's a statistical statement. But every statistics, every distribution has outliers. These outliers are now deemed mental illness. And by saying illness, we may at least stigmatize these outliers, which previous generations never did. Previous generations regarded paranoid schizophrenics as possessing privileged access to God's mind, for example. So they legitimized psychotic disorders. We today delegitimize, we stigmatize, we medicalize, we pathologize these experiments with mental organization. And I think mental illness is an experiment intended to see in a way evolution wants to see whether it can yield or discover higher, more efficient organizational structures, principles and processes. And that's where network modeling comes in. Because the network model is value-free. It's judgment-free. It just says, this is the way it is. This is the network. These are the nodes. And these are the interactions between the nodes, end of story. It doesn't bring into the picture any cultural bound kind of opinions. It's not opinionated. And so network methodology and concepts are not new, of course. And we've been discussing them for like ages. Douglas Hofstadter noted in his wonderful masterpiece, Gerdl Escherbach, he mentioned Indra's bejeweled net. Indra's net. Indra's net is 3,000 years old. It's a network. And the most modern incarnations of this organizational principle of networks has to do with computing, has to do with business. National economies and the global arena are set up as networks of producers, of suppliers and consumers or users. And so the network from time immemorial has been one of two organizing principles in commerce, in business and even in politics. And the other principle has been hierarchy, network and hierarchy. Business units process flows of information, flows of power and flows of economic benefits. And their main role is to reallocate these flows, to channel them, to distribute them among the various stakeholders, management, shareholders, workers, consumers, government, communities, etc. That's what business units do. But the metaphor doesn't break down when we apply it to cerebral networks, to neural networks, to our brains. Neural networks are similarly used to process information. Information can be an endogenous generated from the inside or exogenous generated from the outside. So neural networks process information. They convey instructions and programming. This is a flow of power. They allocate energy and they monitor and distribute outcomes among its corporeal clients, so to speak. The organs of the body, the tissues of the body, including the brain. Neural networks bring together producers of signaling and catalyzing molecules and their consumers and end users. Various tissues and body systems are regulated, affected by these products, by these molecules. And it's the role of the neural networks to put everything together. In other words, to organize. That's not very different to a multinational business. It's not very different to a government. The principle of networking coupled with hierarchy. That's not a new principle at all. Its application may be new. The thought of using this as a metaphor or as a model may be new. And you can see it's new because there are many mistakes being made. But anyone who has dealt with economics and business as I have for decades. You know, not surprising to us. Nothing new. In mental health networks it is possible that symptoms act like thermodynamic sinks. Symptoms drain that data generated from within and data generated from without. So they collect this data like in a sink. They pull this data and then they filter this data via psychological constructs, via defense mechanisms. They filter and they organize this data into memories and they organize the memories into core identity, into socialized roles, inhibitions, internal and external objects. There is a top-down flow. In other words, hierarchy. And there is a networked flow in collecting the data and more or less distributing it and filtering it. So it would seem that the more basic functions, collection, filtering, distributing, the functions that have to do with transport. Functions that have to do with classification, with organization. These are done via networks. And when we talk about mental illness, because mental illness is by definition disorganized and chaotic by definition low-level organization of the personality. Then of course the network would be much more dominant than the hierarchy. So hierarchy is for higher-level mental functions. It's for memories, core identity, etc., etc. So if we take for example borderline personality disorder, there's identity disturbance, identity diffusion. There's a lot of dissociation. It's one of the diagnostic criteria. So memories are severely disrupted. All the high-level functions in people with mental illnesses, they are either missing altogether or extremely problematic. So hierarchy doesn't come into play so much as network. What mental ill people do, they gather data, internal information, external information. They organize it, badly organize it, properly organize it. Then they distribute it. They filter it, they distribute it. And that's where it stops. That's why we call it illness. Because the higher-level functions are very, very disrupted. Within networks, timing determines priority and privileged access. We know from the digital world, from cyberspace, that first movers, pioneers, early adopters, or processes which immediately follow stimuli such as triggers, we know that they benefit the most from the resources of the network and from network effects. In hierarchies, positioning is spatial, not temporal. One's slot in the pyramid determines one's outcome. So in network, it's important when you have joined the network. And in hierarchy, it's important where are you in the hierarchy? This is a crucial difference. A network is time-oriented, a hierarchy is space-oriented. But this picture is completely reversed when we consider interactions with the environment. The spatial scope and structure of the network, for example the number of nodes, the geographic coverage, determine the success of the network. So there, space matters to the network, how big is the network. And the history of the hierarchy, its longevity, in other words, the time aspect of the hierarchy, is the best predictor of the reputational capital of the hierarchy and the hierarchy's capacity for wealth or signal generation. We know that traditional hierarchies, hierarchies with tradition, in other words, hierarchies with a time dimension, are much more stable, much more ingrained, much more difficult to approve, the new hierarchies just established. So when we deal with external interaction, in networks what matters is space, how big and spread out the network is. And in hierarchies what matters is time, how long has the hierarchy been around, its tradition, its track record. This is a very crucial distinction. And dogenously, when you look at the inside, hierarchies, what's important is where are you in the hierarchy, the space. And what's important for networks is how long have you been in the network, incorporated in the network. But when you look at the outside, the picture reverses completely. Counter-intuitively, access to information and to the power that information affords, they are not strongly correlated with accrued benefits. This is contra to everything we are all being taught. We are being taught that the more information you have, the more access to information you have, the more powerful you are. In networks, information and power flow horizontally. Everyone or everything, every node is usually equipotent and isomorphic. A network is like a fractal or like a crystal. Every segment of the network is identical to others. Every segment is the same like others, both structurally and functionally. This is the isomorphism of the network. But benefits in the network accrue vertically to the initiators of the network, the fathers of the network. And benefits are heavily dependent on tenure and on mass, the number of nodes under the actor, time-wise. So the earlier participant, the earliest participants, the earliest nodes, the earliest members, they enjoy an exponentially larger share of the resources and benefits of the network than latecomers. Anyone who has ever participated in MLM networks will tell you that 95% of the commissions go to the 1%. So this asymmetry is built into networks. I would even venture to say that current contemporary income inequality, which is the highest ever in human history, is because we have transitioned to network models in business, in politics, in our communication, in digitally. The world is much more networked today. Because it's networked today, some people get most of the benefits, most of the resources of the network. Add revenues in business. It's also distributed the same way. And access to mental resources within the human mind, access to mental resources and to processing power, similarly accrues disproportionately to early things, early memories. And that was the crucial insight of psychoanalysis, realizing the disproportionate weight and role of early childhood. Early means overwhelming. Early means triggering. Early means hogging the resources of the network, or the resources of the mind, the resources of the brain. The earlier the experience, the earlier the memory, the earlier the trauma, the earlier the relationship, the earlier the interaction, the more power it has. In hierarchies, benefit accrues is also closely correlated with one's position in the organization. It is not so correlated with one's tenure. You could spend 50 years in a hierarchy and still make less money than someone who joined yesterday. Why? Because he's in a higher position than you. Position matters in hierarchy, not time. Hierarchies are in this sense timeless. Power, information and benefits are skewed and flow vertically and asymmetrically. The hierarchical organization is based on diminishing potency and heteromorphism. There's no functional cross-section of the structure that resembles another. If you take one part of the hierarchy, it does not resemble. This is the essence of the hierarchy, to not resemble another part. Hierarchies are heteromorphic. Well, networks are isomorphic. So where you are in the hierarchy is super crucial. Members of the hierarchy experience an external locus of control. Their destiny, their fate, their place in the career ladder, their career path, their earning power, their relative positioning, their own control from the outside and by others. And so they develop usually alloplastic defenses. They blame the organization for their failures and errors. They blame the world. They also evolve passive aggressive reactive patterns. But this is for another lecture. As usual, evolution borrowed, as I said, the best in both worlds. The best of both possible models. The best of structural engineering approaches and action principles. And I said earlier that hierarchies and networks combine seamlessly to yield optimal favorable outcomes. Also in human psychology. And again, let us consider the brain. The brain is the apex and culmination of creation. Well, at least in some people. Neural activity in the brain is subject to thresholds. Thresholds of activation, thresholds of excitation, which accrue in multiple populations or units of neurons. In other words, there's a signal. A signal coming from multiple neurons. If it passes the threshold, if it exceeds the threshold, we have a go, we have a signal. If it does not, it dies. And this kind of structure is midway between network and hierarchy. And it is not unprecedented. We have similar structures, which are combination hybrids of networks and hierarchies in business structures, in politics and so on. Consider, for example, the stock exchange. The stock exchange has a trading curve. What they call a circuit breaker. A circuit breaker. When the signal reaches a certain level, for example, when the stock exchange falls by more than 10%, trading stops. Every equidistant participant in the stock exchange is equivalent, at least ideally. So every equidistant participant is a node. Equipment node in the network. But the network is a hierarchical, hierarchical processing of signals. The signals are not equipped. Signals are not created equal. Signals depend crucially on minimum requirements, on a threshold. And if the threshold is exceeded, in the case of the stock exchange, all activity stops. In the case of neurons, it's exactly the opposite. If the threshold is exceeded, the signal is generated. And passed on, biochemically and electrically, it's passed on. Neural transmitters regulate all this. So you see, the brain is not a network model. It's not a hierarchy model. It's a hybrid model. It would stand to reason that mental illnesses, which are essentially illnesses of the brain, would have the same features. They would have the same specs. Mental illnesses would also have elements of network and elements of hierarchy. I would be very surprised and shocked to discover that mental illnesses are only networks. So the current proponents, the scholars which today are trying to model social mental illnesses and mental health disorders, model them via networks and via networks only, exclusively. I think that they're making a serious mistake in ignoring hierarchical elements because the brain is not built this way. It's not a perfect network. It's a hybrid. Networks evolve from informal, diffuse structures to increasingly formal structures. And hierarchies go exactly the other way. From formal structures to less, to more and more informal structures. So networks from informal to formal, hierarchy from formal to informal. The formal hierarchy ends up playing host to numerous informal networks within the hierarchy. You know, the prime minister's kitchen. They click in the boardroom. They intrigue on the production floor. The brain is neuroplastic. It neuroplastic and it rewires its pathways, its neural pathways as it processes information and generates memories. The brain is a perfect example of informal conspiratorial networks which spring to life ad hoc and then mysteriously vanish in business over time. And as size increases, informal networks tend to introduce terms of service. Regulations and etiquette that render them less nimble, more focused. The brain, these networks generate proteins that code for memories. And these proteins are stable structures. They're stable structures with an otherwise plastic neural networks, plastic neural pathways. Anyone who has ever seen a video rendition of functional magnetic resonance imaging, fMRI, would immediately recognize what I'm saying because there's this kaleidoscopic firework display in the brain when it's exposed to a stimulus, internal or external stimulus. There's this fireworks display, which is pyrotechnic. They're beautiful and technical, but they are these dark, stable areas, spots which represent protein structures, immutable protein structures. These are memories and so on, equivalent maybe to shimmers. So there is this hybrid visual that can be easily visualized with fMRI. And finally, hierarchies tend to concentrate their concerted efforts on problem solving, fending off challenges. Hierarchies are defensive structures. Hierarchies anticipate the worst. There's going to be a problem. There's going to be an enemy. There's going to be an adversary. There's going to be a competitor. We need to pull our resources. We need to gather them and we need to counterattack. Hierarchies seek equilibrium and homeostasis. This is the optimal state of the hierarchy. They avoid, assiduously avoid, creative destruction, disruptive technologies, paradigm shifts, paradigm altering innovation. In the business world, networks thrive on challenges and novelty, exactly the opposite of hierarchies. They seek instability. They benefit, networks benefit from disequilibrium and disruption. Networks foster technological instability as well as other forms of chaotic interactions such as creative disruption and creative destruction. Of course, equally networks tend to attract entrepreneurs, mavericks, not managers, not academics, like us, for instance. So networks at the hub, the hub of both destruction and creation, they are the chaotic maelstrom where things generate and regenerate and then hierarchies take over and kind of ossify and fossilize the end result. So what I'm trying to say is that networks and hierarchies are symbiotic. Everyone knows that a good entrepreneur is a bad manager and a good manager is a very bad entrepreneur an innovator. So they're mutually exclusive in many ways but also symbiotic. They cannot survive without the other. Hierarchies peter out and die out if they don't innovate. Just look at the fate of companies like Kodak or MySpace. So networks tend to fizzle out if they cannot at some point create a hierarchy to take advantage of their products and resources. This is symbiotic. I don't believe that mental illness is the sole exception in nature and the sole exception in human affairs. I believe that networks also are symbiotic structures incorporating network elements and hierarchies. Mental illness is hybrids hybrids of hierarchies and networks not only networks. There's something deeply flawed in trying to reduce mental illness to network elements and network concepts only without taking into account very rigid and structured hierarchies in the brain and in the mind. The brain is a delicate balancing act between these two models of hierarchy and network and we can't just ignore this. Both hierarchies and networks are homophilic. They attract same-minded people in silos. They attract similar stimuli, same information, same constituents, same elements, sameness. Sameness is prevalent in both hierarchies and networks and so both hierarchies and networks act as syncs and both are threatened by the emergence of in-house monocultures which are susceptible to external shocks. If you do the same thing all the time the same way never mind if you do it via a network organizational principle or via hierarchy you create a monoculture of like-minded people. Your subject to confirmation bias you filter out countervailing potentially very useful information and the monoculture lowers your immunity renders you susceptible to a virus, a virus of the mind or a physical virus. But having said this networks are far better suited to leverage synergies. They are less rigid than hierarchies and they have the upper hand as far as coordinated emergent response times and the dissemination of new information. Networks are also far better suited to optimize the social or peer capital. So networks are much better than hierarchies if there's an emergency they respond much more nimbly, fast. They disseminate information on the go and at the speed of light they also optimize their members, the contributions of their members and the same goes for biological cells in a tissue or for neurons in the brain they are all peers these are all peer-to-peer networks and they emphasize social peer-to-peer interactions over top-down flows. This is a crucial aspect of mental illness. Mental illness might be the peer-to-peer network gone awry. Maybe mental illness is at the same when the individual or the collective tries to convert hierarchy into network or network into hierarchy or peer-to-peer network into another type of network. In other words, maybe mental illness is a phase transition symptom or phase transition artifact. When we try to transition from one model to another in how we organize data how we process data and stimuli and how we react to them maybe women try to make this mental shift when we try to go through the portal at warp speed, you know when we go through the wormhole and say ok we no longer want to think about it this way we want to think about it this way that way, new way we want to reconsider our lives we want to rethink our relationships whenever there is a paradigm shift I would tend to believe that it would generate some kind of mental chaos mental disturbance, mental disorder mental diffusion and this is well supported by research by studies we know that life transitions Gail, she had just died she had written the wonderful book Passages and then the follow-up New Passages the 1970s and 90s that was her main thesis her main thesis was that transitions engender internal chaos which can be easily conceptualized as mental illness she had written passages the diagnostic and statistical manual was about if my memory doesn't fail me 200 pages long today it's a thousand pages long we tend to pathologize we tend to medicalize many behaviors and traits which used to be acceptable in the 70s but had she lived today I think she would have used the language of mental illness she would have used the language of the diagnose of the DSM the passages and gender mental illness networks go through a life cycle which can be divided into three phases the mimetic phase the network effects phase the collapse phase and yes the collapse phase is ineluctable it's a natural outgrowth of network dynamics it's exactly like death in the organism or in a complex organism the mimetic phase is autonomous it's based on distributed replication of memes it is characterized by fecundity replication but not by fidelity the authenticity of the replicated memes is usually not preserved the memes are corrupted in transmission you know when we were kids when I was a kid at least we used to have this game of broken telephone the message at the beginning of the line had nothing to do with the message at the end of the line the fidelity the authenticity the genuineness the loyalty and faithfulness to the original message is not preserved the replicated memes get corrupted and changed but fecundity the number of replications I mean it's exponential it explodes exponentially very much like the dynamic in a viral pandemic so the mimetic phase is a viral phase it also preserves longevity the replication provides a reason to exist so as long as the network replicates it expands and as long as it expands it survives we use emotions and cognitions to fixate memories and to contextualize memories precisely for this reason precisely for this reason we use dead memories in networks in many mental health conditions this process is interrupted by various forms of dissociation for example when you forget but I mean forget completely when you dissociate it's not possible to replicate and there's no longevity the meme is lost the information is lost the data are lost and when you're confronted but with infantile and regressive defense mechanisms cognitive deficits cognitive biases emotional dysregulation vulnerability all these things interrupt interrupt the transmission the transmission mechanism the vector mechanism breaks down the network dissipates and disintegrates because it can no longer serve its two main functions distribution of information via replication and surviving longevity and then there's a transition in healthy people to a phase that is a phase of network effects or more technically network externality and it is based on the bandwagon effect a positive feedback loop enhances the value of the network for its members and users the greater the number of members and users the greater the value of the network to each and everyone inside the network to each user, to each member it's a bandwagon effect it's a positive feedback loop same happens in the brain with neurons the bigger the pathway for example the dopamine pathway is possibly the largest stable pathway in the brain and very much a network pathway and you see what happens with the dopamine network it induces addictions it is a direct cause of addictions it's a pleasure principle as Freud called it it fosters an engenders impulsive behavior it's responsible for I don't know half of all human behaviors and 80 to 90% of all dysfunctional behaviors unhealthy behaviors that's a single pathway because it's big so each of its members each of the neurons derives a enormous benefit from remaining in the pathway not exiting the pathway not residing from the network the more insulated the network is the more of a self-sufficient and self-sustaining ecosystem it is the greater its value to members exactly opposite of our intuition our intuition is when the network is open to external input when it's open to the world when it's open to receive additional data it's more self-efficacious it's more efficient it's securing favorable outcomes in the environment and therefore it has greater value for its members the truth is exactly the opposite a degree of openness to the environment is critical no one is disputing this because proper regulation calibration verification within a regime of non-impaired functional testing of reality that crucially depends on interaction with the environment but up to a point beyond that point the network dissipates it falls apart networks therefore exactly like hierarchies must maintain firewalls a defensive perimeter red lines and within this the ecosystem thrives strangers are allowed in in the form of stimuli triggers, information data, they're allowed in but in a very measured way it's like Donald Trump's immigration policies various psychotherapies emphasize the self-reinforcing aspects of networks cognitive behavior therapies for example and other psychotherapies emphasize the homeostatic functions the defensive functions of networks and that would be for example mindfulness but the truth is that both of them are critical the orthodox prevailing wisdom is that as some critical muscle threshold are transcended the network goes viral going viral depends on the threshold there's a tipping point so but this is not necessarily good news in nature viral pandemics self-limit actually and they peter out aging related mental health disorders can be thought of as the unfortunate byproducts of the inexorable process of the winding down of an organism and this winding down of the organism is the outcome of herd immunity that's been established in natural now immune hosts so in a way going viral provokes a backlash if you hear a virus and you're going viral the backlash would be the immune system herd immunity natural group of immune hosts would limit your existence actually would eradicate you like polio in Africa now via vaccination otherwise but you create a backlash and similarly mental health disorders possibly create backlashes or are the backlashes to going viral going viral is a critical a critical feature because it's on the one hand it expands the network enhances the value of the network for its members the other hand it bumps against reality bumps against the environment the environment is bound to react aging related mental health disorders they are the unfortunate byproducts of winding down of the organism but why is the organizing why is the organizing winding down why do we age why do we age is that our networks expand and expand inexorably and we go viral every single individual is going viral especially today when you have social media but even before we went viral we annexed we appropriated we expanded we grew there's a concept of growing up personal growth so we went viral as we went viral reality pushed back created friction I think this friction is what we call aging this friction generates mental illness and there are so many commonalities between aging and mental illness aging is a form of mental illness and aging incorporates numerous mental illnesses needless to say Alzheimer's dementia and so on all networks decline all networks decay and collapse if they fail to activate their members if they fail to monopolize or consume the constituents time to monetize their eyeballs for example digital platforms to reward members for time spent within the network to create value added intrinsically or extrinsically if any failure these critical areas destroys the network and incipient networks decay in the brain if they fail to excite or to activate a neural pathway so we have this network of neurons they're all firing at the same time but they don't succeed to pass a threshold they don't succeed to excite they don't create an excitatory state the multiple unit is not big enough so no non-neural pathways there to be activated or to react and so they get no feedback from the body from the brain and they die they're perfect examples of the decay of decadence of networks various reinforcement techniques leverage this principle to inculcate in the target some pathology or to eradicate healing by flooding the mind the brain with a relevant behavior hearing signals and messages or the opposite by starving the unhealthy people the patients the clients mentally ill people by starving them of the cues and the triggers that provoke the illness social media make abundant use of these psychological insights and revelations to foster operant conditioning and long-term addiction in there I must say also if the network is totally sealed off totally homophilic it's biased as far as information and membership flows are concerned it's subject to solipsistic confirmation bias that I mentioned before it is doomed to collapse following the collapse the network can survive as a remnant or residual network a kind of a neutron star network or as an archive which is exactly what we call memory it's a set of memories organized into reframed narratives problem with mental illness is that this process of narration fails the confirmation bias is so extreme the reframing is so total that finally there's a break chasm in reality testing and in contact with reality itself and we reach a psychotic state and so certain mental health conditions such as psychotic disorders mimic this solipsism by confusing and conflating internal objects with external objects and consequently no information is granted a privileged position no data deemed objective this hyperreflexive confusion makes it impossible for the patient to generate self-efficacious feedback loops based on proper reality testing all told reality regulates networks much more than it does hierarchies networks thrive when two conditions are met rigorously one, when they generate meaning intrinsically no matter how outlandish it is if you consider things like religion I don't know all kinds of sects eccentric cults believers in the unbelievable or disbelieveable if you consider all these people like networks they don't care about the veracity and the objectivity and the truth of the message that the network generates they just care to belong to the network they adhere to the messaging of the network and the signaling of the network no matter how counterfactual how obviously insane just because it guarantees membership such self-generated meaning actually bonds the members and affords them a feeling of home of exclusivity of belonging to a brotherhood it affords them a narcissistic boost due to their access to arcane or occult knowledge this is the psychological trait known as conspiracism conspiracy theorists have this and networks decay and die when meaning is exclusively imported totally from the outside when it's extrinsic or even when it arises only as a result of the network's interactions with other exegetic normalogical or hermeneutic systems mental illness may be reconceived exactly like this an exclusively internal generation of meaning which is not subjected to unimpaired or rigorous friction with reality and the second condition which is very interesting and prospering and success of networks is when they generate value not only meaning but value endogenously by empowering and gratifying their members as they leverage the total resources of the network political parties in opposition social media institutional religions and the Freemasons their examples of such networks networks decay outside for value creation exogenous value proposition even hybrid networks such as multi-level marketing networks I mentioned before they are doomed to fail ultimately and again mental illness mental illness is largely so existing for example in the case of delusions or hallucinations it's totally self-contained it's like to do reality there's no reality testing there, it's not there mental illness serves to restore both egosynthesis and self-efficacy it is therefore of critical value to the mentally ill patient and this might explain why curing mental illness and healing the afflicted why they are so difficult to accomplish mental disorders in most cases are positive adaptations which allow for the optimization of scarce resources under the constraints of the individuals idiosyncratic personality in chaotic life circumstances mental illnesses when they are conceived as networks they generate both things they generate meaning the mentally ill patient derives meaning from his mental disorder and they generate value because they are a positive adaptation they allow the individual to function thus the more insulated the more self-contained the more self-sufficient the network and its mimplex the more they are a floating air balloon self-contained universe a bubble universe like in a multiverse the more the network is like this the more the mental illness is like this the more it generates meaning for example by setting goals or by reinterpreting the world in a certain way the more it generates value benefits, emotional, economic the longer the network survives the longer the mental illness survives and the more they thrive yes, mental illness can thrive Facebook and Apple are prime examples of such insular closed exclusive ecosystems and mental illness may be the equivalent in the human mind or the human brain it's closed off enclave it's a reserve where an alternative reality thrives where virtual reality overtakes so called objective real reality and in many cases mental illness works allows the individual to function we must accept this we must not consider all mental illness as a negative thing as a network using a network model we begin to see its advantages and we should be very careful and discriminating in treating mental illness lest we take away these critical factors adaptation and meaning people can survive without a reality testing they cannot survive without a meaning thank you for your time