As esteemed colleagues, I am delighted to participate in this psychology and psychiatry webinar. It's the new format. We all have to get adopted 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 a professor of psychology in Southern Federal University in Rostov-on-Don, Russia. I'm a professor of finance and psychology in SIAS-CIAPS, the Outreach Centre of CIAS, Centre for International Advanced and Professional Studies.
Today, I would like to discuss a new concept, borrowing 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 academia 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.
Thisof courseraises 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're 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 Borsboom. His latest was in 2017, a network theory of mental disorders published in World Psychiatry, volume 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.
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 ideology 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, and 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 four, text revision, or I would give you a more dynamic model, like in the alternate model in the diagnostic and statistical manual, edition five.
But even this alternate model is essentially not dynamic. It's not dynamic, it's a snapshot. It's a 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, classification of diseases. So I'm 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 Bringman and Ironan 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 starting, admittedly. They assume a common cause, common etiology. Admittedly, it's not always true. 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 reification of internal psychological processes.
These models neglect social and interpersonal interactions as major drivers of mental dysfunction.
Indeed, incorporating other people in such diagrammatics, 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 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. 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 or with colleagues, with family, with friends. These kinds 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 he 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 imperious 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 efficacious model is hierarchy, so the hell with networks.
Evolution is all in competency. It's constantly experimenting, and it borrows the best of all worlds.
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 that 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?
Evolution. They are evolutionary agents, couriers, vectors of evolution. They bring evolution into the body.
It seems that chaos and order, evolution and devolution, experimentation is the foundation of everything.
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 disorders, 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 immediately 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 diligent demise. 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 why 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 culture-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 masterpieceGirdel Escher B. He mentioned Indra's Bejeweled 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.
And so 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 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 their 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 businesses, as I have for decades, you know, it's not surprising to us, nothing new.
In mental health networks, it is possible that symptoms act like thermodynamic sinks. Symptoms drain data generated from within and data generated from without.
So they collect this data, like in a sink, you know, 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.
This hierarchy is for higher level mental functions. It's for memories, core identity, 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 mentally 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, 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 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.
So this is a very crucial distinction. Endogenously, 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.
Counterintuitively, access to information and to the power that information affords, they are not strongly correlated with accrued benefits.
This is contrary 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 equipment 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 initiatives 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 earliest participants, the earliest nodes, the earliest members, they enjoy an exponentially larger share of the resources and benefits of the network than late comers.
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 our 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. Ad revenues in business is 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 accrual 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, while 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 and 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 coming from multiple neurons. If it passes the threshold, if it exceeds the threshold, we have a goal, 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 curb, 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 equipotent, at least ideally. So every equidistant participant is a node, equipotent node in the network.
But the network is a hierarchical processing of signals. The signals are not equipotent. 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 mental illnesses and mental health disorders, model them via networks and exclusively, I think 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 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, the clique in the boardroom, the intrigue on the production floor.
The brain is neuroplastic. It's neuroplastic and it rewires its pathways, its neural pathways as it processes information and generates memories.
So the brain is a perfect example of informal conspiratorial networks, which spring to life ad hoc and 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.
And in the brain, these networks generate proteins that code for memories. And these proteins are stable structures. They're stable structures within otherwise plastic neural networks, plastic neural pathways.
Anyone who has ever seen a video rendition or 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 and beautiful in technical.
But there are these dark, stable areas, spots, which represent, you know, protein structures, immutable protein structures, these are memories and, and so on, equivalent maybe to shamers.
So there is this hybrid visual, we 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 counter attack.
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.
Consequently networks tend to attract entrepreneurs, mavericks, not managers, not academics, like us for instance.
So networks are 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 and 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 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 sinks 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 network organization or principle or via hierarchy, you create a monoculture of like-minded people. You're 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 their 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're all peers. These are all peer-to-peer networks and they emphasize social peer-to-peer interactions over top down floats.
This is maybe a crucial aspect of mental illness. Mental illness might be the peer-to-peer network gone awry. Maybe mental illness is at the same time 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, when we say, okay, 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's 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 Sheehy 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 and gender internal chaos, which can be easily conceptualized as mental illness. When she had written Passages, the Diagnostic and Statistical Manual was about, 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 diagnosis of the DSM. She would have said that Passages and Gender Mental Illness.
Networks go through a life cycle, which can be divided to three phases, the memetic phase, the network effects phase, and the collapse phase. And yes, the collapse phase is inelectable. It's a natural outgrowth of network dynamics. It's exactly like death in the organism or in a complex organism.
The memetic 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. And 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 reserved. 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 memetic 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 embed 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 with infantile and regressive defense mechanisms, cognitive deficits, cognitive biases, emotional dysregulation, wound mobility, all these things 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 this is 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. This is what Freud called it. It fosters an engender's impulsive behavior. It's responsible for, like, 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 enormous benefit from remaining in the pathway, not exiting the pathway, not resigning 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 at 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, proper validation, 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 prevailing wisdom is that as some critical muscle thresholds are transcended, the network goes viral. Going viral depends on a threshold. There's a tipping point.
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 or herd immunity. It's natural. A natural group of immune hosts would limit your existence, actually, would eradicate you, like polio in Africa now, via vaccination, otherwise.
But you've created a backlash. And similarly, mental health disorders possibly create backlashes or are the backlashes to going viral.
The going viral is a critical feature because it's, on the one hand, it expands the network, enhances the value of the network for its members. On the other hand, it bumps against reality, bumps against the environment. The environment is bound to react.
Again, aging-related mental health disorders, they are the unfortunate byproducts of winding down of the organism.
But why is the organizing winding down? Why do we age?
I think one of the reasons we age is that our networks expand and expand and expand inexorably. And we go viral. Every single individual is going viral, especially today when you have social media and so on.
But even before we went viral, we annexed, we appropriated, we expanded, we grew.
There's a concept of growing up, personal growth, growth. So we went viral.
As we went viral, the world, 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 in 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 pathway is there to be activated or to react. And so they get no feedback from the body, from the brain, and they die. They die out.
They are 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 the relevant behavior triggering 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 their, I must say, unfortunate uses.
Also, if the network is totally sealed off, it's 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 or identity. It's a set of memories organized into reframed narratives.
The 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, a chasm in reality testing and in contact with reality itself, and 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.
Consequently, no information is granted a privileged position, no data deemed objective.
This hyper-reflexive 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 disbelievable, if you consider all these people, they're inside networks and 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, when it's 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, nomological or hermeneutic systems.
Metalinus 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 for the thriving and prospering and successful 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, they are examples of such networks.
Networks decay when they depend on the 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 solely existing. For example, in the case of delusions or hallucinations, it's totally self-contained. It's not to do with reality. There's no reality testing. It's not impaired. It's not there.
Mental illness serves to restore both ego-syntheny 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 individual's idiosyncratic personality in chaotic life circumstances.
So 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, a 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 discriminate in treating mental illness lest we take away these critical factors, adaptation, and meaning. These people can survive without a reality testing. They cannot survive without a meaning.
Thank you for your time.