Home Science Page Data Stream Momentum Directionals Root Beings The Experiment
First we review the prior Notebooks. In Notebook #1, Spiral Time, we saw that time has a vertical and horizontal dimension. The vertical dimension of time is virtual, partial, and ever-changing. This is the dimension of time that we are studying. In Notebook #2, Data Stream Momentum, we developed the theoretical framework behind the density and momentum of the vertical dimension of time. In Notebook #3, Decaying Averages, we introduced the notion of Decay into our Data Stream measures. This had the advantages of greater sensitivity, more relevance, and easier to compute from a contextual framework. We also introduced the notion of Realm of Probability vs. Range of Possibility. Also introduced was the concept of Neural Networks to compute the averages and averages of averages.
This part looks at the virtual power of Data Stream measures. For humans, in addition to their descriptive and predictive aspects, DS measures also have the virtual power to influence behavior.
In this part we see that rocks don't evolve. We see that plants evolve survival hardware through generations. These two groups do not need predictive measures to facilitate survival.
Because animals are mobile, they have many more choices than a plant. However mobility increases the potential for danger and the ability to evade or avoid danger through anticipation. In long term analysis the ability to anticipate seems to be a more important mechanism for survival than any physical attribute. The ability to anticipate is related to the ability to predict.
Positive/negative reinforcement prediction is based upon pattern recognition, while computational prediction is based upon complex data analysis. These two types of prediction complement each other. These studies are mainly focusing upon computational predictions.
In order to do quick predictive computations, animals need neural networks, which store a decaying average and a decaying deviation. The average predicts the most probable next Data Byte, while the deviation predicts the expected range of values, the Realm of Probability. These predictive measures enable animals to conserve energy. Predators develop the software to catch the prey through repeated experimentation. This software is modified with age and experience. The prey only develops the hardware because they don't have the luxury of experimentation. Most animals are predator and prey. Hence they store neural networks to compute decaying averages and deviations. The greater the computational ability of the organism, the better the predictions, hence the greater the evolutionary advantage.
While the human being needs to know the Average Deviation just as much as the other animals, the human being seems to be unique in his craving for diversity. Other animals including the Neanderthals, our nearest genetic cousin, don't seem to crave diversity like we do.
These experimenters suggest that it is the potential of Homo sapiens sapiens to experience the pain of boredom that has inspired the need for diversity. We define human boredom as the perceived narrowing of potentials. The narrowing of potentials is based upon the computation of the Deviations. When the Deviation begins to shrink the human feels the pain of boredom. The pain of boredom or the need for diversity is not directly related to survival.
Animals without the boredom are content with existence as it is. When their physical needs are taken care of and there is nothing to do, most animals rest. Homo erectus, a very successful member of the homo genus, was content with the same style stone ax for over one million years. It worked. For Homo sapiens sapiens, however, functionality was not enough. Boredom created the need to experiment with forms for diversity. The Neanderthal, Homo sapiens, our immediate genetic predecessor was our equal in brainpower and yet remained primarily on the functional level of existence, presumably, because they experienced no boredom. The propensity of Homo sapiens sapiens for experimentation with the environment conferred an evolutionary advantage. An experimental strategy is bound to yield both success and failure. The successes will eventually surpassed the tried and true while the failures fade away. While the potential for boredom is great evolutionarily, it does cause greater dissatisfaction and pain. The pain of boredom propels humans forward by denying satisfaction from remaining the same. Hence the pain of boredom is an evolutionary mechanism that communicates with us and needs to be listened to. Many of the messages of boredom are merely extraneous neural networks trying to preserve themselves and must be ignored. Meditation assists in separating the essential from the extraneous.
The Balance to the Boredom Principle is the Homing Principle. The Homing Principle creates a context for change. Constant change is also boring. Humans while being the first to explore their world were also the first to develop a sense of Home in the homo group.
While all of us prey/predators store Deviations in some type of neural network for prediction and description, the ability to experience pain at the death of the neural network seems to be uniquely human. This pain we call boredom. Even our memories seek to preserve themselves through words, photographs, books, and other works of art. Our neural networks have a life of their own which fights for survival by sending out pain messages, which we interpret in different ways, one of which is boredom.
The human experiences the pain of boredom when the neural networks that it has created to compute Deviations begin shrinking. We show the growth of a two dimensional neural network based upon decay. Each successive Data picture is overlaid upon the conglomerate of experiences that went before while this conglomerate is scaled down in intensity. This reflects the decay of experience with time. We show another representation of a neural network, which illustrates the growing stability of the Average. It also shows how we generate the Data that is used to create a neural network for Deviations from the Average. Each of these neural networks seeks to preserve itself.
We set up some definitions of the Greater and Lesser Range of Possibility and the Greater and Lesser Realm of Probability. The borders of these areas are based upon the Averages and Deviations of the Data Stream.
Based upon these definitions we go through a Pulse of Life, frame by frame. We see that when the new Data falls within the Greater Realm that the Realm shrinks while the Ring of Potential Excitement grows.
As the Realm shrinks, the organism feels the pain of boredom until it can take it no longer and shoots into the Ring of Potential. This causes the Realm to grow, which feels good. However when the Realm grows, the Ring of Potential Excitement shrinks. Hence with repeated forays into the Ring of Excitement, this ring of potential shrinks. While an expanding Realm dissipates Boredom, it also shrinks the potential for excitement.
After pushing the limits as far as they will go, the potential for excitement shrinks to such an extent, that the only successful strategy is to retrench. The data falls within the Greater Realm. As potential excitement is stored, the pain of boredom grows. However this strategy is pursued until the Realm shrinks to a point that the Boredom is unbearable.
At this point the cycle starts all over again with the new Data Bytes falling into the Ring of Excitement. This increases the Realm, diminishing boredom, while shrinking the Ring of Excitement, diminishing the potential for future excitement. We show an animation, which turns this cycle into a pulse. The interpretation of the Data by the organism is contextual rather than content based.
The Pulse of Life exhibited in this section, illustrates a successful strategy in dealing with the Boredom Principle. Contract, release, constrain, and escape. This yields a bi-modal distribution to maximize excitement. However if weight loss is the aim this bi-modal distribution is worst. Strategy is contextual.
An evolutionary arms race between predator and prey leads to increased information processing abilities, intelligence. At a certain self-critical point, the neural networks designed to process information achieved a life of their own. This led to the need for self-preservation. This led to many mechanisms, which were unavailable to animals with less intelligence. In this context, intelligence is not wisdom. This section explores these mechanisms.
We've explored the Boredom Principle based upon pain. This part explores the Rush Principle based upon the pleasure of exceeding the Realm of Probability. To achieve this pleasure, a Pulse of behavior must be manifested. On higher levels the Rush seeks to not only exceed the Realm but to stimulate the higher Derivatives as well. Each behavior changes the borders of the Realm. Every behavior that falls outside the Realm increases the area of the Realm decreasing the potential for a Rush. Hence the Rush is based upon the context of behavior not the content of behavior.
The Homing Principle is a mechanism that arises to maximize the potential for the Rush. On one level it is boredom seeking a change from too much change. On another level the Homing Principle is an effective strategy for building up potential energy for the Rush.
The Emptiness Principle is based upon the idea that doing nothing increases the potential for doing something. This principle pervades our lives in the form of vacations, weekends, even sleep. Each of these nothings increases our potentials for something. A testament to the power of this principle is the global nature of the 7-day workweek. The power of these principles based upon information processing is virtual. It is unavailable to animals of lesser intelligence. Hence these principles are uniquely human. While Boredom and Emptiness are similar, the Boredom Principle builds up immediately while the Emptiness Principle takes a period of doing something to come into effect.
On the technical side of the Boredom Principle, a person's Decay factor, D, determines how quickly they will become bored. The Derivatives act as kind of a thermostat, regulating activity by maintaining an optimum size for the neural networks. An interesting implication of the Boredom Principle is the ultimate unpredictability of humans. This leads us to the study of Data Streams rather than Data Sets. Data Streams describe the system in flux while Data Sets describe a fixed slice of life.
These principles we've been discussing are mechanisms not laws. We have explored but one half of the picture. We did not explore the part of our psyche, which abhors change. Again our response is contextual.
Flow Density measures the order or lack of order in a complete system. Random systems have a FD of 0, Physical systems 1, and Live systems between the 1 and 0. Plants tend to have a higher FD than animals because they are more predictable. Humans tend to have a higher Flow Density than other animals for the same reason. Also some humans have a higher FD than other humans. Raising the FD gives a stronger sense of identity. This combats turbulence. Lowering the FD raises the sense of potentiality but approaches turbulence.
The Edge of Chaos is the phase transition between order and chaos. To maximize evolutionary growth this is where the organism wants to be. It is the balance between too loose and too rigid, the very middle of the muddy, little puddle – no absolutes here. Each organism instinctually moves to their own edge of chaos, depending upon their computational abilities. The edge of chaos is where adaptation occurs. The flexibility of response is maximized here. Weaving around the edge is the most effective strategy in achieving self-actualization because one's limits are constantly challenged. Also, to make it even harder to stay upon the Edge, the position of the Edge is constantly changing, because it is determined contextually. Because intelligence leans towards the Edge, Homo sapiens sapiens must constantly be in a state of flux in order to keep up with this changing boundary.
Our meta-principle is that humans avoid randomization as well as materialization. Why does intelligence lean towards chaos? Technically we do not know. Fancifully we suggest a divine Game Player, who is trying to push us to the brink of chaos to further the self-actualization process. Perhaps these principles were created to push us to the Edge. The Boredom principle makes us unhappy with the ordered realm for too long. The Rush principle lures us into Chaos. The Homing Principle pulls us back into the ordered realm. Each of these mechanisms is triggered contextually.
From where did the Boredom and Rush Principles come? Perhaps Mother Nature became bored with one million years of Homo erectus, and this boredom was manifested in the modern human.
Perhaps the neural networks, in assuming a life of their own, sent out messages of pain when they began to shrink. "Help! I'm shrinking!?" Perhaps consciousness itself needs to move to perceive itself. Perhaps consciousness created these mechanisms to further its existence by stimulating the organism to movement. Also pain and consciousness are linked. "Pinch me so that I know that I'm not dreaming." Perhaps the pain of boredom keeps consciousness, conscious. This study is interested in when life, especially humans, don't behave like rocks. We really don't know how these mechanisms arose, but we do know that they reflect and predict a whole lot of human behavior on very fundamental levels.