Anticipation Across Disciplines (M. Nadin, Ed.) Cognitive Science Monographs. Cham CH: Springer. Vol. 29, pp. 283-339. September 2015
Anticipation, a definitory characteristic of the living, is expressed in action. It implies awareness of past, present, and future, i.e., of time. Anticipatory processes pertain to the world’s dynamics. Anticipation also implies an observation capability, the acquired function of processing what is observed, and the ability to effect change. Computation means processing quantitative distinctions of physical entities and of those that inform the condition and behavior of the living. Autonomic processing is the prerequisite for anticipatory expression. In the physical, processing is reactive; in the living it is autonomic. Automated calculations, inspired by human “computers,” are different in nature from those involved in living dynamics. To distinguish between anticipatory and predictive computation is to account for the role of the possible future in dealing with change.
Algorithmic, Anticipation, Computer, Forecast, Prediction, Non-algorithmic, Turing
1 Theoretical Considerations
The context is clear: it was asserted (Mitchell ) that the world to which we belong is the outcome of computation (of quantum nature, in particular, according to Deutsch ). Consequently, to understand computation is a prerequisite for evaluating the message of concern regarding the long-term consequences of the increasing dependence on a particular machine, i.e., the computer, that humankind is experiencing. The broad view recalled above does not assuage the worry some express. But even if the hypothesis were to prove wrong, the dependence would not go away. In particular, artificial intelligence, together with the associated science of robotics, has prompted messages of doom: “The development of full artificial intelligence (AI) could spell the end of the human race” (Hawking ). Such messages are comparable, I hasten to add, to those euphoric forecasts (Kurzweil ) announcing an age in which machines will outsmart even those who conceived them. Shannon  went as far as to say that “I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” (As respectful as I am of Shannon, I am not willing to accept the leash.)
Of particular interest in this respect are achievements in the area of predictive computation and, related to it, in neural networks-based deep reinforcement learning competing with human-level control. The most common embodiment of these developments is mobile computing. What used to be wireless telephony became the hybrid platform of algorithmic computation, integrated with a variety of non-algorithmic processes, supported by a vast array of sensors. Machine learning affords the connection of data and meaning (e.g., position, activity, possible purpose, in other words: what, where, why). It produces information pertinent to the situation (e.g., a sales chart for a marketing meeting, a simulation for a class in Big Data visualization). Other “feats” make for spectacular headlines: the algorithm for playing video games that plays better than the living player for whom the games were conceived; and the algorithm for understanding language. These transcend Big Blue, a high-performance machine programmed to play chess, and which eventually became a successful contestant on the game show Jeopardy, and then a digital doctor. For this purpose, huge resources were made available to be thrown at the problem of beating a world champion (through brute force computation). The more recent claims are for game competence across the gamut of games, regardless of experience; and for understanding questions posed in everyday language, that is, the ability to answer them. High-dimensional sensory inputs (representations of the environment) drive deep network agents able to outperform humans. (In language understanding, deep reinforcement learning is the chosen path.) The video game playing “intelligent” machine knows nothing about the Atari games (of the early “romantic” age of video games). It was tested on 49 of them (Minh et al. ). It makes inferences from pixels from the game images and from game scores, that is, how others played. Of course, the game’s algorithmic nature itself is congruent with that of the artificial agent driven by high-dimensional sensory inputs.
In reference to understanding language (Weston et al. ), proxy tasks are set out in order to facilitate the evaluation of reading comprehension via the mechanism of answering questions. By no coincidence, a particular kind of games (text adventure connected to interactive fiction, Monfort ) provides a medium for categorizing various types of questions. Far from being only examples of successful programming and clever methods, these define a new frontier in computation. Implicit in the challenge is the question of whether human performance, anticipatory in nature, can be matched, or even outdone, by algorithmic forms of computation. Of course, the goal has to be defined as clearly as possible. To understand the significance of all this breakthrough research, we shall first define the underlying concepts involved.
1.2 What Is and What Is not Anticipation—A Question that Does not Go Away
Let us return to the issue of the human being’s progressive dependence on computers. Being part of a reality within which everything associated with human existence is, in one way or another, dependent on computers undermines the effort of a neutral evaluation. For example, this present text originates in a word-processing program. In the writing, the author used speech recognition and image processing, and benefited from machine learning-based search for references. The text will, along the academic path of publication (editing, peer review, additional feedback, layout, etc.) be made available—on paper, using digital printing, and in e-formats— to a readership shaped by the experience of computation to the extent that dependencies are established. References will be cross-linked, keywords highlighted; the text will become an easy-to-explore hypertext, all set to be further indexed and eventually fed into a complex network visualization. If the means of expression (language, formulae, images, etc.), communication (sharing), and signification (evaluation of originality, impact, usefulness over time, etc.) were passive, it would not make any difference that this text is not the outcome of orality, or of handwriting on parchment or paper, or of lead-based typography, or of the Gutenberg printing press. But the media involved are never neutral. Tools are not passive partakers in the activity. Being used within a culture, they “make” a new content, a new user, a new public—and thus contribute to the change of culture itself.
This is all the more important as we realize the ubiquity and diversity of computation. We understand that a new human condition is ascertained in the ever-expanding use of digital technology. Humankind might project itself into a way more exciting future than ever. Alternatively, it might wipe itself out (or at least place itself on a degenerative path), and thus eliminate humans from the dynamics of evolution, or at least diminish their influence. Computers that perform better in chess or programs that outperform the human in Atari games (or any other machine-based game), and computers capable of understanding and answering questions, are only indicative of the breadth of the process. What counts in the perspective of time is the depth of the process: how the mind and body change, how human pragmatics is redefined.
1.2.1 Winning, or Changing the Game
Having taken this broader view in order to establish a context, it is time to focus on the terms that frame the question we are trying to answer: anticipation and computation. Mitchell (or for that matter Wolfram  or Zuse ) claims that somehow the universe is being deterministically computed on some sort of giant but discrete computer (literally). If indeed all there is is an outcome of deterministic computation, then what is the rationale behind the fact that, in the world as we know it, some entities are alive and some not? Computation, itself grounded in rationality, ought to have an explanation for this, if indeed we are only its outcome (whether as stones, micro-organisms, or individuals who conceived computation). Living entities (from bacteria to the human being) come into existence at some moment in time, unfold in a dynamic driven by survival—including reproduction— and eventually die. As they do so, they join the non-living (water, chemical elements, dissipated energy, etc.), characterized by a dynamic driven by the forces at work on Earth and in the cosmic space Earth occupies (according to descriptions in astrophysics). Of course, sun and wind, humidity, a variety of particles and radiations, as well as interaction (local or galactic), affect stones and rivers, the air, and decomposition of dead organic matter as much as they affect the living. Experimental evidence shows that the dynamics of the living is, moreover, characterized by adaptivity. The non-living does not exhibit adaptivity—at least not at the timescale of those who observe them. What most radically distinguishes life from not-life is the sense of future, i.e., the vector of change. It is goal-driven change that explains why there is life, more so than the rather unsubstantiated, and therefore dubious, affirmation of a universal computation of almost deistic nature.
Between the living and the physical—which is subject to descriptions constituting a body of knowledge known as physics—there is a definite systemic distinction: the living is complex; the physical is complicated. To reproduce here the arguments upon which this epistemological model is based would probably invite attention to a subject different from that pursued herein. Suffice it to say, the criterion used is derived from Gödel’s  notion of the undecidable: entities of complex nature, or processes characterized as complex, cannot be fully and consistently described. The living is undecidable. This means that interactions implicit in the dynamics of the living cannot be fully and consistently described. As a consequence, no part of a living entity is less complex than the whole to which it belongs—unless it is reduced to a merely physical entity. The famous decerabrated frog experiment (described in almost all physiology books) is illustrative of this thought. The physical is decidable. A fragment of a stone is as much representative for the whole stone as the laws of physics are for the universe. Of course, Gödel referred to descriptions of reality (a nominalist view), to statements about it, to the logic guiding such statements, and, further, to operations upon them. We take the view (resulting in the definition of G-complexity, Nadin ), that the decidable/undecidable, as a gnoseological construct, defines states of complementary nature (physics vs. living). Anticipation is associated with the undecidable nature of the living. In the decidable realm, action-reaction entails change.
The most recent attempt to explain the emergence of life (England ) returns to the obsessive model of physics inspired by the laws of thermodynamics. If indeed capturing energy from the environment and dissipating that energy as heat were conducive to the restructuring of matter (carbon atoms, in particular), leading in turn to increased dissipation, the process would not have ended, and more such restructuring would take place. It does not take place on Terra, (i.e., our Earth) and nobody has yet documented it on other planets or cosmic bodies. That physics is fundamentally inadequate for explaining the emergence of life and, further, for explaining it, is a realization that physicists swore to ignore. Anticipation does not contradict the predicaments of physics, but complements them with an understanding of causality that integrates the future. The increased entropy (cf. the Second Law of Thermodynamics) of physical systems explains, to a certain extent, how they change over time. Physical information degrades. Reaction is the only remedy. In the living, we are faced with the evidence of long-term stability of species. Biological information (DNA is an example) is maintained as a condition of life. Entropy does not increase. Anticipation is the expression of the never-ending search for equilibrium in the living, and therefore its definitory characteristic.
It is easier to postulate (such as in the above text) and navigate a clean conceptual universe in which words mean exactly what we want them to mean (cf. Humpty Dumpty in Carroll ). To deal with the messy reality of complementarity—the living and the non-living—in which concepts are ill defined, implies awareness beyond the views that shaped civilization after Descartes. In respect to anticipation, such clarity—not only of semantic nature—is essential since the terminology permeates the pragmatic level, in particular in the language domain associated with computation.
When machine performance (of the computer or of any other device) is juxtaposed to that of the human—machines outperforming world champions in chess or game fanatics in 49 Atari games—one has to define the criteria. The same applies for understanding language. On account of anticipation, humans answer questions even before they are posed. Indeed, the knowledge used in such performance is as important as understanding the difference between repetitive tasks and creativity. Algorithms that integrate better prediction models in data-processing characteristic of playing games (or any other form of algorithmic expression) or understanding questions, together with high-speed processing, will outperform the human being to the extent that the “self-driving” car will outperform the human-driven car. But the real problem is “Will they generate new games, or new instances of competitive dynamics? Will they generate, as human do, new language, within which new ideas are seeded?” This is where anticipation comes into the picture. Winning and changing the game are two sides of a coin about to be flipped. Making way for new language is part of the continuous remaking of the individual.
1.2.2 Reaction and Anticipation
Anticipation pertains to change, i.e., to a sense of the future. The image (Fig. 1) is suggestive.
To comment on the particular words would only result in anecdotal evidence. First, let us clarify some of the terms. Foremost in importance is the understanding that the physical is defined through interactions driven exclusively by reaction. The physics of action-reaction, as formulated in Newton’s Third Law  provides a decidable model:
Lex III: Actioni contrariam semper et Ã¦qualem esse reactionem: sive corporum duorum actiones in se mutuo semper esse Ã¦quales et in partes contrarias dirigi.
(Translated to English, this reads: Law III: To every action there is always opposed an equal reaction: or the mutual actions of two bodies upon each other are always equal, and directed to contrary parts.)
If body A exerts a force F on body B, then body B exerts an equal and opposite force âˆ’F back on body A.
FAB = —FBA
The subscript AB indicates that A exerts a force on B, and BA indicates that B exerts a force on A. The minus sign indicates that the forces are in opposite directions. Often FAB and FBA are referred to as the action force and the reaction force; however, the choice of which is which is completely arbitrary. Elementary particles, as much as physical entities (at the scale of our immediate reality or at cosmic scale), behave as though they follow the law. Within the living, this is no longer the case. Cells, in their infinite diversity, have a dynamics for which the description in Newton’s laws are no longer applicable.
Descriptions of entities and processes restricted to the dynamics originating in action-reaction can be fully and non-contradictorily described. As stated above, this applies across the reality of physics—from the micro-universe to cosmic space.
1.2.3 The Undecidable “Falling of the Cat”
The living, like the physical, is embodied in matter; hence, the reactive dynamics is unavoidable. However, the physical dynamics—i.e., how change of matter takes place—of what is alive is complemented by the anticipatory dynamics—how the living changes ahead of what might cause its future condition. Newton’s laws, like all laws anchored in the deterministic cause-and-effect sequence (past?present?future), preempt the goal-driven changes ahead of material causes. Awareness of change, pertaining to the living, is reactive. The living reacts (to changes in temperature, to stimuli, to other people, etc.), but at the same time, it is also anticipatory (preparedness, as well as foresight, for instance). Adaptivity over shorter or longer intervals is the specific expression of this interplay. It also explains the long-term stability of the living. From a physics perspective, the following would appear as unavoidable: A stone and a cat of equal weight fall (Fig. 2), regardless of the moment in time, and even regardless of the measuring process, acceding to Newton’s law of gravity. But the stone falls “passively”—along the path of the gravitational force. The cat’s fall is suggestive of anticipation. It is expressed in action; and it is meant to preserve life (the cat usually avoids getting hurt).
The equation of the “change”—coordinates (falling from height h) in this case— is straightforward:
h = ½gt2
in which h is the falling height, g the acceleration due to gravity, and t the falling time. If, for example, the height is given by h = 10 m and g = 9.81 m/s2, the predicted falling time is obtained by inserting these values in (2) and solving for t. Introducing the variable T for the falling time and the function At for the prediction procedure yields the following predicted event, which consists of one element only:
This description omits some variables related to air resistance (object’s shape, air density, effect of temperature and humidity, etc.).
The cat falls “actively.” The cat’s response to falling (even if the fall is accidental, i.e., not caused within an experiment) is at least a change in geometry: actively turning (by triggering the motoric) increases the surface, and thus air resistance. The equation pertinent to the fall of the stone still applies, but in a rather approximate way (more approximate than considering friction). The living “fights” gravity. (The metaphor is a mere translation of the fact that nobody likes to fall.) The past (cat’s position at the start of the fall), but also the possible future (how and where to land) affect the outcome.
1.2.4 The Living Can Observe the Physical
The fall of the same stone, repeated, from the same position, is captured in the physical law description: air resistance can be precisely accounted for and, even under experimental conditions, maintained. The gravitational field strength (9.8 N upon every 1 kilogram) is a characteristic of the location within the Earth’s field of gravity, not a property of the falling stone or cat. The nomothetic (Windelband ) corresponds to a description of a phenomenon (or phenomena) characterized as law. The fact that mathematicians extend the nomothetic description to the falling cat is testimony to their incomplete knowledge. It does not include anticipatory dynamics. Indeed, the cat, as opposed to the stone, will not fall the same way twice. Mathematicians, like many others, scientists or not, observed the fact mentioned above.
But since the cat’s falling became a mathematical problem, let’s take a closer look at what is described. The purpose is simple: that we understand why the “recipe” for calculating the parameters of physical phenomena cannot be extended to predictions of living processes. Prestigious scientists (such as George Gabriel Stokes [1819–1903], James Clark Maxwell [1831–1879], and Etienne Jules Marey [1830–1904]) were tempted to explain the falling of cats, more precisely, how they turn in the air.
For them and their followers, the falling cat problem consists of explaining the physics underlying the common observation of the “cat-righting reflex.” To discover how a free-falling cat can turn itself right side up as it falls—no matter which way up it was initially, without violating the law of conservation of angular momentum—is a challenge. There is one limitation: all that counts is that the cat fall on its legs. As a leading mathematician in the falling cat problem puts it:
Although somewhat amusing, and trivial to pose, the solution of the problem is not as straightforward as its statement would suggest, leading towards surprisingly deep mathematical topics, including control theory for nonholonomic systems, optimal motion planning, Lagrangian reductions, differential geometry, and the gauge theory of Yang-Mills fields .
Within this study, we will not go into the details of all of the above. In broad strokes: applied differential geometry allows for the approximate description of an object flipping itself right side up, even though its angular momentum is zero.
In order to accomplish that, it changes shape (no stone changes shape in the air). In terms of gauge theory, the shape-space of a principal SO(3)-bundle, and the statement, “Angular momentum equals zero,” defines a connection on this bundle. The particular movement of paws and tail conserves the zero angular momentum. The final upright state has the same value. This is the “geometric phase effect,” or monodrony.
The idea is simple: Let a cat fall; and derive the pertinent knowledge from the experiment. (In 1882, Marey used a chronophotographic gun for this purpose; in our days, motion capture equipment is used.) But this is no longer a reproducible event. It is not the passive fall of a stone—reproducible, of course—but the active fall embodying anticipation. The outcome varies a great deal, not the least from one hour to another, or if the landing topology changes. The stone will never get tired, annoyed, or excited by the exercise. And it will never learn. We shall explain this, in reference to the cat’s fall, using images (Figs. 3 and 4).
The cat’s shape is given by two angles: (Î˜) (Ïˆ).
Ïˆ is the angle between the two halves of the cat’s body.
Î˜ describes the direction of the cat’s legs (Î˜ = 0 when the front and back legs
are closest to each other).
A change in Î˜ corresponds to a rotation of the cat’s body around the \spinal axis.
Heisenberg’s uncertainty relation  suggests that, although such descriptions are particularly accurate, we are, in observing the falling of a cat, not isolated viewers, but co-producers of the event. To observe entails influencing the result. The falling of human beings, of consequence as we advance in age, makes it clear that “to know how to fall” (as the cat obviously does) is more than a problem in physics or a mathematical exercise. (No kitten should be subject to such an experiment.)
Just as an aside: inspired by the cat’s fall, Apple, Inc. patented a method for controlling the accidental fall of the iPhone on its precious screen. The iPhone’s vibration motor (Fig. 5) is programmed to change the angle of the fall in mid-air. This change is based on data from the device’s positioning sensors. The patent is, in its own way, an example of engineering inspired by the expression of anticipation in the living. It is based on knowledge from physics (coordinates and center of gravity) and takes predictions based on Newton’s laws in order to activate the vibration motor so that the device is turned in the air—pretty much like a cat made out of stone or wood.
With this device, we are in the reaction domain, taking advantage of a good understanding of physical laws.
to why cats fall on their feet. (Reproduced from )”]
The unity reaction-anticipation—characteristic of the living—corresponds to a different condition of matter and its change over time. The measurement process, i.e., the observation of the change (the falling cat), influences the outcome. Our watching how the smartphone falls does not affect the process.
Thesis 1: The living can observe the physical.
Actually, as it evolves, it continuously does—because the process is affected by the context. The physical does not have an observation capability. It is rather a stage on which the living performs (while it also reshapes the stage). Perception is nothing more than the process through which awareness of here and now is established.
Thesis 2: Awareness of immediate space and time is the outcome of perception processes.
1.3 Expectation, Guessing, Prediction
We become aware of anticipation when it is successful: falling the “right way,” avoiding danger, rewarding creative activities, competence in competition, understanding language, images, and textures, for example. From such activities, we can generalize to processes in which anticipation is sometimes involved, and also to other forms of dealing with the future. From guessing and expectation to prediction and planning as human endeavors, we infer that reaction and anticipation are not reciprocally exclusive, but rather intertwined. Acknowledged in language (and in experiments) are various forms of what is called premonition (of danger, usually), foretelling (mostly associated with the dubious commerce of “seeing into the future”), not to mention curses and blessings, and voodoo. There is no reason to go into these; although for those fixed on the notion of algorithm—description of actions through which a goal is attained—they can be given as examples of processed information to which misinterpretation also belongs. Each of the above-mentioned aspects (including the slippery practices) is a combination of reaction and anticipation. Just as an innocent illustration: The fortuneteller reacts to someone’s need for reassurance or comfort, creating the illusion of a successful (or unsuccessful) anticipation, “You will not be awarded the Nobel Prize, but your love life will improve.”
To guess is to select from what might happen—a sequence of clearly defined options—on account of various experiences: one’s own; of others; or based on unrelated patterns (the so-called “lucky throw” of a coin or dice, for example).
Guess ? selection from a well-defined set of choices
If you have to guess a number from one to one hundred, the first thing is to reduce the space of choices.
The reaction component (“I know that the person asking the question likes the queen of hearts!”) is based on real or construed prior knowledge. For an anticipatory dimension, one would have to combine a wager: “Guess what number I chose” (or what card, or what word) “and you win!” (See Fig. 6). A generalization can be made: when reaction and anticipation are suggested, the outcome will show that some people are better at guessing than others because of heightened perception of cues of all kind.
Reactions are based on the evaluation of the information pertinent to the situation—different when one visits a fortuneteller or a casino from when one guesses the correct answer in a multiple-choice test. In the multiple-choice situation, one infers from the known to the unknown. When patterns emerge, there is learning in guessing: the next attempt integrates the observation of related or unrelated information. This associative action is the cognitive ingredient most connected to guessing and, at the same time, to learning. (We retain patterns and recall them when faced with choices.) The rest is often statistics at work, combined with ad hoc associative schemes pertinent to what is possible. (That’s where premonition, mentioned above, comes into play.) Let us acknowledge that guessing (as well as learning) is reduced to nil in predictable situations. The anticipation component of guessing is related to the state of the self. From all possible games in the casino, some are more “favorable” at a certain time, something like: “Guess who’s knocking at the door,” after grandma’s voice was heard. Only surprise justifies the effort, even when the result is negative. Recent research of responses of the human frontal cortex to surprising events (Fletcher et al. ) points to the relation to learning mentioned above. The dorsolateral prefrontal cortex contributes to the adjustment of inferential learning. Associative relationships (Fig. 7) that lead to learning (also qualified as associative) are based on the action of discriminating the degree (strength) of interrelation. Of course, fuzzy sets are the appropriate mathematical perspective for describing such interrelations.
) degree (strength) of interrelation. Of course, fuzzy sets are the appropriate mathematical perspective for describing such interrelations.”]
Empirical data (statistics, actually) document “better days,” i.e., above-average guessing performance. This corresponds to a variety of circumstances: additional information about the process (acquired consciously or, most of the time, through processes “under the radar”), a state of cognitive or sensorial alertness (for whatever reason), or simply a statistical distribution (“lucky”), to name only a few. There is no magic in the exceptional (a “good” day, “bad luck”), but there is quite a bit to consider in terms of the large number of variables involved in the outcome of human actions. The manner in which anticipatory action is intertwined with the reactive is difficult to describe exactly because of the multiplicity of factors involved. If anything, anticipation actually undermines success in guessing, given its non-deterministic nature; it integrates the subjective, the emotional, the spontaneous. A guessing machine—computer or any other type of machine—can automate the guessing knowledge specific to well-defined selection and thus outperform the guessing living (not only human beings are involved in guessing as they face change). Machine learning provides a good basis for such applications. The algorithm for successfully playing computer games is based on data acquired through what is called deep reinforcement learning. The algorithm for understanding questions formulated in natural language is based on a multilinear map subjected to processing in Memory Networks (MN).
In comparison to guessing, expectation does not entail choosing (“Heads or tails?”), but rather an evaluation of the outcome of some open-ended process. An example: A child’s expression is informative of what might happen when the child will “hang out” with friends. The parents’ evaluation might be difficult, if at all possible, to describe (e.g., “I know what you guys plan to do”), that is, to formalize. In the act of forming an expectation (such as in carrying out experiments), the focus on the reaction component changes from the probable (which number from the set defined?) to the inferred. Several sources of information pertinent to forming an expectation are weighed against each other. What appears most probable out of all that is possible gets the highest evaluation, especially if its outcome is desirable (for instance, pleasant weather preferred over the expectation of rain). Expectations associated with experiments are usually in the area of confirming a hypothesis or someone else’s results. If the outcome is judged to be negative, then avoiding it is the basis for action. Again, anticipation—reflected in what is perceived as possible —meets reaction, and information is associated with probable cause. Weather is often expected—inferred from opinion, observation, data—not guessed. So are the outcomes of activities that weather might influence. Agriculture practiced prior to the integration of digital information in agricultural production was often in the realm of the expected. A cornfield is not equally fertile in every spot. Learning how to increase production by extracting data (through GPS-based measurements) pertinent to fertility and applying fertilizers or planting more seeds in certain spots grounds expectation in knowledge—and thus makes it look like an algorithm (a set of rules which, if respected, can yield a result). Based on the evaluation of the outcome, new expectations are generated. Events with a certain regularity prompt patterns of expectation: a wife awaits her husband, a child awaits a parent, a dog awaits its owner, who usually returns from work at a certain time. Such regular events are encountered on many occasions and in many activities.
Expectation ? evaluation of outcome based on incomplete knowledge (from a limited set of probabilities)
P(p1, p2, . . .pn)
An expectation machine is actually a learning procedure that attaches weights (some subjective) to choices from the limited set of possibilities. The reactive component dominates the anticipatory. False expectations (of personal or group significance) are the outcome of skewed evaluations. Expectation and superstition are examples of such evaluations. They are driven more by desire or wishful thinking that tends to falsify the premise (adding self-generated data to the factual incomplete knowledge).
Among the cognitive illusions (Kahneman and Tversky  existing in culture are those formed by gamblers (Delfabbro , as well as by a professional acting in a state of over confidence. Physicians making inferences based on limited medical tests (Gigerenzer and Gray , Sedlmeier and Gigerenzer ); coaches captive to the “hot-hand” model (Tversky and Gilovich , Miller and Sanjurjo ; economists absorbed in data patterns more relevant to the past than applicable to future developments (Hertwig and Ortmann ) can be given as examples. These have in common the perception of random and non-random events. Statistically significant deviations from the expected (e.g., the average scoring performance of a gambler in a casino, of a basketball player, of the stock market, etc.) lead to beliefs that translate into actions (a gambler can be refused entry, the basketball player believed to have a “hot-hand” day faces a stronger defense, hot stock market days mean more trades, i.e., more speculation, etc.). Physicians interpret deviations in respect to expected values (blood glucose, cholesterol, vitamin D, creatinine), and automatic procedures (comparison with average values) trigger warnings. What we get after a blood test, for example, is an expectation map. Guessing and expectation, each in its own way, are meant to inform choices or result in decisions. Positive and negative factors weigh in with every option. The integration of biases in making the choice leads to the surprising observation that what some call instinct (choose among options in the absence of identifiable previous knowledge, in common parlance, “gut feeling”) can explain successful guesses or actions driven by expectation —such as which direction to take at a fork in the road.
Connecting cause and effect, i.e., associating data generalized from statistical observations describing their connection, is the easiest way to characterize prediction. Causality, as the primary, but not exclusive, source of predictive power is rarely explicit. Prediction—explicit or implicit—expresses the degree of ignorance: what is not known about change. Uncertainty is the shadow projected by each prediction (Bernoulli ). Therefore, it is representative of the limits of understanding whatever is predicted. In some cases, the prediction is fed back into what we want to predict: how a certain political decision will affect society; how an economic mechanism will affect the market; how technological innovation (let’s say multimedia) will affect education. As a result, a self-referential loop is created. The outcome is nothing more than what is inputted as prediction. Those who predict are not always fully aware of the circularity inherent in the process. The impossibility of disconnecting the observer (the subject, in learning) from the observed (the object of learning) is an inherent condition of learning, whether human or machine learning. The constructivist perspective demonstrated the point quite convincingly (von Glasersfeld ).
Prediction ? inference based on probability
Prediction machines of all kind are deployed in situations in which the outcome is associated with reward/punishment (loss). In particular, Bayes-inspired prediction is driven by a hypothesis: You “know” the answer, or at least part of it (your best guess). Predictions of election results, of weather patterns, of sports competitions are based on such assumptions. Prediction as a process that describes the outcome of action-reaction dynamics can be usefully affected by experiential evaluations.
1.3.4 Future States and the Probability Space
But there are also predictions driven, to an extent larger than the Bayesian state of belief, by anticipatory processes, involving the probability space also. Falling in love at first sight—which is neither guessing nor expectation—is a prediction difficult to make explicit. (It combines rationality and consistency with a subjective perspective such as the above-mentioned “gut feeling.”) There is no explicit cause-and-effect connection to uncover, and no frequencies to account for. The future state (the romantic ideal of a great love, or the calculated outcome of an arranged marriage) affects current states as these succeed each other in a sequence of a time often described as “out of this world.” We could add the dopamine release during anticipation of a musical experience (Salimpoor et al. ). Peak emotional responses to music are different from the experience of winning a computer game, or answering a question. Therefore, it would be inadequate to even consider an algorithm for returning the value of musical experience based on statistical data. Machine-based performance (such as winning games, or understanding questions) corresponds to different domains of computation.
Facial expression as a predictor is yet another example of Bayesian probability-based inferences. In very sophisticated studies (Ekman and Rosenberg , Ekman ), it was shown that the “language” of facial expression speaks of facts to happen before they are even initiated—which is anticipation in pure form. The Facial Action Coding System (FACS), which is a taxonomy of facial expression (and the associated emotional semantics), inspired Rana El Kalioubi in her work on computationally interpreting the language of faces. For those who “read” the face’s expression, i.e., for those who learned the language of facial expression, the emotion predictions based on their own anticipation guides their action. Gladwell  describes the case of a Los Angeles policeman who reads on the face of the criminal holding a gun on him that he will not shoot, leading the officer to avoid shooting the criminal. The expectation—criminal pulls out gun and points it at the policeman pursuing him—and the prediction—this person with the particular facial expression, as studied by the interpreter, will not shoot—collide. So do the probability of being shot and the prediction informed by knowledge otherwise not accounted for.
Descriptions of the relation between expectation and prediction are informative in respect to the mechanisms on which both are based. The various levels at which learning—different in expectation-driven from prediction-based decisions—takes place are not independent of each other. Expectations pertain to more patterned situations: e.g., “I expect to be paid for my work based on our agreement.” (The intitial state d, to be hired; the future state x, to be paid for work, depends on initial state d.) The prediction, “Based on the record of your employer, you will be paid,” conjures different data. An acceptable description is that the learner extracts regularities or uses innate knowledge. They are often an expression of what in ordinary language is described as stereotype or, in some cases, wishful thinking. However, when the individuals become involved in the activity of predicting (literally, “to say beforehand,” i.e., before something happens), they expect the prediction to actually take place. It is no longer a wish, but rather the human desire, expressed in some action, to succeed.
Many activities, from policing the streets to conceiving political reform, urban development, military strategy, educational plans (to name a few areas of practical activity with features with little or nothing in common) are informed by the very competitive “industry” of predictions. Generalizing from the past can take many forms. Sensor-based acquisition of data provides in algorithmic computation the simuli of learning through experience. Evidently, the focus is on relationships as a substratum for deriving instructions pertinent to the present and of relevance to the future. Ignorance, which is what probabilities describe, is fought with plenty of data. The typology of predictions (linear, non-linear, statistical inference, stochastic, etc.) corresponds to the different perspectives from which change and its outcome are considered. At the processing level, extraction of knowledge from data makes available criteria for choices (such as those in spatial navigation, playing games, choosing among options, etc.).
Change means evolution, variability over time. Predictive efforts are focused on understanding sequences: how one step in time is followed by another. However, these efforts focus on what, ultimately, anticipatory processes are: a modeling of the entity for which they are an agency, and the execution of the model in faster than real time speed. The limited deterministic perspective, mechanic in nature, repetitive—i.e., what cause leads to which ensuing effect—affects the understanding of anticipation through a description of predictive mechanisms. Predictions made following known methods (such as time series analysis and linear predictors theory) capture the reaction component of human action (Arsham ). The anticipatory component is left out most of the time, as a matter of definition and convenience. Complexity is difficult to recognize, and even more difficult to handle because it corresponds to open-ended systems. Once a predictive hypothesis—let’s say every minute the clock mechanism engages the minute hand—is adopted, it defines the cognitive frame of reference. On a digital display, the predictive hypothesis will be different. Should the predicted behavior of the mechanism somehow not take place, expectation is tested. However, mechanisms, as embodiments of determinism, rarely fail. And when they do, it is always for reasons independent of the mechanism’s structure.
1.3.5 Learning and Expectation
Predictions concerning the living are less obliging since interactions are practically infinite. Structure matters, interdependencies are fundamental. It happens at all levels of the living that predictions—what will happen next (immediate or less immediate future)—are either partially correct or not at all. In studying learning and selective attention, Dayan et al.  refer to reward mechanisms in the Kalman filter model (more experience leads to higher certainty). For any process in progress —e.g., moving a vehicle, recalling a detail in an image, thinking something out— there are, from the perspective of the Kalman filter, two distinct phases: 1) predict; 2) update. The filter is a recursive operation that estimates the state of a linear dynamic system. In physical entities, the space of observable parameters is smaller than that of describing the degrees of freedom defining the internal state. In the living, the situation is reversed. Learning, for instance, triggers expectations that turn out to be a measure of how much the deterministic instinct (culture, if you prefer) takes over the more complex model that accounts for both reaction and anticipation in the dynamics of the living.
Predictors reflect the desire to understand how change takes place. They express the practical need to deal with change. However, they omit change from the equation of those predicting or subject to prediction. Actions from thoughts, as Nicolelis  calls them, account for the self-awareness of change. What is learned supports inferences (statistical or possibilistic); uncertainty results as the competitive resources engaged in the inference are overwritten by unrelated factors. Predictions also capture the interconnectedness of all elements involved in the dynamics of the observed. Learning involves predictions. In this sense, they open access to ways to emulate (or imitate) change.
Expectations have no direct learning component. One cannot learn explicitly how to expect, even accepting that there might be structure in the learning process after an expectation is validated, and in the representation associated with the expectation. Expectations only occasionally produce knowledge: a series of expectations with a certain pattern of success, or failure for that matter. Predictions, even when only marginally successful, support activities such as forecasting—for short or less than short sequences of change—of modeling, and of inference to the characteristics of the observed dynamic entities.
For learning (prerequisite to prediction and to anticipation) to come about, representations of the dynamic process have to be generated. Some will correspond to the immediateness of the evolving phenomena—what the next state will be, how the phenomena will evolve over time—others involve deeper levels of understanding. Whether in medicine, the economy, politics, military actions, urban policy, or education, etc., predictions or anticipations emerge on account of considerations regarding cascading amounts of data. Just to genralize, we can consider the ever-increasing amount of sensors deployed as the source of this data. Integrated sensors generate high-level, multi-dimensional representations. Their interpretation, by individuals or intelligent agents, emulates the machine model of neuronal activity. As a consequence, we end up with algorithmic computation, extremely efficient in terms of generalizing from past to present. The so-called deep Q-network agent, which has as output “human-level control” performance (in playing games, but applicable as well to other choice-making situations), is the embodiment of prediction based on reinforcement learning .
Without the intention of deriving full-fledged conclusions, an example could suggest the interrelated nature of expectation, guessing, and prediction. The painful revelation of the practice of torture associated with the “war on terror” (a very misleading formula) prompted discussions that ranged from the moral, aesthetic, medical, to political, and ultimately focused on how successful torture is in extracting useful information. Data of all kind, from anecdote to statistics (perversely kept by those regimes that for centuries have practiced torture, some methodically, some as circumstances deemed necessary) document both the efficiency of brutal treatment of prisoners and the possibility of collecting misinformation. The process is non-deterministic. Moreover, principles of conduct—some by tacit agreement (what is hateful to you, don’t do to others), others codified by the community of nations— associated with extreme treatment of the adversary, set moral borderlines (some less clear than they should be). Still, contrary to this foundation on data and rules, the practice continues. (Those on whose behalf torture is employed tend to find justification for it, since they form the notion that it has served them well.)
Prediction-data show that torture occasionally begets information. A torture information production machine—i.e., a computer, or better yet, a robot, with the applicable moral constraints built in—would decide on a cost-benefit analysis model whether torture should be applied or not. In retrospect, it is evident that guessing would be a weak description of the future: it has the highest margin of error. Expectation would not be much better: a machine does not output expectations since their variability escapes algorithmic descriptions. Predictions, especially in the Bayesian sense, are more effective. According to the Report of the Senate Intelligence Committee on the CIA Counter-Terrorism Program, some cases of torture were doomed from the outset.
Of course, the above description pertains to the macro-level. The interrogator and the interrogated are actually in an anticipatory situation: winning or losing drives their actions. Guessing, expectation, and prediction meld as they do in hide-and-go-seek, in playing tennis, in poker. In view of this observation, the understanding of prediction (implicit in guessing and expectation) takes on new meaning.
Predictions regarding the living, although inappropriate for systematically capturing their anticipatory dimension, are a good indicator of what is lacking when anticipation is ignored. An example: in focusing only on human beings, predictions based on physiological data remain at a primitive stage at best, despite the spectacular progress in technology and in the scientific theory of prediction. Streams of data (from a multitude of sensors) in association with some analytical tool (data-mining, usually) could, of course, help identify where and how the physical component of life is affected by change (aging, environment, medical care, hygiene, alimentation, driving, etc.). The reactive component of what is called “health” is of extreme importance. Clogged arteries, degradation of hearing or of the eyes can be identified with the help of real-time monitoring of blood pressure, hearing, or the macula. But they remain partial indicators. In evaluating change in the condition of the living, of the human being, in particular, what counts are not only the parts under observation, but their interconnectedness, especially of the whole.
Reaction is reductive. Anticipation is a holistic expression. Albeit, if we could improve such predictions by accounting for the role of anticipation—the possible future state influencing, if not determining, the current state—we would be in a better position to deal with life-threatening occurrences (strokes, sudden cardiac death, diabetic shock, epileptic seizure, etc. (Nicolelis and Lebedev ). Learning (i.e., deep reinforcement learning) about such occurrences in ways transcending their appearance and probability is one possible avenue. Things are not different in the many and varied attempts undertaken in predictions concerning the environment— the well-known climate change issue, for example—education, market functioning. It is easier, when addressing a given concern, to deal with “recipes” (e.g., reduction of CO2 emissions as a solution to climate change, with its reductionist focus, to the detriment or exclusion of other variables, either ignored or opportunistically downplayed), than to articulate an anticipatory perspective, holistic by definition.
Unless and until anticipation is acknowledged and appropriate forms of accounting for it are established, the situation will not change drastically. Neither will medical care, environmental policies, political matters, or education change, no matter how consequential their change (if appropriate) could be. Physical processes have well-defined outcomes; living processes have multiple outcomes (some reciprocally antagonistic.) This aspect becomes even clearer when we look at the very important experiences of forecasting and planning. Policies, i.e., social awareness and political action, depend on forecasts and involve responsible planning, liberated from the influence of opportunistic interests.
1.3.7 Forecasting and Planning
Predictions, explicit or implicit, are a prerequisite of forecasting. The etymology points to a pragmatics, one that involves randomness—as in casting. Under certain circumstances, predictions can refer to the past (more precisely, to their validation after the fact). Take a sequence in time—let’s say the San Francisco earthquake of 1906—and try to describe the event (after the fact). In order to do so, the data, as registered by many devices (some local, some remote) and the theory are subjected to interpretations. The so-called Heat-Flow Paradox is a good example. If tectonic plates grind against one another, there should be friction and consequently heat. This is the result of learning from physical phenomena involving friction. Along the well-known San Andreas Fault, geologists (and others) have measured (and keep measuring) every conceivable phenomenon. No heat has been detected. The generalization from knowledge regarding friction alone proved doubtful. Accordingly, in order to maintain the heat dissipation hypothesis as a basis for forecasting, scientists started to consider the composition of the fault. This new learning—extraction of regularities other than those pertaining to friction and heat dissipation—was focused on an aspect of friction initially ignored. A strong fault and a weak fault behave differently under stress, and therefore release different quantities of heat. This is a case in which data is fitted to a hypothesis—heat release resulting from friction. To adapt what was learned to a different context is frequently used in forecasting.
In other cases, as researchers eventually learned, what was measured as “noise” was treated as data. Learning noise patterns is a subject rarely approached. Procedures for effectively distinguishing between noise and data are slow in coming, and usually involve elements that cannot be easily identified. In medicine, where the qualifiers “symptomatic” vs. “non-symptomatic” are applied in order to distinguish between data and noise, this occurs to the detriment of predictive performance. The lawsuit industry has exploited the situation to the extent that medicine is becoming defensive at a prohibitive cost (or overly aggressive, through the variety of surgical interventions, for instance, at an even higher price).
In general, theories are advanced and tested against the description given in the form of data. Regardless, predictions pertinent to previous change (i.e., descriptions of the change) are not unlike descriptions geared to future change. In regard to the past, one can continue to improve the description (fitting the data to a theory) until some pattern is eventually discerned and false knowledge discarded. (Successive diet plans exemplify how data were frequently fitted to accommodate the pharmaceutical industry’s agenda, sometimes to the detriment of patient health.)
To ascertain that something will happen in advance of the actual occurrence— prediction (the weather will change, it will rain)—and to cast in advance—forecast —(tomorrow it will rain) might at first glance seem more similar than they are. A computer program for predicting weather could process historic data: weather patterns over a long time period. It could associate them with the most recent sequence. And in the end, it could come up with an acceptable global prediction for a season, year, or decade. In contrast, a forecasting model would be local and specific. The prediction based on “measuring” the “physical state” of a person (how the “pump,” i.e., heart, and “pipes,” i.e., blood vessels, are doing, the state of tissue and bone) can be well expressed in such terms as “clean bill of health” or “worrisome heart symptoms.” But it can almost never become a forecast: “You will have a heart attack 351 days from now;” or “In one year and seven hours, you will fall and break your jaw.” Or even: “This will be a historic storm” (the prediction, so much off target, of the “Nor’easter” of January 2015).
Forecast ? infer from past data-based predictions to the future under involvement of self-generated data
Forecasts are not reducible to the algorithmic machine structure. They involve data we can harvest outside our own system (the sensorial, in the broadest sense). The major difference is that they involve also data that human beings themselves generate (informed by incomplete knowledge or simplified models). The interplay of initial conditions (internal and external dynamics, linearity and non-linearity, to name a few factors), that is, the interplay of reaction and anticipation, is what makes or breaks a forecast.
To summarize: forecasting implies an estimation of what, from among few possibilities, might happen. The process of estimation can be based on “common knowledge” (“Winds from the west never bring rain”); on time series; on data from cross-sectional observation (the differences among those in a sample); or on longitudinal data (same subject observed over a long time). Evidently, forecasting is domain specific. Meteorology practices forecasting as a public service; commerce needs it for adapting to customer variability of demand. Urban planners rely on forecasting in order to optimize municipal dynamics (housing, utilities, traffic, etc.). The latter example suggests a relation between forecasting and planning. How change might affect reality in comparison to how change should affect reality distinguishes forecasts from predictions.
Predictions are based on the explanatory models (explicit or not) adopted. Forecasts, even when delivered without explanation, are interpretive. They contain an answer to the question behind the forecasted phenomenon. “The price of oil will change due to….” You can fill in the blank as the situation prompts: cold winter, pipeline failure, war. “Tomorrow at 11:30 AM it will rain….” because of whatever brings on rain. “There will be a change in government….” “Your baby will be born in the next two hours.” A good predictive model can be turned into a machine— something we do quite often, turning into a device the physics or chemistry behind a good prediction: “If you don’t watch the heat under the frying pan, the oil in it will catch fire.”
Our own existence is one of never-ending change. Implicit in this dynamic condition of the living are:
(a) the impossibility of accurate forecasting, and
(b) the possibility of improving the prediction of physical phenomena, to the extent that we can separate the physical from the living.
Our guesses, expectations, predictions, and forecasts—in other words, our learning in a broad sense—co-affect human actions and affect pragmatics. Each of them, in a different way, partakes in shaping actions. Their interplay makes up a very difficult array of factors impossible to escape, but even more difficult to account for in detail. Mutually reinforcing guesses, expectations, predictions, and forecasts, corresponding to a course of events for which there are effective descriptions, allow, but do not guarantee successful actions. Occasionally, they appear to cancel each other out, and thus undermine the action, or negatively affect its outcome. Learning and unlearning (which is different from forgetting) probably need to be approached together. Indeterminacy can be experienced as well. It corresponds to descriptions of events for which we have insufficient information and experience, or lack of knowledge. They can also correspond to events that by their nature seem to be ill defined. The living, in all its varied embodiments, reacts and anticipates. Of course, this applies to every other living form. The reaction-anticipation conjunction defines how effective the living is in dealing with change.
1.4 Self-awareness, Intentionality, and Planning
The human being has a distinct condition in the extraordinarily large realm of the living. It doesn’t only play games (the example chosen in advanced research of high levels of control), but also conceives them. It not only understands questions in a given language, but also changes the language according to the human’s changing pragmatic condition. Moreover, the human depends on a variety of other forms of living (billions of bacteria, for instance, inhabit the body), but in the larger scheme of things, it acquired a dominant position (not yet challenged by the technology created). In our world, human activity (although often enhanced through science and technology) is, for all practical purposes, the dominant force of change. Humans “are what we do” (the pragmatic foundation of identity, [41, pp. 258ff], ). The only identifier of human actions (and of other living entities) is their outcome. This is an instantiation of identity at the same time. The question, “What do you do?” cannot be answered with “I anticipate,” followed, or not, by an object, such as “I anticipate that an object will fall,” or “I anticipate my wife’s arrival,” or “I anticipate smelling something that I never experienced before.”
Anticipation is a characteristic of the living, but not a specific action or activity. Humans do not undertake anticipation. The dopamine release in anticipation of high emotional anticipation (associated with sex, eating, music, scientific discovery, for example) is autonomic. Humans are in anticipation. Anticipation is not a specific task. It is the result of a variety of processes. As an outcome, anticipation is expressed through consequences: increased performance (an anticipated tennis serve is returned); danger (such as a speeding car) is avoided; an opportunity (in the stock market, for instance) is used to advantage. Anticipatory processes are autonomic. Implicit in the functioning of the living, such processes result in the proactive dimension of life. This is where identity originates. Anticipatory processes are defined in contrast to reaction, although they often imply reaction as well. Playing a computer game—with the game “canned on the machine,” or competing with someone via the medium of the game or a MMORPG (massively multiplayer online role-playing game)—over the internet can be reactive (with a predictive component) or anticipatory. It can also be random. Characteristic of the deterministic sequence of action-reaction defined in physics, reaction is the expression of the living’s physical nature. Identity is expressed in the unity of the reactive and proactive dimensions of the human being. It appears as a stable expression, but actually defines change. It is the difference between what we seem to be and what we are becoming as our existence unfolds over time. Identity is affected by, but is not the outcome of, learning.
No matter what humans do, the doing itself—to which explicit and implicit learning belongs—is what defines the unfolding identity. The outcome is the expression of physical and intellectual abilities. It also reflects knowledge and experience. The expression of goals, whether they are specifically spelled out or implicitly assumed, affects the outcome of actions as well. The process through which existence is preserved at the lowest level—as with the phototropic mono-cell and progressing all the way up to the human being—is anticipatory. But at a certain level of life organization and complexity, the preservation drive assumes new forms through which it is realized. Anticipation is the common denominator. However, the concrete aspect of how it is eventually expressed—i.e., through self-awareness, intentionality, or in the activity called “planning”—changes as the interdependence of the processes through which the living unfolds increases.
Anticipation at the level of preserving existence is unreflected. Facial expression in anticipation of an action is a good example here, too. It seems that facial expression is not defined on a cultural level but is species wide (Ekman , Gladwell ). It is not a learned expression. Individuals can control their facial expression to an extent. However, there is always that one second or less in which control is out of the question. Intentionality is always entangled with awareness— one cannot intend something without awareness, even in vague forms. But this awareness does not automatically make human expressions carry anticipations more than the expression of the rest of the living does. We sweat “sensing” danger even before we are aware of it. The difference is evident on a different level. Humans reach self-awareness; the mind is the subject of knowledge of the mind itself. As such, we eventually recognize that our faces “speak” before we act (or before perspiration starts). They are our forecasts, the majority of them involuntary. Those intent on deciphering facial expression obtain access to some intriguing anticipatory mechanisms, or at least to their expression.
Planning (Fig. 8) is more than calculation. A planning machine for integrated activities carried out in an open system over a longer period of time would require real-time adaptive capabilities.
The planning dimension is based on learning capabilities: what road to choose at which time; how long it takes to find the daughter and prepare for the gym; how long will parent and daughter spend at the gym; which is the best way home, assuming that some other activity might be spontaneously chosen. It also implies flexibility, as a form of adapting to new circumstances (the daughter has a lot of homework, for example). “Take me from the University to my daughter’s school. After she joins me, take us to the gym. After that, we go home.” To prepare for a worst-case situation, one would have to generate possible breakdown timelines and provide contingency measures for each. Various reactive components (which correspond to reactive planning, i.e., how to react) can be effectively described in computational terms. For instance, process planning maps from design (which is an expression of anticipation) to instructions and procedures, some computer-aided (e.g., 3D printing), for effectively making things, or changing things. Operations (deterministic), operation sequences, tooling, and fabrication procedures are described in computer process planning and serve as input for automated activities.
Planning, expressed through policymaking, management, prevention, logistics, and even design, implies the ante element—giving advance thought, directing towards something, looking forward, engaging resources (including the self). Moreover, it implies understanding, which resonates with the initial form of the word denoting anticipation: antecapere. As such, the activity through which human beings identify themselves as authors of the blueprint of their actions takes place no longer at the object level, but on a meta-level. It is an activity of abstracting future actions from their object. It is also their definition in a cognitive domain not directly associated with sensory input, but rather with understanding, with knowledge. Plans synthesize predictive abilities, forecasting, and modeling (Fig. 9).
A plan is the expression of understanding actions in relation to their consequences. It is what is expressed in goals, in means to attain these goals, as well as in the time sequence for achieving them. A plan is a timeline; it is a script for interactions indexed to the timeline. To what we call understanding belong goals, the means, the underlying structure of the endeavor (tasks assumed by one person, by several, the nature of their relation, etc.), a sense of progression in time, awareness of consequences, i.e., a sense of value. As such, they report upon the physical determination of everything people do, and of the anticipatory framework. In every plan, from the most primitive to the utmost complex, the goal is associated with the reality for which the plan provides a description (a theory), which is called configuration space. If it is a scientific plan, such as the exploration of the moon or the genome project, the plan describes where the “science” actually resides, where those equations we call descriptions are “located.” If it is a political plan, or an education plan, the configuration space is made up of the people that the plan intends to engage, and of the means and methods to make it work. Our own description of the people, like the mathematical equations of science, is relative. Such description of the configuration space, and, within that space, of the interactions through which people learn from each other are subject to adjustments.
The plan also has to describe the time-space in which the goal pursued will eventually be embodied. This is a manifold, towards which the dynamics of actions and interactions (social context) will move those involved. In science, this is the landing on the moon, or the map of the human gene; it can as well be a new educational strategy or, in politics, the outcome of equal opportunity policies. The plan associated with the self-driving automobile taking its user to the daughter’s school, to the gym, etc., is of a different scale, but not fundamentally dissimilar. All the goals are anticipations projected against the background of understanding change in the world as an expression of the unity between the dynamics of the physical and the living. Plans spell out variables to be affected through actions, and the nature of the interrelationships established in pursuing the plans. Quite often, plans infer from the past (the reactive component) to the future (proactive component). They also project how the future will eventually affect the sequence of ensuing current states. Planning and self-regulation are related. The inner dynamics of phenomena and their attractors—the goals to be attained—reflect this interconnectedness. These attractors are the states into which the system will settle—at least for a while. They are the descriptions of self-organizing processes, their eventual destination, if we can understand it as a dynamic entity, not the statement of a static finality. Planning sets the limits within which adaptive processes are allowed. Each plan is in effect an expression of learning in action, and of the need to adapt to circumstances far from remaining the same.
Processes with anticipatory, predictive, and forecasting characteristics are described through
Control ? function of (past state, current state, future state) system
Adaptivity ? circumstances related to goals
Knowledge of future states is a matter of possibilistic distributions:
in which ∪ defines the large space of values a variable can take. The function ℜ is actually a fuzzy restriction associated with the variable X:
ℜ(X) = F
It is associated with a possibility distribution Î x (Nadin ). Nothing is probable unless it is possible. Not every possible value becomes probable.
The anticipated performance (von Glasersfeld ) and the actual performance are usually related. The difference between the pursued goal and the concrete output of the process, together with the reward mechanism, guides the learning component.
Functioning under continuously changing conditions means that control mechanisms will have to reflect the dynamics of the activity (Fig. 10). This is not possible without learning. If we finally combine the automated part (everything involving the change of the physical can be automated) and human performance (expressed in behavior features), we arrive at an architecture that reflects the hybrid nature of plan-driven human activities that feed values into the sensors. Based on these values, the system is reconfigured under the control of the dynamic model continuously refreshed in accordance with the behavior of the world. Learning results in the process of successive refreshment of data. Effectors act upon the world as a control procedure. If we compare this architecture to that of the Google Deep Mind Group, we notice that the difference is operational. Convolutional neural networks are used to appropriate the parameters that guide the action. The Q-network agent is nothing other than a reduction of anticipation to prediction.
Indexed behavior features (of students in a class, patients, vehicle drivers, airplane pilots, politicians in a power position, computer game choices, etc.) and the methods for extracting regularities characteristic of their behavior are connected. Learning ensues from adapting to new circumstances (i.e., change). The “learning”—classroom, physician’s office, car, airplane, management system guiding a prime minister or a secretary of state, successfully playing a game, understanding a question and answering it, etc.—is thus one that combines its own dynamic (modified, evolving knowledge) and that of the persons involved (anticipation included). The suggestion here is that conceiving intelligent classrooms, intelligent schools, intelligent cars and airplanes, intelligent “assistants” for those in power, or intelligent game players is characteristic of an anticipatory perspective. However, the perspective does not automatically translate into proactive activity. Most of the time the system remains reactive. Embodied intelligence and the intelligence of challenged users could augment the perception of time, and thus help mitigate consequences of change for which society is rarely (if ever) prepared. If the generic diagram of the hybrid control mechanisms endowed with learning conjures associations with the smartphones of our time (i.e., mobile computing), it is not by accident (as we shall see in the second part of this study).
Part 2 →