Anticipatory Systems

This paper examines anticipation as a fundamental cognitive and semiotic phenomenon through which humans construct meaning and interpret the world. Drawing on enactive theories of perception, Bayesian inference models, and semiotic frameworks, the study positions anticipation not as mere prediction, but as an active, creative process that generates "significant surfaces" (Paolucci, 2021: 6) mediating our relationship with the environment. The investigation explores the intrinsic connection between memory and prospection, supported by neuroimaging evidence that shows overlapping neural systems for both processes. Through analyses of Proust's madeleine episode and design practices, the paper discusses how memory functions not as passive storage but as an imaginative reconstruction oriented toward future action, while prospection operates as a form of abductive reasoning that creates new semiotic universes. The study refers, then, to the distinction between "the clock of narration" and "the clock of interpretation" introduced by Mihai Nadin (2014) to illuminate the temporal gap where human anticipatory capacity emerges. By reconceptualizing anticipation as semiotic action rather than deterministic prediction, this paper offers insights into how humans navigate temporality through creative acts of signification that transcend simple causeeffect relationships, and how the past and the future inhabit the present.

This paper advances a clarified account of abduction by stipulating its meaning strictly in the Peircean sense: abduction is the act of nominating a candidate hypothesis in response to surprising evidence-that is, observations that resist accommodation by existing background theories or expectations. Such evidence prompts the generation of a novel explanatory conjecture, which may then enter the broader inductive cycle for testing and refinement. On this definition, abduction is shown to be a proper subset of induction rather than an autonomous mode of inference. A formal containment result establishes that every abductive act belongs within the inductive process, while induction also requires stages of testing, replication, and convergence. A Bayesian framework demonstrates that abduction alone cannot secure acceptance because rivals and unconceived alternatives dominate the probability space until reduced by testing. A game-theoretic model then recasts inquiry as a repeated interaction with Nature, where abduction is a myopic best-response and induction is the long-horizon explorationexploitation policy that stabilizes belief. Together these perspectives reveal abduction as indispensable for launching inquiry but epistemically incomplete on its own. Properly understood, abduction is induction in embryo: the first step of inquiry, never the last.

Supervenience is a relationship which has been used recently to explain the physical determination of biological phenomena despite resistance to reduction (Rosenberg, 1978, 1985; Sober, 1984a). Supervenience, however, is plagued by ambiguities which weaken its explanatory value and obscure some interesting aspects of reduction in biology. Although I suspect that similar considerations affect the use of supervenience in ethics and the philosophy of mind, I don’t intend anything I have to say here to apply outside of the physical and biological cases I consider.The main point of this paper is that there is a property of biological systems which makes it both misleading and inappropriate to reduce central biological phenomena to the properties of underlying components. Despite this, reductive explanation has been a major source of innovation in biological theory. The apparent tension can be resolved if underlying properties are explanatorily relevant to the higher level phenomena even ...

Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processing, and so on. Yet, there is no theoretical explanation of this success. In particular, it is not clear why the deeper the network, the better it actually performs. We argue that the explanation is intimately connected to a key feature of the data collected from our surrounding universe to feed the machine learning algorithms: large non-parallelizable logical depth. Roughly speaking, we conjecture that the shortest computational descriptions of the universe are algorithms with inherently large computation times, even when a large number of computers are available for parallelization. Interestingly, this conjecture, combined with the folklore conjecture in theoretical computer science that P = N C, explains the success of deep learning. In 1950, Alan Turing boldly conjectured that passing the now-called Turing test would require machine learning. His amazing insight was essentially the following. The human brain has around 10 15 synapses and 10 9 of these synapses are likely critical to the kind of natural language processing needed to pass the Turing test. Turing went on regarding this quantity as the minimal amount of bits needed for any algorithm to pass the test. In modern terms, we would say that the Kolmogorov complexity [10] of the Turing test is likely to be of the order of 10 9 bits. This corresponds to saying that no shorter algorithm can solve this problem. Unfortunately, writing quality source codes that are 10 9 bits long is extremely challenging, tedious, time-consuming and prone to errors, even for large teams of top software developers. Recall that there are around 2 10 9 such source codes, which is much larger than the number of particles in the universe. Back in 1950, Turing foresaw that, around 2000, computer science would undergo a revolution, as it would rely more and more on machine-written rather than hand-written algorithms. Machine learning (or learning machines, as Turing called it) would thus correspond to the

Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processing, and so on. Yet, there is no theoretical explanation of this success. In particular, it is not clear why the deeper the network, the better it actually performs. We argue that the explanation is intimately connected to a key feature of the data collected from our surrounding universe to feed the machine learning algorithms: large non-parallelizable logical depth. Roughly speaking, we conjecture that the shortest computational descriptions of the universe are algorithms with inherently large computation times, even when a large number of computers are available for parallelization. Interestingly, this conjecture, combined with the folklore conjecture in theoretical computer science that P = N C, explains the success of deep learning. In 1950, Alan Turing boldly conjectured that passing the now-called Turing test would require machine learning. His amazing insight was essentially the following. The human brain has around 10 15 synapses and 10 9 of these synapses are likely critical to the kind of natural language processing needed to pass the Turing test. Turing went on regarding this quantity as the minimal amount of bits needed for any algorithm to pass the test. In modern terms, we would say that the Kolmogorov complexity [10] of the Turing test is likely to be of the order of 10 9 bits. This corresponds to saying that no shorter algorithm can solve this problem. Unfortunately, writing quality source codes that are 10 9 bits long is extremely challenging, tedious, time-consuming and prone to errors, even for large teams of top software developers. Recall that there are around 2 10 9 such source codes, which is much larger than the number of particles in the universe. Back in 1950, Turing foresaw that, around 2000, computer science would undergo a revolution, as it would rely more and more on machine-written rather than hand-written algorithms. Machine learning (or learning machines, as Turing called it) would thus correspond to the

In an era defined by systemic disruption, radical unpredictability, and the rapid evolution of artificial intelligence, the classical distinction between risk and uncertainty—first articulated by Frank Knight and John Maynard Keynes—demands urgent reexamination. While traditional risk is measurable through probabilistic models, deep uncertainty involves unknowable probabilities and indeterminate outcomes that increasingly defy legacy risk frameworks. This paper explores how the rising frequency of high-impact shocks—technological, geopolitical, financial, and epidemiological—exposes the fragility of conventional risk management approaches. It argues for a paradigm shift: from viewing disruption as episodic to understanding it as systemic, where interdependent stressors interact to produce cascading, nonlinear impacts. To address this complexity, we propose the AAA Framework (Antifragile, Anticipatory, and Agility) as a novel approach for uncertainty management. Drawing on insights from complexity science, strategic foresight, and adaptive resilience, the framework emphasizes imagination over prediction and prioritizes managing outcome amplitude over probability. By cultivating organizational shock absorbers and dynamic responsiveness, the AAA Framework enables stakeholders to navigate open-ended volatility, build adaptive capability for decision-making under deep uncertainty (DMDU), and seize emergent opportunities across multiple plausible futures. This has critical implications for risk intelligence, insurability, and policy design in increasingly volatile and complex environments.

CITATION: Spitz, R., and Desbiey, O. “The future of risk and insurability in the era of systemic disruption, unpredictability and artificial intelligence”. Journal of Operational Risk. Risk.net, June 9, 2025.

DOI: 10.21314/JOP.2025.004

This paper 1 proposes an abstract mathematical frame for describing some features of cognitive and biological time. We focus here on the so called "extended present" as a result of protentional and retentional activities (memory and anticipation). Memory, as retention, is treated in some physical theories (relaxation phenomena, which will inspire our approach), while protention (or anticipation) seems outside the scope of physics. We then suggest a simple functional representation of biological protention. This allows us to introduce the abstract notion of "biological inertia".

This talk establishes a probabilistic, nonlinear model of anticipation, integrating Taleb's antifragility with Rosen's anticipatory systems. The core insight is that anticipation emerges not through explicit foresight, but through systems that structurally benefit from uncertainty.

This paper 1 proposes an abstract mathematical frame for describing some features of cognitive and biological time. We focus here on the so called "extended present" as a result of protentional and retentional activities (memory and anticipation). Memory, as retention, is treated in some physical theories (relaxation phenomena, which will inspire our approach), while protention (or anticipation) seems outside the scope of physics. We then suggest a simple functional representation of biological protention. This allows us to introduce the abstract notion of "biological inertia".

Since Cannon, inspired by Bernard's discussion of the conditions required for free and independent life, introduced the term homeostasis, many have embraced it as the main theoretical principle guiding physiology and medicine. Nonetheless, critics have argued that homeostasis is too limiting and have advanced a variety of alternative concepts such as heterostasis, rheostasis, and allostasis. We argue that the critics target a much narrower understanding of homeostasis put forward by the cyberneticists and that Bernard and Cannon embraced a far broader understanding that can accommodate the alternatives advanced by the critics and provide an integrated theoretical framework for physiology.

Of human sentiments and in the gradual enlargement of our regards to JuStlCe, in proportion as we become acquainted with the extensive utility of that virtue. David Hume It is the task ofsocial theory to explain how the unintended consequences Of our intentions and actions arise, and what kind of consequences arise if people do this that or the other in a certain social situation. And it is, eSPeCia11y, the task of the social sciences to analyze in this way the existence and the functlOnmg Of ;n巌加わn∫… and of social co妨cti胸・

Spitz, R. “How anticipatory governance can help us with unpredictability”. World Economic Forum, July 31, 2024.

Systemic disruption, such as the COVID-19 pandemic, the energy crises and inflationary pressures, forces us to confront unpredictability. The recent CrowdStrike incident was just the tip of the iceberg, with any one event capable of rewriting the world's economic landscape. Following the AAA Framework-representing 'antifragile, anticipatory and agility'-can help create pathways to being more future-savvy. Systemic disruption-the cascade of constant disruptions across our hyperconnected world-forces us to confront unpredictability. The COVID-19 pandemic served as a stark example, and was followed by energy crises, supply chain disruptions and inflationary pressures unseen in decades. Indeed, the US Treasury has admitted its lack of understanding regarding these "unanticipated" economic shocks. Even the technological realm isn't immune, evidenced by recent large-scale outages caused by innocuous software updates. The recent CrowdStrike incident underlined

Responsible Foresight (RF) emerges as a critical strategic approach for organizations and individuals seeking to understand, anticipate, and proactively navigate increasingly complex global challenges. This presentation explores RF as an advanced methodology that integrates systems thinking, ethical decision-making, and comprehensive scenario planning to develop adaptive and sustainable strategies. By synthesizing insights from futures studies, technology forecasting, and anticipatory systems research, RF provides a robust framework for understanding potential future trajectories. The presentation demonstrates briefly how RF transcends traditional forecasting by emphasizing responsible innovation, stakeholder engagement, and holistic strategic thinking.

A plain language description of the author's 2010 synthesis of the late Dr. Robert Rosen's relational complexity theory is provided to explain some of the implications of that theory for current and future science. Priority directions for research are also discussed.

1 In examining relationships between autopoiesis and anticipation in artificial life (Alife) systems it is demonstrated that anticipation may increase efficiency and viability in artificial autopoietic living systems. This paper, firstly, gives a review of the Varela et al [1974] ...

T his work addresses the critical issue of flooding, a significant natural hazard, consistently ranked highest in the 2023 World Risk Index. The annual onslaught of tropical cyclones and the associated abnormal rainfall threaten lives, and destroy crops and property, thereby causing great economic damage, demanding urgent and science-based decision-making. We introduce an impact-based flood forecasting system as a proactive anticipatory action (i.e., AA) measure, linking climate services and disaster risk management to mitigate extreme weather impacts. Developed by the University of the Philippines Resilience Institute, the National Operational Assessment of Hazards (NOAH) Center, and Gerry Bagtasa of the Institute of Environmental Science and Meteorology, this system advances disaster resilience efforts, leveraging sciencebased forecasts to prioritize vulnerable barangays (villages). Unlike traditional early warning systems, the proposed automated system predicts flooding one day in advance and assesses exposure levels based on population distribution. By utilizing global weather forecast models locally calibrated for Philippine conditions and 100-year rain return flood hazard maps for the Philippines, the system forecasts river inundated areas, enabling local government units (LGUs) and humanitarian organizations to prioritize preparations for communities and allocate resources during flooding events effectively. This system enhances the efficiency of disaster response planning, fostering a proactive and impactful strategy to address the recurring threat of flooding in the country. The system also underscores the role of open data in disaster resilience, exemplified by NOAH's system to disseminating big data, which aligns with open governance principles. By encouraging transparency and stakeholder collaboration, data exchange is promoted, providing actionable

W e live in an increasingly technological world. Automated systems certainly can make life easier, but they can also create complexity and uncertainty. Moreover, it is clear that automation does not merely supplant human activity, but also transforms the nature of human work. This review examines an original account of this transformation-a link between automation technology and the sense that our actions cause effects on the outside world (so-called 'agency'). Accordingly, we first discuss the human factor issues related to automation technology. Particularly, we introduce the out-of-the-loop performance problem. Then, we introduce recent findings about agency. We propose that several recently developed psychological approaches to the self-promise to enhance our comprehension of the transformation induced by increased automation. Next, we address the controversial issue of agency measuring, particularly the necessary dissociation between explicit and implicit agency measurement. In particular, we introduce the intentional binding effect as an implicit agency measurement, and we discuss the problems and issues related to the generalization of this effect to more complex situations. Finally, we suggest that the investigation of this authorship processing in the field of human-machine interaction may be fruitful, both to elaborate concrete design recommendations and to evaluate the potentiality for an HMI to satisfy the agency mechanism. Automation and human control in complex systems "The burning question of the near future will not be how much work a man can do safely, but how little." [85] There is perhaps no facet of modern society in which the influence of automation technology has not been felt. Whether at work or at home, while travelling or while engaged in leisurely pursuits, human beings are becoming increasingly accustomed to using and interacting with sophisticated computer systems designed to assist them in their activities. Even more radical changes are anticipated in the future, as computers increase in power, speed and "intelligence". We have usually focused on the perceived benefits of new automated or computerized devices. This is perhaps not surprising, given the sophistication and ingenuity of design of many such systems (e.g., the automatic landing of a jumbo jet, or the docking of two spacecraft). The economic benefits that automation can provide, or is perceived to offer, also tend to focus public attention on the technical capabilities of automation. However, our fascination with the possibilities afforded by technology often obscures the fact that new computerized and automated devices also create new burdens and complexities for the individuals and teams of practitioners responsible for operating, troubleshooting and managing high-consequence systems. Whatever the merits of any particular automation technology, it is clear that automation does not merely supplant human activity but also transforms the nature of human work. As a matter of fact, the role of the human actors may possibly evolve from direct control to supervision. Understanding the characteristics of this transformation is vital for successful design of new automated systems. Automation and OOL performance problem When new automation is introduced into a system, or when there is an increase in the autonomy of automated systems, developers often assume that adding "automation" is a simple substitution of a machine activity for human activity (substitution myth, see [92]). Empirical data on the relationship of people and technology suggest that this is not the case and that traditional automation has many negative performance and safety consequences associated with it stemming from the human out-of-the-loop (OOL) performance problem (see [22], [50]). Classically, the out-of-the-loop performance problem leaves operators of automated systems handicapped in their ability to take over manual operations in the event of automation failure [22]. The OOL performance problem has been attributed to a number of underlying factors, including human vigilance decrements (see [7], [86]), Issue 4-May 2012-Sense of Control in Supervision Tasks of Automated Systems

In 2014 the Defence Science and Technology Laboratory developed and implemented a novel approach to assess the system by which the United Kingdom Ministry of Defence delivers infrastructure projects and services. This approach brought together existing methods to constitute a hybrid problem structuring method that offered the potential to trigger anticipatory intervention by focusing on the health as opposed to the performance of this system. This paper revisits the initial assessment to examine whether use of the method has led to increased system resilience, and in particular to understand what it was about the method that helped to deliver benefits. Insights with regard to the structures and processes necessary to enable anticipatory action for resilience are presented.

In 2014 the Defence Science and Technology Laboratory developed and implemented a novel approach to assess the system by which the United Kingdom Ministry of Defence delivers infrastructure projects and services. This approach brought together existing methods to constitute a hybrid problem structuring method that offered the potential to trigger anticipatory intervention by focusing on the health as opposed to the performance of this system. This paper revisits the initial assessment to examine whether use of the method has led to increased system resilience, and in particular to understand what it was about the method that helped to deliver benefits. Insights with regard to the structures and processes necessary to enable anticipatory action for resilience are presented.

Faith in the Anthropocene requires a re-imagined account of Christian hope. Research on the emergence of eco-anxiety disorder shows that climate crisis and ecological destruction have psychological and emotional effects on persons and communities, producing fear, despair, and hopelessness. Accounts of hope in recent environmental literature and in traditional Christian formulations rely on faith in political will, technological innovation, or an omnipotent divine sovereign to intervene and save. Such accounts are inadequate for this moment. A re-imagined notion of Christian hope will embrace hopelessness, understood as the relinquishment of false optimism that the climate crisis can be reversed and a commitment to act without expectation of success, but with a commitment to nurturing the wisdom to live more humanly.

A formal model of the processes of digestion in a hypothetical cell is developed and discussed as a case study of how the threefold logic of Peircean semiotics works within Rosen's paradigm of relational ontology. The formal model is used to demonstrate several fundamental differences between a relational description of biological processes and a mechanistic description. The formal model produces a logic of embodied generalization that is mediated and determined by the cell through its interactions with the environment. Specifically, the synchronization of the functions of pattern recognition and semantic attribution results in an open and adaptive learning system that is stabilized by a hermeneutical circle. The relational principles of biosemiotics demonstrated through this case study are applicable to other biological systems, as well as to the relational ontology of systems theory and relativistic quantum theory.

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This meta-analysis of 26 reports published between 1978 and 2010 tests an unusual hypothesis: for stimuli of two or more types that are presented in an order designed to be unpredictable and that produce different post-stimulus physiological activity, the direction of pre-stimulus physiological activity reflects the direction of post-stimulus physiological activity, resulting in an unexplained anticipatory effect. The reports we examined used one of two paradigms: (1) randomly ordered presentations of arousing vs. neutral stimuli, or (2) guessing tasks with feedback (correct vs. incorrect). Dependent variables included: electrodermal activity, heart rate, blood volume, pupil dilation, electroencephalographic activity, and blood oxygenation level dependent (BOLD) activity.To avoid including data hand-picked from multiple different analyses, no post hoc experiments were considered. The results reveal a significant overall effect with a small effect size [fixed effect: overall ES = 0.21, 95% CI = 0.15-0.27, z = 6.9, p < 2.7 × 10 −12 ; random effects: overall (weighted) ES = 0.21, 95% CI = 0.13-0.29, z = 5.3, p < 5.7 × 10 −8 ]. Higher quality experiments produced a quantitatively larger effect size and a greater level of significance than lower quality studies. The number of contrary unpublished reports that would be necessary to reduce the level of significance to chance (p > 0.05) was conservatively calculated to be 87 reports. We explore alternative explanations and examine the potential linkage between this unexplained anticipatory activity and other results demonstrating meaningful pre-stimulus activity preceding behaviorally relevant events.We conclude that to further examine this currently unexplained anticipatory activity, multiple replications arising from different laboratories using the same methods are necessary. The cause of this anticipatory activity, which undoubtedly lies within the realm of natural physical processes (as opposed to supernatural or paranormal ones), remains to be determined.

With this study, we investigated the possibility of exploiting the psychophysiological predictive anticipatory activity to avoid random negative events by using a portable device we named CardioAlert. The device uses the heart rate to send an alert when a negative event is anticipated, thus giving the individual an opportunity to change his/her choice. In an exploratory experiment, one-hundred participants were asked to choose between two alternatives which had a random chance of leading to a "winning" outcome. Participants obtained 52.5% of "winning" choices when using the CardioAlert and 48.2% when they were not using it (effect size = 0.18; 95% confidence interval-.02 to .38). The results of a pre-registered confirmatory experiment confirmed the main findings of the exploratory one. The mean percentage of positive choices was 52.7% when using the CardioAlert with an effect size of 0.23, 95% confidence interval .03 to .43. A conceptual replication experiment which used the heart rate PPAA to avoid the negative event automatically instead of voluntarily, correctly predicted the occurrence of 56.3% of negative events with an effect size of 0.69, 95% confidence interval .47 to .91. These findings suggest that psychophysiological predictive anticipatory activity can be exploited for developing practical decision supporting systems .

Although rarely, if ever, acknowledged, there is real confusion existing over several of the fundamental ideas of classical macroscopic thermodynamics. Many of these surround the concept of entropy and one of the big questions never publicly asked is whether or not the functions called entropy in various branches of mathematics and physics are all the same? Here some thoughts are presented which have been provoked by reading some of the writings of J. P. Wesley in the hope that they, in turn, will provoke further examination of these notions, many of which are so basic to so many areas of physics and are causing so much trouble for students at the present time.

The system thinking is the important component of study at the Faculty of Economics and Management, Czech University of Life Sciences Prague (CULS Prague) but the system dynamics is not the part of education there. Current effort is to focus on implementation of system dynamics into existing courses of Mathematical Methods in Economics. The objective of the paper is to design and test flexible seminars' structure of system dynamics at the Czech University of Life Sciences Prague during the lessons and seminars on the courses of Mathematical Methods in Economics. The results are necessary for successful introduction of system dynamics courses at CULS Prague. First part of the paper describes the role of system dynamics in education and the registered benefits from its implementation into education at different schools and universities. Second part contains the sources that constitute the basis for the seminars' structure design. The proposed structure is tested in third part through the questionnaire and the opinions of students.

Even in inter regional risk management phenomena of retardation and adaptation play an important role in the interplay between different logical management levels. In this paper it is demonstrated that an System Dynamics (SD) approach may be very suitable for modeling and simulating such phenomena. In some situations, however, SD approaches and modeling tools may pose some unnecessary restrictions on the procedure. Hence, the authors recommend a pragmatic and flexible attitude towards the choice of approach and tools.

Even in inter regional risk management phenomena of retardation and adaptation play an important role in the interplay between different logical management levels. In this paper it is demonstrated that an System Dynamics (SD) approach may be very suitable for modeling and simulating such phenomena. In some situations, however, SD approaches and modeling tools may pose some unnecessary restrictions on the procedure. Hence, the authors recommend a pragmatic and flexible attitude towards the choice of approach and tools.

We try not just to reconcile but to “integrate” Cognitivism and Behaviorism by a theory of different forms of purposiveness in behavior and mind. This also implies a criticism of the Dual System theory and a claim on the strong interaction and integration of Sist1 (automatic) and Sist2 (deliberative), based on reasons, preferences, and decisions. We present a theory of different kinds of teleology. Mere “functions” of the behavior: finalism not represented in the mind of the agent, not “regulating” the behavior. Two kinds of teleological mental representations: true “Goals” in control-theory, cybernetic view, with “goal-driven” behavior (intentional action); vs. Expectations in Anticipatory Classifiers: a reactive but anticipatory device, explaining the “instrumental” (finalistic) nature of Skinner’s reinforcement learning. We present different kinds of Goals and goal processing and on this ground the theory of what “intentions” are. On such basis, we can discuss Kathy Wilkes’s hint...

Experiencing sensory-pleasing environments and recreating perceptions of past lived environments can benefit patients greatly in coping with irreversible conditions (e.g., dementia) or during healing after traumatic experiences. Such environments can evoke emotional responses that are associated with a patient’s positive memories of a place, an activity, or people. A design concept of an intimate architectural space informed by knowledge of anticipatory systems was developed for the purpose of generating variable personalized environments. In particular, such environments are designed for patients, according to their individual demands and expectations. The design is guided by aesthetic data acquired through perceptual user studies. Such adaptive spaces can be delivered to a large number of users in healthcare in the form of Architecture-as-Service (AaS). The research presented expands the notion of AaS and is premised upon an IoT application.

and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

Aging, along with the associated costs (not just in dollars), is a major health problem (cf. United Nations Programme on Ageing 1). Many resources are utilized for fi ghting aging and its effects at its core (genetic, molecular, neuronal), but few for attenuating the consequences of the basic aging process. The fact that the post-World War II baby boom generation will be reaching elderly status in the coming years adds urgency to the need to address foreseeable problems before they occur. This situation is tantamount to a “Sputnik ” for research in aging, similar to what became the impetus for the American space program

Justified by spectacular achievements facilitated through applied deep learning methodology (based on neural networks), the "Everything is possible" view dominates this new hour in the "boom and bust" curve of AI performance. The optimistic view collides head on with the "It is not possible"-ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views can be assessed only by addressing the nature of medicine. Specifically: Which part of medicine, if any, can and should be entrusted to AI-now or at some moment in the future? AI or not, medicine should incorporate the anticipation perspective in providing care.

We are our brains. The study argues for a theory of the brain based on the brain itself, not on theories generated to explain the world in some of its many aspects. Consequently, this study of the brain debunks those analogy assumptions, never confirmed by science, that still dominate brain science. The hypothesis advanced concerns the distributed nature of brain activity. It describes the role of interactions, the specific causality characteristic of brain related dynamics, and the broader questions of cognitive activity associated with awareness of change (usually subsumed as consciousness). Empirical evidence that brain processes are anticipatory in nature leads to the conclusion that reproducibility, as defined within the deterministic experimental method, cannot be expected. Significance for the performance of the living (reproduction, survival, evolutionary edge, etc.) informs brain processes and explains their non-deterministic nature. Keywords Complementarity Á Decidability Á G-complexity Á Interaction Á Reactive brain Á Anticipatory brain 1 Prediction In the age of computation, consider X to be the next focus in science. In a time shorter then what it took for scientists and technologists to define, test, and deploy X, there will be a theory and an experimental proof generalizing from X to the living. The statement (known in a variety of formulations, from very crude to very subtle) that brains are computers is one example. It took less time than it did to define, test, and deploy computers, for McCulloch (1949) to come up with the formulation. Von Neumann, Newell, and Simon, etc. followed suit. The most recent M. Nadin (&) antÉ-Institute for Research in Anticipatory Systems,

Predictive computation is rapidly becoming the new focus of computer science and technological development. In some cases, under marketing pressure, it is characterised as 'anticipatory computing.' In other cases, computation is expected to deliver predictive phenotyping, or new materials that will behave according to predefined expectations. Since dealing with the future is significant from the perspective of acknowledging and preparing for change, concepts pertinent to describing the future take on importance. There is a pragmatic necessity for establishing a common understanding of various aspects of prediction: forecasting, premonition, guessing, inference, foretelling, prolepsis, expectation, etc. Technology affords new possibilities, such as wearables and mobile computation. However, technology does not substitute for a sense of direction.

The fundamental distinction between reaction and anticipation corresponds to their respective condition. Reaction is by its nature the expression of a particular form of causality defined within Newtonian physics. Anticipation corresponds to a specific form of causality associated with the past and with possible futures. Bernstein acknowledged that motoric activity is driven by the future, i.e., by the goal pursued. The formula "repetition without repetition" captures the role that variability, as an expression of anticipation, plays in the motoric process. This study focuses on an attempt to capture the holistic expression of anticipation in quantitative descriptions, and draws a parallel between Bernstein's experimental work and that carried out in the AnticipationScope. Keywords AnticipationScope Á Causality Á Motoric repetition Á Variability Anticipation is systemic in nature, and of non-deterministic condition. Bernstein, whom we mentioned already as a precursor of anticipation research [1-3] examined movement in order to obtain information on the workings of the brain (Meijer and Bruijn [4]). Feigenberg [5] considered the variety of ways in which future-driven activities can be described. He opted for the mathematics of probability, and Bernstein was quite satisfied with this option. At the antÉ-Institute for Research in Anticipatory Systems, we considered human performance in its most general terms: work, learning, leisure activities, sexuality, and reproduction. Activities (intentional or not) that trigger change or assist in adapting to change are, in my view, a better description of performance than particular motoric expressions. The goal assumed in our experiments was to characterize performance in both qualitative and quantitative ways. A simple diagram (Fig. 1) captures the working hypothesis.

Anticipation has frequently been acknowledged, but mainly on account of qualitative observations. To quantify the expression of anticipation is a challenge in two ways: (1) Anticipation is unique in its expression; (2) given the nondeterministic nature of anticipatory processes, to describe quantitatively how they take place is to describe not only successful anticipations, but also failed anticipations. The AnticipationScope is an original data acquisition and data processing environment. The Anticipatory Profile is the aggregate expression of anticipation as a realization in the possibility space. A subsystem of the AnticipationScope could be a predictive machine that monitors the performance of deterministic processes.

and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

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The crisis in the reproducibility of experiments invites a re-evaluation of methods of inquiry and validation procedures. The text challenges current assumptions of knowledge acquisition and introduces G-complexity for defining decidable vs. non-decidable knowledge domains. A &quot;second Cartesian revolution&quot; should result in scientific methods that transcend determinism and reductionism.

Uncertainty about potential negative future outcomes can cause stress and is a central feature of anxiety disorders. The stress and anxiety associated with uncertain situations may lead individuals to overestimate the frequency with which uncertain cues are followed by negative outcomes, an example of covariation bias. Using functional magnetic resonance imaging, we found that uncertaintyrelated expectations modulated neural responses to aversion. Insula and amygdala responses to aversive pictures were larger after an uncertain cue (that preceded aversive or neutral pictures) than a certain cue (that always preceded aversive pictures). Anticipatory anterior cingulate cortex (ACC) activity elicited by the cues was inversely associated with the insula and amygdala responses to aversive pictures following the cues. Nearly 75% of subjects overestimated the frequency of aversive pictures following uncertain cues, and ACC and insula activity predicted this uncertainty-related covariation bias. Findings provide the first evidence of the brain mechanisms of covariation bias and highlight the temporal dynamics of ACC, insula, and amygdala recruitment for processing aversion in the context of uncertainty.

AAA is often used to reflect the ultimate achievement. Those with finance backgrounds will recognize that AAA is the highest level of credit worthiness, or in science the best rank for alphabetical grading scales.

Stephen Hawking (2000) qualified the 21st century as “the century of complexity.” With that backdrop, for some time we have been using AAA as “Anticipatory, Antifragile and Agility” (AAA) to define what humans should be developing to improve their abilities as the world becomes more complex. This need for humans to enhance their capabilities is that much more relevant in the context of machines learning fast and with increasingly higher-level human functions.

While the term anticipatory is intimately related to foresight, for our AAA taxonomy we borrow the definition of antifragile from Taleb (2012): “Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better.” And we use “agility” in the context of the Cynefin framework (Snowden & Boone, 2007), looking at properties such as our ability to be curious, innovative and experimental, to know how to amplify or dampen our evolving behaviors depending on feedback, thus allowing instructive patterns to emerge, especially in complex adaptive systems.

- CITATION: Spitz, R. “The Future of Strategic Decision-Making”. Journal of Futures Studies, July 26, 2020.