scholarly journals Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1155
Author(s):  
Stephen Fox
Keyword(s):  
Stress Management ◽  
Life Histories ◽  
Internal State ◽  
Generative Models ◽  
Calibration Model ◽  
Active Inference ◽  
Ecological Fitness ◽  
Adaptive Calibration ◽  
Stress Responsivity ◽  
Inference Theory

In this paper, the Adaptive Calibration Model (ACM) and Active Inference Theory (AIT) are related to future-proofing startups. ACM encompasses the allocation of energy by the stress response system to alternative options for action, depending upon individuals’ life histories and changing external contexts. More broadly, within AIT, it is posited that humans survive by taking action to align their internal generative models with sensory inputs from external states. The first contribution of the paper is to address the need for future-proofing methods for startups by providing eight stress management principles based on ACM and AIT. Future-proofing methods are needed because, typically, nine out of ten startups do not survive. A second contribution is to relate ACM and AIT to startup life cycle stages. The third contribution is to provide practical examples that show the broader relevance ACM and AIT to organizational practice. These contributions go beyond previous literature concerned with entrepreneurial stress and organizational stress. In particular, rather than focusing on particular stressors, this paper is focused on the recalibrating/updating of startups’ stress responsivity patterns in relation to changes in the internal state of the startup and/or changes in the external state. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action and ecological fitness to human organizations.

scholarly journals Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 198
Author(s):  
Stephen Fox
Keyword(s):  
Artificial Intelligence ◽  
Quality Management ◽  
Social Organization ◽  
Generative Models ◽  
Industrial Engineering ◽  
Prediction Errors ◽  
Active Inference ◽  
For Profit ◽  
Different Types ◽  
Inference Theory

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

scholarly journals The Adaptive Calibration Model of stress responsivity

2011 ◽  
Vol 35 (7) ◽  
pp. 1562-1592 ◽  
Author(s):  
Marco Del Giudice ◽  
Bruce J. Ellis ◽  
Elizabeth A. Shirtcliff
Keyword(s):  
Calibration Model ◽  
Adaptive Calibration ◽  
Stress Responsivity

Biological sensitivity to context: A test of the hypothesized U-shaped relation between early adversity and stress responsivity

2019 ◽  
Vol 32 (2) ◽  
pp. 641-660 ◽  
Author(s):  
Nila Shakiba ◽  
Bruce J. Ellis ◽  
Nicole R. Bush ◽  
W. Thomas Boyce
Keyword(s):  
Stress Reactivity ◽  
Kindergarten Children ◽  
Calibration Model ◽  
Growing Up ◽  
Cortisol Reactivity ◽  
Number Of Children ◽  
Adaptive Calibration ◽  
Stress Responsivity ◽  
Disproportionate Number ◽  
Biological Sensitivity To Context

AbstractWe conducted signal detection analyses to test for curvilinear, U-shaped relations between early experiences of adversity and heightened physiological responses to challenge, as proposed by biological sensitivity to context theory. Based on analysis of an ethnically diverse sample of 338 kindergarten children (4–6 years old) and their families, we identified levels and types of adversity that, singly and interactively, predicted high (top 25%) and low (bottom 25%) rates of stress reactivity. The results offered support for the hypothesized U-shaped curve and conceptually replicated and extended the work of Ellis, Essex, and Boyce (2005). Across both sympathetic and adrenocortical systems, a disproportionate number of children growing up under conditions characterized by either low or high adversity (as indexed by restrictive parenting, family stress, and family economic condition) displayed heightened stress reactivity, compared with peers growing up under conditions of moderate adversity. Finally, as hypothesized by the adaptive calibration model, a disproportionate number of children who experienced exceptionally stressful family conditions displayed blunted cortisol reactivity to stress.

Beyond allostatic load: Rethinking the role of stress in regulating human development

2013 ◽  
Vol 26 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Bruce J. Ellis ◽  
Marco Del Giudice
Keyword(s):  
Life History ◽  
Stress Response ◽  
Allostatic Load ◽  
Life History Theory ◽  
Calibration Model ◽  
Load Model ◽  
Adaptive Calibration ◽  
Trade Offs ◽  
Response Systems

AbstractHow do exposures to stress affect biobehavioral development and, through it, psychiatric and biomedical disorder? In the health sciences, the allostatic load model provides a widely accepted answer to this question: stress responses, while essential for survival, have negative long-term effects that promote illness. Thus, the benefits of mounting repeated biological responses to threat are traded off against costs to mental and physical health. The adaptive calibration model, an evolutionary–developmental theory of stress–health relations, extends this logic by conceptualizing these trade-offs as decision nodes in allocation of resources. Each decision node influences the next in a chain of resource allocations that become instantiated in the regulatory parameters of stress response systems. Over development, these parameters filter and embed information about key dimensions of environmental stress and support, mediating the organism's openness to environmental inputs, and function to regulate life history strategies to match those dimensions. Drawing on the adaptive calibration model, we propose that consideration of biological fitness trade-offs, as delineated by life history theory, is needed to more fully explain the complex relations between developmental exposures to stress, stress responsivity, behavioral strategies, and health. We conclude that the adaptive calibration model and allostatic load model are only partially complementary and, in some cases, support different approaches to intervention. In the long run, the field may be better served by a model informed by life history theory that addresses the adaptive role of stress response systems in regulating alternative developmental pathways.

scholarly journals New Perspectives in the Model of Stress Response

2017 ◽  
pp. S173-S185 ◽  
Author(s):  
I. TONHAJZEROVA ◽  
M. MESTANIK
Keyword(s):  
Stress Response ◽  
Complex Analysis ◽  
Stress Reactivity ◽  
Developmental Trajectory ◽  
Biofeedback Training ◽  
Human Organism ◽  
Calibration Model ◽  
Promising Tool ◽  
Adaptive Calibration ◽  
As Stress

The reactions of human organism to changes of internal or external environment termed as stress response have been at the center of interest during recent decades. Several theories were designed to describe the regulatory mechanisms which maintain the stability of vital physiological functions under conditions of threat or other environmental challenges. However, most of the models of stress reactivity were focused on specific aspects of the regulatory outcomes – physiological (e.g. neuroendocrine), psychological or behavioral regulation. Recently, a novel complex theory based on evolutionary and developmental biology has been introduced. The Adaptive Calibration Model of stress response employs a broad range of the findings from previous theories of stress and analyzes the responsivity to stress with respect to interindividual differences as a consequence of conditional adaptation – the ability to modify developmental trajectory to match the conditions of the social and physical environment. This review summarizes the contributions of the most important models in the field of stress response and emphasizes the importance of complex analysis of the psycho-physiological mechanisms. Moreover, it outlines the implications for nonpharmacological treatment of stress-related disorders with the application of biofeedback training as a promising tool based on voluntary modification of neurophysiological functions.

scholarly journals Accessing Active Inference Theory through Its Implicit and Deliberative Practice in Human Organizations

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1521
Author(s):  
Stephen Fox
Keyword(s):  
Lived Experience ◽  
Public Institutions ◽  
Active Inference ◽  
Energy Principle ◽  
Private Companies ◽  
Free Energy Principle ◽  
Starting Point ◽  
And Mathematics ◽  
Autopoietic Organization ◽  
Inference Theory

Active inference theory (AIT) is a corollary of the free-energy principle, which formalizes cognition of living system’s autopoietic organization. AIT comprises specialist terminology and mathematics used in theoretical neurobiology. Yet, active inference is common practice in human organizations, such as private companies, public institutions, and not-for-profits. Active inference encompasses three interrelated types of actions, which are carried out to minimize uncertainty about how organizations will survive. The three types of action are updating work beliefs, shifting work attention, and/or changing how work is performed. Accordingly, an alternative starting point for grasping active inference, rather than trying to understand AIT specialist terminology and mathematics, is to reflect upon lived experience. In other words, grasping active inference through autoethnographic research. In this short communication paper, accessing AIT through autoethnography is explained in terms of active inference in existing organizational practice (implicit active inference), new organizational methodologies that are informed by AIT (deliberative active inference), and combining implicit and deliberative active inference. In addition, these autoethnographic options for grasping AIT are related to generative learning.

scholarly journals Active Inference: Demystified and Compared

2021 ◽  
pp. 1-39
Author(s):  
Noor Sajid ◽  
Philip J. Ball ◽  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Agents ◽  
Arm Movement ◽  
Generative Models ◽  
Active Inference ◽  
Discrete State ◽  
Q Learning ◽  
Learning Agents ◽  
Time Formulation ◽  
Complex Settings

Active inference is a first principle account of how autonomous agents operate in dynamic, nonstationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration—and account for uncertainty about their environment—in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in reinforcement learning is removed in active inference, where reward can simply be treated as another observation we have a preference over; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based reinforcement learning agents and by placing zero prior preferences over rewards and learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings (e.g., robotic arm movement, Atari games) if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents.

scholarly journals PID Control as a Process of Active Inference with Linear Generative Models

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 257 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Prediction Errors ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.

scholarly journals A tale of two densities: active inference is enactive inference

2019 ◽  
Vol 28 (4) ◽  
pp. 225-239 ◽  
Author(s):  
Maxwell JD Ramstead ◽  
Michael D Kirchhoff ◽  
Karl J Friston
Keyword(s):  
Predictive Coding ◽  
Generative Models ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
The One ◽  
And Control ◽  
The Brain ◽  
Selection Of

The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) – and its corollary, active inference – in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain – variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference – what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control – the self-organising, belief-guided selection of action policies – and do not have the properties ascribed by structural representationalists.

Stress-Responsivity in Secondary School Students: Implications for Educational Psychologists

1990 ◽  
Vol 7 (1) ◽  
pp. 15-18
Author(s):  
Christopher F Sharpley ◽  
Andrew Mavroudis ◽  
Anne-Maree James
Keyword(s):  
Heart Rate ◽  
Secondary School ◽  
Physical Fitness ◽  
Stress Management ◽  
Secondary School Students ◽  
Age Groups ◽  
Stressful Event ◽  
Resting Heart Rate ◽  
School Students ◽  
Stress Responsivity

ABSTRACTThe range and incidence of responsivity to the onset of a stressful event was investigated with two different age groups of teenagers. There was a wide degree of responsivity to the stressor used and this responsivity was not found to be related to performance on the stressful event, sex of respondent, or physical fitness as measured by resting heart rate. Implications for effective stress-management of secondary school students' hyperresponsivity to similar stressors are discussed, with several suggestions given for implementation of these procedures.

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