On Epistemics in Expected Free Energy for Linear Gaussian State Space Models

Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents). In this paper we show that Expected Free Energy (EFE) minimisation, a core feature of the framework, does not lead to purposeful explorative behaviour in linear Gaussian dynamical systems. We provide a simple proof that, due to the specific construction used for the EFE, the terms responsible for the exploratory (epistemic) drive become constant in the case of linear Gaussian systems. This renders AIF equivalent to KL control. From a theoretical point of view this is an interesting result since it is generally assumed that EFE minimisation will always introduce an exploratory drive in AIF agents. While the full EFE objective does not lead to exploration in linear Gaussian dynamical systems, the principles of its construction can still be used to design objectives that include an epistemic drive. We provide an in-depth analysis of the mechanics behind the epistemic drive of AIF agents and show how to design objectives for linear Gaussian dynamical systems that do include an epistemic drive. Concretely, we show that focusing solely on epistemics and dispensing with goal-directed terms leads to a form of maximum entropy exploration that is heavily dependent on the type of control signals driving the system. Additive controls do not permit such exploration. From a practical point of view this is an important result since linear Gaussian dynamical systems with additive controls are an extensively used model class, encompassing for instance Linear Quadratic Gaussian controllers. On the other hand, linear Gaussian dynamical systems driven by multiplicative controls such as switching transition matrices do permit an exploratory drive.     

Multiscale Free Energy Analysis of Human Ecosystem Engineering

Unlike ecosystem engineering by other living things, which brings a relatively limited range of sensations that are connected to a few enduring survival preferences, human ecosystem engineering brings an increasing variety and frequency of novel sensations. Many of these novel sensations can quickly become preferences as they indicate that human life will be less strenuous and more stimulating. Furthermore, they can soon become addictive. By contrast, unwanted surprise from these novel sensations may become apparent decades later. This recognition can come after the survival of millions of humans and other species has been undermined. In this paper, it is explained that, while multiscale free energy provides a useful hypothesis for framing human ecosystem engineering, disconnects between preferences and survival from human ecosystem engineering limit the application of current assumptions that underlie continuous state-space and discrete state-space modelling of active inference.     

碰撞打靶实验中的能量损失及不确定度估算

对碰撞打靶实验中的能量损失,先作理论估算,再通过实验验证.讨论二者不一致时如何获得修正值以及测量值的显著性检验问题.还讨论了实验的不确定度估算问题.得出的结论是碰撞打靶实验过程中的能量损失大部分源于非弹性碰撞,其余主要来自空气阻力.     

Variational Message Passing and Local Constraint Manipulation in Factor Graphs

Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in model development. Since evidence evaluations are usually intractable, in practice variational free energy (VFE) minimization provides an attractive alternative, as the VFE is an upper bound on negative model log-evidence (NLE). In order to improve tractability of the VFE, it is common to manipulate the constraints in the search space for the posterior distribution of the latent variables. Unfortunately, constraint manipulation may also lead to a less accurate estimate of the NLE. Thus, constraint manipulation implies an engineering trade-off between tractability and accuracy of model evidence estimation. In this paper, we develop a unifying account of constraint manipulation for variational inference in models that can be represented by a (Forney-style) factor graph, for which we identify the Bethe Free Energy as an approximation to the VFE. We derive well-known message passing algorithms from first principles, as the result of minimizing the constrained Bethe Free Energy (BFE). The proposed method supports evaluation of the BFE in factor graphs for model scoring and development of new message passing-based inference algorithms that potentially improve evidence estimation accuracy.     

测量不确定度的评定与表示

测量不确定度和如何正确评定与表示，是个极其重要的问题，文章指出了研究不确定度的意义，介绍了不确定度的有关概念，按实际工作的测量模型，给出了标准不确定度Ａ类、Ｂ类评定的各种具体方法，提出了标准不确定度的俣成方法与展伸不确定度的给出方法，对不确定度评定与表示的程序进行了汇总，并举出了应用实例。     

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

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.     

Uncertainty of the beam energy measurement in the e~+e~- collision using Compton backscattering

The beam energy is measured in the e+e-collision by using Compton backscattering. The uncertainty of this measurement process is studied by virtue of analytical formulas, and the special effects of variant energy spread and energy drift on the systematic uncertainty estimation are also studied with the Monte Carlo sampling technique. These quantitative conclusions are especially important for understanding the uncertainty of the beam energy measurement system.     

Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing

Active inference is a normative framework for explaining behaviour under the free energy principle—a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy—a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error—plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.     

开关触发延迟时间和抖动测量的不确定度分析

为明确脉冲功率装置开关触发延迟时间和抖动测量的可信度,进行了不确定度评定。使用拟合的线性关系式结合示波器水平分辨力导致的不确定度建立开关延迟时间不确定度的数学模型;根据抖动的定义建立抖动的测量不确定度数学模型。两者均按B类不确定度评定。以相关实验数据为基础计算了各个不确定度分量、合成标准不确定度以及扩展不确定度。按工程测量要求置信概率为95%,取包含因子为2,可得初级实验平台(PTS)单路样机激光触发开关触发延迟时间测量的扩展不确定度为0.38 ns;抖动测量的扩展不确定度为0.13 ns。延迟时间和抖动测量结果的不确定度满足实验分析的要求。     

Universality principle and the development of classical density functional theory

The universality principle of the free energy density functional and the ‘test particle' trick by Percus are combined to construct the approximate free energy density functional or its functional derivative. Information about the bulk fluid radial distribution function is integrated into the density functional approximation directly for the first time in the present methodology. The physical foundation of the present methodology also applies to the quantum density functional theory.     

Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active 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.     

Entropic Uncertainty in Neutrino and Meson Systems

The dynamics of quantum‐memory‐assisted entropic uncertainty for the closed neutrino system in the context of two flavor oscillations and the meson system within the framework of open quantum system are investigated. It is found that the entropic uncertainty exists in close relation with the quantum correlation, and growing quantum correlation can decrease the uncertainty. The oscillatory behaviors of entropic uncertainty in neutrino system brought about by neutrino oscillating property are different from the decaying behaviors of entropic uncertainty in meson system induced by the meson decaying nature. In addition, the entropic uncertainty is always equal to its lower bound in the two subatomic systems. This study would throw light on the particle behavior characteristics of high energy physics, and may be useful to the tasks of quantum information‐processing implemented with subatomic system since the uncertainty principle plays vital role in quantum information science and technology.     

大学物理实验数据处理中不确定度的计算

讨论了不确定度的概念与分类,针对物理实验数据处理过程中不确定度计算的难点进行分析讨论,提出了在大学物理实验数据处理中正确计算不确定度的方法.并以测钢丝杨氏模量为例介绍不确定度引人数据评定的具体做法.     

开关触发延迟时间和抖动测量的不确定度分析

为明确脉冲功率装置开关触发延迟时间和抖动测量的可信度,进行了不确定度评定。使用拟合的线性关系式结合示波器水平分辨力导致的不确定度建立开关延迟时间不确定度的数学模型;根据抖动的定义建立抖动的测量不确定度数学模型。两者均按B类不确定度评定。以相关实验数据为基础计算了各个不确定度分量、合成标准不确定度以及扩展不确定度。按工程测量要求置信概率为95％,取包含因子为2,可得初级实验平台（PTS）单路样机激光触发开关触发延迟时间测量的扩展不确定度为0.38 ns;抖动测量的扩展不确定度为0.13 ns。延迟时间和抖动测量结果的不确定度满足实验分析的要求。     

Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion

Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of attention. Both the basic probability assignment (BPA) and the negation of BPA in the evidence theory framework can model and reason uncertain information. However, how to address the uncertainty in the negation information modeled as the negation of BPA is still an open issue. Inspired by the uncertainty measures in Dempster–Shafer evidence theory, a method of measuring the uncertainty in the negation evidence is proposed. The belief entropy named Deng entropy, which has attracted a lot of attention among researchers, is adopted and improved for measuring the uncertainty of negation evidence. The proposed measure is defined based on the negation function of BPA and can quantify the uncertainty of the negation evidence. In addition, an improved method of multi-source information fusion considering uncertainty quantification in the negation evidence with the new measure is proposed. Experimental results on a numerical example and a fault diagnosis problem verify the rationality and effectiveness of the proposed method in measuring and fusing uncertain information.