Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

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The knowledge complexity of interactive proof-systems

The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e. into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e. on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e. path-tracking, mode-tracking and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.

On Bayesian mechanics: a physics of and by beliefs

This paper suggests that we need an alternative approach to economic modeling in general, and macroeconomic modeling in particular, if we are to capture salient characteristics of recent economic developments. Rather than focusing on models built on the basis of isolated, rational, optimizing agents, we should recognize that much simpler individuals following basic rules can collectively generate complex behavior. We have lessons to learn from studying the behavior of social insects for example. Noisy systems of interactive agents can generate aggregate phenomena such as sudden changes in the state of an economy or market, with no external shock. I give two examples of simple models of financial markets to illustrate this but would argue more generally that such models are indispensible if we are to understand aggregate economic phenomena.

Ants And Nonoptimal Self-Organization: Lessons For Macroeconomics

The demonstration by Ivan Barnes et al. that the serpentinization of fresh Alpine-type ultramafic rocks results in the exhalation of hot alkaline fluids is foundational to the submarine alkaline vent theory (AVT) for life’s emergence to its ‘improbable’ thermodynamic state. In AVT, such alkaline fluids ≤ 150°C, bearing H2 > CH4 > HS−—generated and driven convectively by a serpentinizing exothermic mega-engine operating in the ultramafic crust—exhale into the iron-rich, CO2> > > NO3−-bearing Hadean ocean to result in hydrothermal precipitate mounds comprising macromolecular ferroferric-carbonate oxyhydroxide and minor sulfide. As the nanocrystalline minerals fougerite/green rust and mackinawite (FeS), they compose the spontaneously precipitated inorganic membranes that keep the highly contrasting solutions apart, thereby maintaining redox and pH disequilibria. They do so in the form of fine chimneys and chemical gardens. The same disequilibria drive the reduction of CO2 to HCOO− or CO, and the oxidation of CH4 to a methyl group—the two products reacting to form acetate in a sequence antedating the ‘energy-producing’ acetyl coenzyme-A pathway. Fougerite is a 2D-layered mineral in which the hydrous interlayers themselves harbor 2D solutions, in effect constricted to ~ 1D by preferentially directed electron hopping/tunneling, and proton Gröthuss ‘bucket-brigading’ when subject to charge. As a redox-driven nanoengine or peristaltic pump, fougerite forces the ordered reduction of nitrate to ammonium, the amination of pyruvate and oxalate to alanine and glycine, and their condensation to short peptides. In turn, these peptides have the flexibility to sequester the founding inorganic iron oxyhydroxide, sulfide, and pyrophosphate clusters, to produce metal- and phosphate-dosed organic films and cells. As the feed to the hydrothermal mound fails, the only equivalent sustenance on offer to the first autotrophs is the still mildly serpentinizing upper crust beneath. While the conditions here are very much less bountiful, they do offer the similar feed and disequilibria the survivors are accustomed to. Sometime during this transition, a replicating non-ribosomal guidance system is discovered to provide the rules to take on the incrementally changing surroundings. The details of how these replicating apparatuses emerged are the hard problem, but by doing so the progenote archaea and bacteria could begin to colonize what would become the deep biosphere. Indeed, that the anaerobic nitrate-respiring methanotrophic archaea and the deep-branching Acetothermia presently comprise a portion of that microbiome occupying serpentinizing rocks offers circumstantial support for this notion. However, the inescapable, if jarring conclusion is drawn that, absent fougerite/green rust, there would be no structured channelway to life.

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A self-sustaining serpentinization mega-engine feeds the fougerite nanoengines implicated in the emergence of guided metabolism

Introduction Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC. Methods 40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded. Results Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest. Discussion In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

Low-dissipation optimization of the prefrontal cortex in the −12° head-down tilt position: A functional near-infrared spectroscopy study

Dissipative accounts of structure formation show that the self-organisation of complex structures is thermodynamically favoured, whenever these structures dissipate free energy that could not be accessed otherwise. These structures therefore open transition channels for the state of the universe to move from a frustrated, metastable state to another metastable state of higher entropy. However, these accounts apply as well to relatively simple, dissipative systems, such as convection cells, hurricanes, candle flames, lightning strikes, or mechanical cracks, as they do to complex biological systems. Conversely, interesting computational properties—that characterize complex biological systems, such as efficient, predictive representations of environmental dynamics—can be linked to the thermodynamic efficiency of underlying physical processes. However, the potential mechanisms that underwrite the selection of dissipative structures with thermodynamically efficient subprocesses is not completely understood. We address these mechanisms by explaining how bifurcation-based, work-harvesting processes—required to sustain complex dissipative structures—might be driven towards thermodynamic efficiency. We first demonstrate a simple mechanism that leads to self-selection of efficient dissipative structures in a stochastic chemical reaction network, when the dissipated driving chemical potential difference is decreased. We then discuss how such a drive can emerge naturally in a hierarchy of self-similar dissipative structures, each feeding on the dissipative structures of a previous level, when moving away from the initial, driving disequilibrium.

https://www.mdpi.com/1099-4300/23/9/1115/pdf

A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks.

Nowadays, in the cognitive fields, there has been increasing focus on knowledge input and output measurement rather than assessing the quality of the knowledge created. In this paper, we discuss the reaction-diffusion equations and their application to a particular economic phenomenon: namely, that of the ways in which knowledge complexity diffusion might affect or not affect the labour productivity across the European countries. More specifically, the goal followed by the present research is to supply European countries with an alternative approach: a strategy. This strategy has the purpose of enabling some of these countries (especially the low-income ones) to avoid acting as knowledge predators towards other countries (i.e. those such as Sweden and Finland, referred to as prey countries, which invest their resources in knowledge and therefore in technology). We emulate the knowledge competition and the spill-over productivity effects across the European countries using alternative reaction-diffusion equations: specifically, the Lotka-Volterra equations, which are able to describe complex dynamic system of the prey-predator phenomenon. Our approach is based on the analysis using the canonical Lotka-Volterra models of patents from the European Patent Office and the labour productivity data from the Eurostat database from 2000 and 2017. Furthermore, we have identified the presence of lagged knowledge on a European scale, especially that between Northern European and Eastern European countries. In conclusion, the implementation of specific knowledge diffusion policy strategies could be helpful for future applications and may avoid predator and intra-guild cannibalism phenomena from being engaged in some low-income countries.

Reaction-Diffusion Equations with Application to the Knowledge Complexity Diffusion in Europe

Nowadays, economic geography and cognitive fields are has besetted with knowledge input and output measurement instead of assessing the quality of the knowledge produced. In this paper, we measure the complexity of knowledge, and map the distribution and the evolution of knowledge in the Italian regions through a neural network approach, based on non-linear clustering with the use of the Self-Organizing Map, and exploring in which way spatial knowledge could be linked to complexity via a relatedness measurement, the Knowledge Complexity Index. Our measurement is based on the analysis of patent data from the European Patent Office, focusing on the Italian context, between 2004 and 2012. We find that knowledge complexity is not homogeneously distributed across Italy. The implementation of this new research line could be helpful in terms of Italian regional diversification because its implementation is still at an early stage at both nation and regional levels and, at the same time, the reduction in the divergence between Regional Innovation Systems in Northern and Southern of Italy will continue to represent the major policy challenge.

Mapping the Geography of Italian Knowledge

Nowadays, the Self-Organizing Map (SOM) is configured as one of the most widely used algorithms for visualising and exploring high-dimensional data: this visual formation is derived from the learning map process in which neighbouring neurons, on the input lattice surface, are connected to their respective neighbouring neurons on the output lattice surface. The present methodological note will introduce the Self-Organizing Map (SOM) algorithm, discussing, in the first part, its background, properties, applications and extensions and, in the second part, its evolution: the formation of new types of topographic maps, used for categorical data, such as time series and the tree structured data. In particular, this new type of map could be useful for further micro data analysis applications, such as document analysis or web navigation analysis, going beyond the limitation of the kernel-based topographic maps or creating new type of kernels, detailed in the Support Vector Machine literature.

The Self-Organizing Map: An Methodological Note

The reviews in this edition of the Journal have in common a critique of the language and practices of the business and organizational world, together with suggestions and approaches to improve that world through the promotion of inclusion, collective intelligence and democracy. In a contribution which illustrates the increasingly wide reach of action approaches to knowledge and learning, Fadhila Yonata and colleagues from STAIN University in Indonesia review Critical Action Research: Challenging Neoliberal Language and Literacies Education by Antoinette Gagne ́ et al. Whilst the authors of this book discuss ‘Critical Action Research’ in the context of educational settings, Anderson argues that the findings are equally relevant to community and organizational environments. One interesting chapter reports on how teachers as researchers working with critical action research challenged the use of neoliberal language and ‘the language of prestige’ (English) by using local languages as the medium of instruction. By contrast, the world of the Business Schools seems to be somewhere that critical action research and learning have yet to reach. In his review of Martin Parker’s Shut Down the Business School: What’s Wrong with Management Education, Chris Blantern notes Parker’s accusation that the Business Schools serve to normalize institutional power and reinforce divisions in society rather than enabling students to be more aware of the futures they are being enrolled to deliver and educating them for the benefit of society rather than for private gain. For Parker, the Business School remains ‘a factory for producing employees for capitalist organizations, a machine for producing a very particular kind of future.’ This core function remains critically unaddressed and yet, as Blantern points out in his critique, Parker doesn’t inquire into the kinds of learning that would populate his ‘school for organizing’. Action research and action learning are obvious candidates, being inclusive and pragmatic whilst also inculcating democratic and ethical ways of working. Continuing the theme of how our professions and institutions serve to encourage private gain via ‘surfing capitalism’s changing trends’, John Edmonstone reviews Mariana Mazzucato and Rosie Collington’s The Big Con: How the Consulting Industry Weakens our Businesses, Infantilizes our Governments and Warps our Economies. The big consultancies have profited greatly in the era of public sector ‘reforms’, based on the myth that the entire sector is inefficient, ineffective and un-innovative, and have somehow gained a reputation for being indispensable. The authors’ conclusion is that instead of wasting billions in this way, governments should invest in creating capable organizations that foster learning where people are empowered to take risks: ‘It is time to invest in the collective intelligence of the public sector and end the consulting con once and for all.’ To close his review, Edmonstone shows how the philosophy and practices of action learning are ideally suited to this vital task. A key part of the development of ‘collective intelligence’ is the fostering of inclusion and diversity in workplaces. Our fourth review is of the latest edition of the well-established text Managing Diversity: Toward a Globally Inclusive Workplace by Michalle Mor Barak. Reviewed

https://www.tandfonline.com/doi/pdf/10.1080/14767333.2023.2218131?needAccess=true&role=button

Daniela Cialfi

Papers

A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks.

Reaction-Diffusion Equations with Application to the Knowledge Complexity Diffusion in Europe

Mapping the Geography of Italian Knowledge

The Self-Organizing Map: An Methodological Note

How to promote inclusion, collective intelligence and democracy