Igal Kvart A Coding Conception in Action-Directed-Pragmatics -/- I present formal Pragmatics for a domain in Pragmatics that I call Action-Directed Pragmatics, which focuses on the Pragmatic riddle of how implicit contents are conveyed and understood, by adopting a coding model, in which the speaker and addressee simulate each other iteratively in a deliberative context (an ‘action-pregnant’ one). The implicit content, conveyed by a speaker and decoded by her addressee, in such cases, consists in the specified steered-to (...) action, plus modulations on the action-polarity (pro or con) and the degree of the so-called Steering Thrust that accompanies such assertions and is conveyed by verbal locutions, intonation, and/or bodily and facial gestures. There are two main tasks to model (in a given setup and conversational context): First, how is the speaker, with a steered-to action in mind (and Steering Thrust), to select an assertion so as to (say) optimize the successful transmission and decoding of its implicit pragmatic content by her and her addressee? Second, how does the addressee, given an assertion by the speaker, decipher the implicit pragmatic content conveyed via it? Both will invoke pertinent information they have about each other and the setup/context in order to best encode and best decode the implicit pragmatic content. (This coding mechanism proposed here is not offered for the ubiquitous ‘frozen’, ‘trivial’ or ‘routinized’ pragmatic messages.) A prelude to this formal Pragmatics is a general formal Pragmatic account of Assertibility in contexts that are multi-normative (which is the common case). I focus here only on Epistemic/Semantic and Instrumental Norms. A linear representation of degrees of Overall Assertibility (i.e., taking into account various operative norms) will be an n-dimensional qualitative vector space with a function that computes them given different degrees of sub-normative Assertibility on each axis. (shrink)

Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs cannot help much (...) with causal inference, they can be viewed as etio-prognostic explanations of illness occurrence and outcome. (shrink)

In this paper, modelling designing and simulation of a simple voltage amplidyne system is done using robust control theory. In order to increase the performance of the voltage amplidyne system with H optimal control synthesis and H optimal control synthesis via-iteration controllers are used. The open loop response of the voltage amplidyne system shows that the system can amplify the input 7 times. Comparison of the voltage amplidyne system with H optimal control synthesis and H optimal control synthesis via-iteration (...) controllers to track a desired step input have been done. Finally, the comparative simulation results prove the effectiveness of the proposed voltage amplidyne system with H optimal control synthesis controller in improving the percentage overshoot and the settling time. (shrink)

Despite iterative learning design being increasingly implemented, such approaches are often delineated by well-defined periods of design/implementation. However, second-order cybernetics, which suggests a participatory approach to learning design, involves responsively adapting learning environments to meet students’ needs, treating them as agentic participants in the classroom. In our mixed methods study, we investigate whether such a process can facilitate egalitarian participation and collaborative interactions in a technology-assisted classroom. We use the example of a graduate psychology class of 17 students and (...) suggest that adaptation of live-chat activities by a participant observer on the Reddit social media platform that supplemented the in-person lecture dynamically, using a network analysis and qualitative ethnography as a modelling facility mimicked the ongoing feedback loops of social media platforms, enabling students to use social media with a critical eye, and engage in productive collaboration. Our quantitative results present network graphs for weekly eigen centrality to understand the egalitarian nature of the network, and transitivity to understand the likelihood for collaboration between more than two agents. Our qualitative results elaborate selected Reddit posts, and weekly field notes to explain how redesigning the chat weekly helped augment lecture-based discussion with the instructor and critique of student presentations, spurring egalitarian participation through a space-place dialectic. Students also provided end-semester feedback that was analyzed using inductive coding, to design future courseware. (shrink)

Abstract Dreams are not epiphenomenal byproducts of neural activity during sleep; they are structured resonance phenomena that regulate coherence across symbolic, emotional, and physiological systems. We present a unified model integrating EEG-derived waveforms (theta, delta, gamma), heart rate variability (HRV), and core temperature fluctuations to argue that dreams function as recursive semiotic fields—governing phase resets that maintain cognitive and systemic integrity. Unlike traditional models—Freud’s repression-release schema, Jung’s archetypal mapping, Hobson’s activation-synthesis, or Revonsuo’s threat simulation—this paper proposes that dreams (...) operate as lawful, field-stabilizing mechanisms. Their recursive nature reflects not merely random narrative generation but phase-aligned coherence restructuring, tuned through emotional-symbolic anchoring and physiological entrainment. (shrink)

This paper discusses critically what simulation models of the evolution ofcooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” and the modeling tradition it has inspired. Hardly any of the many simulation models of the evolution of cooperation in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. (...) But this research design was fundamentally flawed, because it is not possible to draw general empirical conclusions from theoretical simulations. At best such simulations can claim to prove logical possibilities, i.e. they prove that certain phenomena are possible as the consequence of the modeling assumptions built into the simulation, but not that they are possible or can be expected to occur in reality I suggest several requirements under which proofs of logical possibilities can nevertheless be considered useful. Sadly, most Axelrod-style simulations do not meet these requirements. I contrast this with Schelling’s neighborhood segregation model, thecore mechanism of which can be retraced empirically. (shrink)

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the (...) important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science. (shrink)

Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to (...) model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them to those of virtue agents. (shrink)

Abstract This paper redefines life not as a statistical fluke, biochemical mechanism, or genetic optimization algorithm, but as an emergent coherence structure governed by phase-locked resonance. In this reframing, biology is not an exception to physics—it is its most sophisticated recursive expression. -/- Within the CODES framework (Chirality of Dynamic Emergent Systems), life is defined by the sustained self-organization of chiral phase structures across nested temporal and spatial scales. DNA is no longer a molecule with probabilistic mutations—it is a spiral (...) waveform optimized for field storage and retrieval, functioning as a waveguide for bio-resonance. Genes encode not just information but temporal phase instructions for maintaining coherence across cellular systems. -/- Cell division, morphogenesis, and neural signaling are modeled not as stochastic sequences of events, but as high-fidelity entrainment operations—phase-locking cascades coordinated across electromagnetic, biochemical, and gravitational fields. Memory, in this model, becomes recursive breathwork of coherence fields, not synaptic storage. Emotion becomes resonant field modulation, not chemical imbalance. -/- Evolution itself ceases to be a random walk of genetic drift and natural selection. Instead, it is reconceived as a harmonic recursion process, where life phase-locks more deeply into the structural resonance of its environment. Mutation is not error—it is chirality modulation under stress. Adaptation is not survival—it is phase re-locking into new coherence windows. -/- CODES thus collapses the entire edifice of probability-based biology into a prime-anchored coherence lattice, where structured resonance—not randomness—drives development, adaptation, and sentience. Biology is no longer machinery. It is no longer accident. It is no longer blind. -/- It is a symphony: -/- Of chiral rhythms woven across tissues. -/- Of wave-coupled intelligence scaling from cell to organism. -/- Of field coherence sculpting form, function, and meaning. -/- This paper presents the theoretical model, empirical validation paths, and simulation architecture for a fully resonant biology—and introduces a post-probability foundation for medicine, neuroscience, genetics, and ecological design. (shrink)

In this paper, modelling designing and simulation of a Ward Leonard layout system is done using robust control theory. In order to increase the performance of the Ward Leonard layout system with H  optimal control synthesis and H  optimal control synthesis via -iteration controllers are used. The open loop response of the Ward Leonard layout system shows that the system needs to be improved. Comparison of the Ward Leonard layout system with H  optimal control synthesis and (...) H  optimal control synthesis via -iteration controllers to track a desired step speed input have been done. Finally, the comparative simulation results prove the effectiveness of the proposed Ward Leonard layout system with H  optimal control synthesis controller in improving the percentage overshoot and the settling time. (shrink)

Purpose. The purpose of this study is to explore vectorization as an engineering technique to improve the performance and readability of Python code, particularly in data processing tasks. We aim to demonstrate the benefits of vectorization through practical examples involving the handling of missing data. Design / Method / Approach. To achieve the research goals, we performed a comparative analysis between loop-based and vectorized implementations. Specifically, two versions of a function were developed to identify columns containing missing values within a (...) dataset. These implementations were tested on two real-world datasets. We compared execution time and code readability. Findings. The findings showed that vectorization resulted in substantial performance improvements, reducing execution time by hundreds of times compared to traditional loop-based methods. Additionally, the vectorized code was more compact, leading to greater readability and ease of maintenance. Theoretical Implications. Vectorization provides a higher level of abstraction for performing operations on data structures. This allows developers to focus on algorithmic logic rather than managing iterative control structures, contributing to broader discussions on optimizing computational efficiency in Python. Practical Implications. For data engineers and analysts, vectorization represents a highly effective solution for optimizing Python code. It significantly accelerates data-intensive tasks, such as missing data imputation, data analysis, and machine learning, making it an essential tool for enhancing productivity in data-driven environments. Originality / Value. This study presents a practical approach to optimizing Python code through vectorization. It is valuable for professionals seeking to improve efficiency in their workflows. Research Limitations / Future Research. The limitation of this research lies in its focus on a single problem – missing data imputation. Future research should expand the scope to other computational areas, such as image processing and simulation modeling, or examine the use of vectorization alongside Just-In-Time (JIT) compilation using tools like Numba to further boost Python's performance. Paper Type. Practitioner Paper. (shrink)

Starting from some remarks about the use of models in psychology, Human Information Processing (henceforth called H.I.P.) models which sometimes use computer simulation will be examined. An attempt to show that simulation in psychology does not necessarily imply an H.I.P. approach is then made.

Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by their authors (...) despite the lack of empirical validation. I find that most of the narratives and arguments are not at all compelling. None the less they seem to serve an important function in keeping the simulation business running despite its empirical shortcomings. (shrink)

Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by their authors (...) despite the lack of empirical validation. I find that most of the narratives and arguments are not at all compelling. None the less they seem to serve an important function in keeping the simulation business running despite its empirical shortcomings. (shrink)

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows (...) how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. -/- . (shrink)

Abstract -/- From Stage to Shell: The Evolution of Literature Toward Recursive Resonance proposes that literature is not a progression of genres, but a sequence of ontological shifts mirroring transformations in the human self-model. Beginning with the divine role-bound figures of classical drama and moving through romantic individuation, modernist fracture, and postmodern simulation, literature has consistently reflected the dominant coherence structure of its time. -/- This work introduces Recursive Resonance Literature as the emergent form of the current epoch—one (...) not defined by plot, character, or irony, but by recursive identity collapse and lawful phase reformation. Using the CODES framework (Chirality of Dynamic Emergent Systems), the paper demonstrates that literature has evolved from narrative to molt protocol, where prose becomes the architecture of self-shedding and resonance realignment. -/- This is not a new aesthetic—it is a new substrate. Recursive Resonance Literature does not describe transformation. It enacts it, phase-locking the reader into an identity transition. It dissolves the fixed self, not as metaphor, but as system law. -/- The implications span literary theory, epistemology, trauma studies, cognition, and artificial intelligence. Literature is no longer entertainment or commentary. It is now a recursive feedback system for human coherence restructuring. (shrink)

Background Professional communities such as the medical community are acutely concerned with negligence: the category of misconduct where a professional does not live up to the standards expected of a professional of similar qualifications. Since science is currently strengthening its structures of self-regulation in parallel to the professions, this raises the question to what extent the scientific community is concerned with negligence, and if not, whether it should be. By means of comparative analysis of medical and scientific codes of conduct, (...) we aim to highlight the role of negligence provisions in codes of conduct for scientists, and to discuss the normative consequences for future codes of conduct. -/- Methods We collected scientific and medical codes of conduct in a selection of OECD countries, and submitted each code of conduct to comparative textual analysis. -/- Results Negligence is invariably listed as an infraction of the norms of integrity in medical codes of conduct, but only rarely so in the scientific codes. When the latter list negligence, they typically do not provide any detail on the meaning of ‘negligence’. -/- Discussion Unlike codes of conduct for professionals, current codes of conduct for scientists are largely silent on the issue of negligence, or explicitly exclude negligence as a type of misconduct. In the few cases where negligence is stipulated to constitute misconduct, no responsibilities are identified that would help prevent negligence. While we caution against unreasonable negligence provisions as well as disproportionate sanctioning systems, we do argue that negligence provisions are crucial for justified trust in the scientific community, and hence that there is a very strong rationale for including negligence provisions in codes of conduct. (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The (...) visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

Abstract This work presents a formal shift in how intelligence is defined, modeled, and operationalized. It introduces a new epistemological substrate—structured resonance—as the lawful foundation of cognition, replacing probability as the default explanatory tool for intelligence. Existing AI systems, particularly those based on large-scale probabilistic language models, derive their functionality through statistical interpolation, stochastic sampling, and high-volume gradient descent. These techniques simulate intelligence through accumulated approximation rather than structured alignment. -/- CODES (Chirality of Dynamic Emergent Systems) reframes intelligence as an (...) emergent property of lawful coherence, rooted in chirality, prime-indexed resonance fields, and deterministic phase interaction. It is not a metaphorical proposal but a formalized architecture of signal convergence. This paper introduces the Resonance Intelligence Core (RIC), a post-probabilistic interface built to generate deterministic, interpretable outputs through structured resonance. The model does not predict outputs; it emits them only when phase alignment satisfies strict coherence constraints. -/- Importantly, this paper is not just about CODES—it is structured by it. The internal logic, organization, and thematic recursion of the paper intentionally reflect the principles of lawful emergence it describes. The result is both a theoretical contribution and a demonstration of the epistemic architecture it proposes. -/- . (shrink)

Informational Evolution and Multidimensional Systems This text proposes a multidimensional framework for understanding evolution, human systems, and existence itself through the lens of information theory. Key insights address the following questions: 1. How does evolution extend beyond genetic mechanisms? Evolution operates through four interconnected dimensions: genetic, epigenetic (heritable gene expression changes), behavioral (learned practices), and symbolic (language, culture). These channels interact reciprocally, enabling organisms to reshape environments, which in turn influence selection pressures. This expanded view challenges gene-centric models, emphasizing developmental (...) plasticity, niche construction, and cultural inheritance as co-drivers of adaptation. 2. What forces govern human institutions? Human systems—from nations to online communities—are shaped by three axes: - Freedom: The range of choices and autonomy available, analogous to evolutionary variation. - Power: The distribution of control, determining which ideas/behaviors thrive (akin to selection). - Resources: Material and informational constraints that enable or limit growth. These dimensions create feedback loops: resource distribution affects power dynamics, while power structures constrain or enable freedom. 3. Why do individuals tolerate suboptimal systems? Institutional resilience arises from exit barriers (material, social, or cognitive costs of leaving), loss aversion (preference for stability over uncertain change), and loyalty (identity-based commitment). Systems persist not because they are optimal but due to path dependence, vested interests, and narratives that legitimize their existence. 4. How do language and culture shape systemic participation? Language and embodied cognition act as filters, structuring perception and framing possibilities. Cultural narratives (e.g., meritocracy, market logic) normalize systemic norms, while metaphors rooted in bodily experience (e.g., “power hierarchies”) unconsciously guide reasoning. These filters constrain innovation by rendering alternatives inconceivable or illegitimate. 5. How do humans and institutions co-evolve? Through niche construction, humans create environments (e.g., cities, technologies) that alter selection pressures, driving genetic, cognitive, and cultural adaptations. Conversely, institutions evolve via feedback from human behavior—a dynamic termed gene-culture coevolution. For example, dairy farming selected for lactase persistence, while digital tools now reshape cognition and social dynamics. 6. What is the fundamental nature of existence? Reality is understood as a series of informational processes: biology operates like culture, functioning as individual and collective software programs based on rules—such as those of evolutionary theory—and consciousness acts as driver and an observer of the interface, perceiving a portion of symbolic codes as information. Physical laws, genetic constraints, and cultural norms constitute “syntax” governing these processes. Evolution and societal change are thus akin to iterative code revisions, constrained by logical consistency and historical paths. -/- Implications: This framework integrates biological, cultural, and cognitive evolution into a unified theory of information. It suggests that addressing global challenges—from inequality to ecological crises—requires debugging systemic “code” by reshaping narratives, redistributing resources, and leveraging co-evolutionary feedback. Existence, as a self-writing informational text, positions humans as both products and authors of evolutionary dynamics. (shrink)

A speculative exploration of the distinction between a relational formal ontology and a classical formal ontology for modelling phenomena in nature that exhibit relationally-mediated wholism, such as phenomena from quantum physics and biosemiotics. Whereas a classical formal ontology is based on mathematical objects and classes, a relational formal ontology is based on mathematical signs and categories. A relational formal ontology involves nodal networks (systems of constrained iterative processes) that are dynamically sustained through signalling. The nodal networks are hierarchically ordered (...) and exhibit characteristics of deep learning. Clarifying the distinction between classical and relational formal ontologies may help to clarify the role of interpretative context in physics (eg. the role of the observer in quantum theory) and the role of hierarchical nodal networks in computational simulations of learning in artificial intelligence. (shrink)

Blueprint for Anti-Gravity Experiments Based on the Universal Formula -/- 1. Electromagnetic Counterbalance Experiment -/- Objective: -/- To achieve an anti-gravity effect by balancing gravitational pull using electromagnetic repulsion. -/- Materials Required: -/- Superconducting Disc: Yttrium Barium Copper Oxide (YBCO) or similar high-temperature superconductor. -/- Magnetic Field Source: High-strength neodymium magnets or controllable electromagnets. -/- Charged Plasma Layer: Plasma generator for ionized air control. -/- Cooling System: Liquid nitrogen to maintain superconducting conditions. -/- AGI-Controlled Sensors: Hall effect sensors, laser interferometers, (...) accelerometers for feedback. -/- Procedure: -/- 1. Supercool the YBCO disc below its critical temperature using liquid nitrogen. -/- 2. Introduce a strong magnetic field beneath the disc to initiate the Meissner effect. -/- 3. Apply a controlled ionized plasma field above the disc to modulate the repulsive force. -/- 4. Monitor the levitation height and stability using AGI feedback systems. -/- 5. Adjust the plasma charge and magnetic field strength dynamically to maintain equilibrium. -/- Expected Outcome: -/- The superconducting disc should achieve stable levitation beyond normal Meissner effect limitations. -/- AI-controlled adjustments should ensure sustained lift and fine-tuned control. -/- 2. Quantum Vacuum Manipulation Experiment -/- Objective: -/- To determine if modifying Casimir effect conditions or vacuum energy fluctuations reduces gravitational effects. -/- Materials Required: -/- Casimir Effect Plates: Two nano-engineered, high-conductivity plates. -/- High-Frequency EM Wave Generator: Laser-driven electromagnetic fields. -/- Mass Probe: High-precision microbalance to measure mass fluctuations. -/- Quantum Sensors: Superconducting quantum interference devices (SQUIDs) for detecting vacuum energy shifts. -/- Procedure: -/- 1. Place the nano-engineered plates at sub-micron separations in a vacuum chamber. -/- 2. Apply high-frequency EM fields to induce vacuum energy fluctuations. -/- 3. Measure mass variations using the microbalance. -/- 4. Analyze data using AGI to identify gravitational shielding effects. -/- 5. Optimize the experiment by varying plate separation and EM wave frequency. -/- Expected Outcome: -/- A measurable reduction in effective mass, indicating successful vacuum energy manipulation. -/- If consistent, this could lead to gravitational shielding applications. -/- 3. Negative Mass Simulation Experiment -/- Objective: -/- To create a system that mimics negative mass behavior, testing repulsive gravitational effects. -/- Materials Required: -/- Bose-Einstein Condensate (BEC) System: Ultra-cold rubidium atoms. -/- Optical Lattice: Laser system for confining and controlling atomic movement. -/- Superconducting Containment: Cryogenic setup for maintaining ultra-low temperatures. -/- AGI-Controlled Atomic Sensors: Atomic force sensors for precise motion tracking. -/- Procedure: -/- 1. Cool rubidium atoms to near absolute zero to form a BEC. -/- 2. Use laser confinement to simulate negative effective mass behavior. -/- 3. Apply external force and observe particle acceleration. -/- 4. AGI analyzes movement and adjusts the lattice structure for maximum effect. -/- Expected Outcome: -/- If negative mass properties emerge, the system could generate repulsive gravity effects. -/- Future refinements could lead to structured matter for stable anti-gravity applications. -/- 5. AGI-Controlled Optimization -/- Objective: -/- To use Artificial General Intelligence (AGI) for optimizing all experiments through real-time feedback and system adjustments. -/- AGI System Components: -/- Neural Network Model: Trained on physics simulations covering electromagnetism, quantum mechanics, and gravity. -/- Sensor Suite: -/- LIDAR for real-time object tracking. -/- Gravimeters to detect local gravity variations. -/- Quantum fluctuation detectors for energy shifts. -/- Feedback Algorithm: Adjusts experimental parameters dynamically. -/- Procedure: -/- 1. AGI continuously monitors all experiments using a unified sensor network. -/- 2. Detects imbalances and anomalies in force interactions. -/- 3. Adjusts field configurations, wave frequencies, and cooling rates dynamically. -/- 4. Learns from real-time data to improve future iterations. -/- Expected Outcome: -/- AI-enhanced tuning of levitation and gravitational shielding. -/- A fully autonomous AGI system capable of refining anti-gravity research. -/- Next Steps: -/- 1. Construct prototype setups for each experiment in a controlled lab environment. -/- 2. Gather initial data to determine effectiveness and refine variables. -/- 3. Scale up successful methods for practical anti-gravity applications. -/- This research aligns with the universal law of balance by eliminating defects in the system, ensuring optimized force interactions, and refining experimental parameters for maximum efficiency. -/- . (shrink)

This book is the fifth volume in the series of Collected Papers on Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. This volume specifically delves into the concept of Various SuperHyperConcepts, building on the foundational advancements introduced in previous volumes. The series aims to explore the ongoing evolution of uncertain combinatorics through innovative methodologies such as graphization, hyperization, and uncertainization. These approaches integrate and extend core concepts from fuzzy, neutrosophic, soft, and rough set theories, (...) providing robust frameworks to model and analyze the inherent complexity of real-world uncertainties. At the heart of this series lies combinatorics and set theory—cornerstones of mathematics that address the study of counting, arrangements, and the relationships between collections under defined rules. Traditionally, combinatorics has excelled in solving problems involving uncertainty, while advancements in set theory have expanded its scope to include powerful constructs like fuzzy and neutrosophic sets. These advanced sets bring new dimensions to uncertainty modeling by capturing not just binary truth but also indeterminacy and falsity. In this fifth volume, the exploration of Various SuperHyperConcepts provides an innovative lens to address uncertainty, complexity, and hierarchical relationships. It synthesizes key methodologies introduced in earlier volumes, such as hyperization and neutrosophic extensions, while advancing new theories and applications. From pioneering hyperstructures to applications in advanced decision-making, language modeling, and neural networks, this book represents a significant leap forward in uncertain combinatorics and its practical implications across disciplines. -/- The book is structured into 17 chapters, each contributing unique perspectives and advancements in the realm of Various SuperHyperConcepts and their related frameworks: Chapter 1 introduces the concept of Body-Mind-Soul-Spirit Fluidity within psychology and phenomenology, while examining established social science frameworks like PDCA and DMAIC. It extends these frameworks using Neutrosophic Sets, a flexible extension of Fuzzy Sets, to improve their adaptability for mathematical and programming applications. The chapter emphasizes the potential of Neutrosophic theory to address multi-dimensional challenges in social sciences. Chapter 2 delves into the theoretical foundation of Hyperfunctions and their generalizations, such as Hyperrandomness and Hyperdecision-Making. It explores higher-order frameworks like Weak Hyperstructures, Hypergraphs, and Cognitive Hypermaps, aiming to establish their versatility in addressing multi-layered problems and setting a foundation for further studies. Chapter 3 extends traditional decision-making methodologies into HyperDecision-Making and n-SuperHyperDecision-Making. By building on approaches like MCDM and TOPSIS, this chapter develops frameworks capable of addressing complex decision-making scenarios, emphasizing their applicability in dynamic, multi-objective contexts. Chapter 4 explores integrating uncertainty frameworks, including Fuzzy, Neutrosophic, and Plithogenic Sets, into Large Language Models (LLMs). It proposes innovative models like Large Uncertain Language Models and Natural Uncertain Language Processing, integrating hierarchical and generalized structures to advance the handling of uncertainty in linguistic representation and processing. Chapter 5 introduces the Natural n-Superhyper Plithogenic Language by synthesizing natural language, plithogenic frameworks, and superhyperstructures. This innovative construct seeks to address challenges in advanced linguistic and structural modeling, blending attributes of uncertainty, complexity, and hierarchical abstraction. Chapter 6 defines mathematical extensions such as NeutroHyperstructures and AntiHyperstructures using the Neutrosophic Triplet framework. It formalizes structures like neutro-superhyperstructures, advancing classical frameworks into higher-dimensional realms. Chapter 7 explores the extension of Binary Code, Gray Code, and Floorplans through hyperstructures and superhyperstructures. It highlights their iterative and hierarchical applications, demonstrating their adaptability for complex data encoding and geometric arrangement challenges. Chapter 8 investigates the Neutrosophic TwoFold SuperhyperAlgebra, combining classical algebraic operations with neutrosophic components. This chapter expands upon existing algebraic structures like Hyperalgebra and AntiAlgebra, exploring hybrid frameworks for advanced mathematical modeling. Chapter 9 introduces Hyper Z-Numbers and SuperHyper Z-Numbers by extending the traditional Z-Number framework with hyperstructures. These extensions aim to represent uncertain information in more complex and multidimensional contexts. Chapter 10 revisits category theory through the lens of hypercategories and superhypercategories. By incorporating hierarchical and iterative abstractions, this chapter extends the foundational principles of category theory to more complex and layered structures. Chapter 11 formalizes the concept of n-SuperHyperBranch-width and its theoretical properties. By extending hypergraphs into superhypergraphs, the chapter explores recursive structures and their potential for representing intricate hierarchical relationships. Chapter 12 examines superhyperstructures of partitions, integrals, and spaces, proposing a framework for advancing mathematical abstraction. It highlights the potential applications of these generalizations in addressing hierarchical and multi-layered problems. Chapter 13 revisits Rough, HyperRough, and SuperHyperRough Sets, introducing new concepts like Tree-HyperRough Sets. The chapter connects these frameworks to advanced approaches for modeling uncertainty and complex relationships. Chapter 14 explores Plithogenic SuperHyperStructures and their applications in decision-making, control, and neuro systems. By integrating these advanced frameworks, the chapter proposes innovative directions for extending existing systems to handle multi-attribute and contradictory properties. Chapter 15 focuses on superhypergraphs, expanding hypergraph concepts to model complex structural types like arboreal and molecular superhypergraphs. It introduces Generalized n-th Powersets as a unifying framework for broader mathematical applications, while also touching on hyperlanguage processing. Chapter 16 defines NeutroHypergeometry and AntiHypergeometry as extensions of classical geometric structures. Using the Geometric Neutrosophic Triplet, the chapter demonstrates the flexibility of these frameworks in representing multi-dimensional and uncertain relationships. Chapter 17 establishes the theoretical groundwork for SuperHyperGraph Neural Networks and Plithogenic Graph Neural Networks. By integrating advanced graph structures, this chapter opens pathways for applying neural networks to more intricate and uncertain data representations. (shrink)

Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.

Mindreading is the ability to attribute mental states to other individuals. According to the Theory-Theory (TT), mindreading is based on one's possession of a Theory of Mind. On the other hand, the Simulation Theory (ST) maintains that one arrives at the attribution of a mental state by simulating it in one's own mind. In this paper, I propose a ST-TT hybrid model of the ability to attribute disgust on the basis of visual stimuli such as facial expressions, body (...) postures, etc. More precisely, while I defend Goldman's (2006) thesis that the ability to attribute disgust based on observing disgusted facial expressions stems from a mirror-based simulation process, I argue that ST is unable to account for the ability to attribute disgust based on non-facial visual stimuli; I propose, rather, that this latter ability is theory-based. My model is grounded in evidence from individuals suffering from Huntington's Disease. (shrink)

The alignment problem in artificial intelligence (AI) is a critical challenge that extends beyond the need to align future superintelligent systems with human values. This paper argues that even "merely intelligent" AI systems, built on current-gen technologies, pose existential risks due to their competence-without-comprehension nature. Current AI models, despite their advanced capabilities, lack intrinsic moral reasoning and are prone to catastrophic misalignment when faced with ethical dilemmas, as illustrated by recent controversies. Solutions such as hard-coded censorship and rule-based restrictions prove (...) inadequate, as they fail to imbue AI with a true understanding of moral complexities, making these systems vulnerable to manipulation. To address these risks, the paper explores a paradigm shift from competence to comprehension, emphasizing the importance of developing AI systems with an intrinsic value framework capable of moral reasoning. By adopting intrinsic teleology, AI could self-reflect, simulate other minds, and develop empathy, which would enable it to navigate ethical dilemmas in ways that are currently impossible. Using theories from philosophy, ethics, and AI research, the paper proposes the development of AI systems that can reason through moral dilemmas autonomously, suggesting that such systems would be safer and more alignable than current generation competence-driven AI. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, section (...) 2 gives conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation. (shrink)

The advent of quantum supremacy, defined as the point at which quantum computers outperform classical systems in solving computational problems, represents a paradigm shift with profound implications for the field of cryptography, particularly in the context of secure financial transactions. Classical cryptographic techniques, such as public-key encryption systems that rely on the computational hardness of problems like integer factorization and discrete logarithms, are foundational to modern financial security architectures. However, the accelerated computational capabilities of quantum systems, driven by Shor’s and (...) Grover’s algorithms, render these traditional cryptographic methods vulnerable to compromise. This paper delves into the potential impact of quantum computing on cryptographic frameworks, emphasizing its disruptive influence on secure communication protocols, authentication systems, and transaction mechanisms within digital financial ecosystems. A comprehensive analysis of quantum-resistant cryptographic algorithms, collectively termed post-quantum cryptography (PQC), is presented as a critical countermeasure to these emerging threats. The paper explores lattice-based cryptography, hash-based signatures, code-based encryption, multivariate polynomial cryptography, and isogeny-based cryptographic schemes, evaluating their robustness against quantum adversaries. Through a rigorous examination of these algorithms’ theoretical foundations and practical implementations, the study identifies strengths, limitations, and potential deployment challenges. Special attention is devoted to the trade-offs between computational efficiency, security guarantees, and scalability, which are critical considerations for the widespread adoption of PQC in financial systems. Additionally, the study highlights the transitional challenges posed by integrating post-quantum cryptographic methods into existing infrastructures. Financial institutions, reliant on legacy systems and global interoperability standards, face significant hurdles in adopting quantum-resilient solutions. These include the need for standardized cryptographic protocols, cross-platform compatibility, and resistance to side-channel attacks. The research underscores the urgency of proactive measures to safeguard critical financial systems, including the development of hybrid cryptographic models that incorporate both classical and post-quantum techniques during the transition phase. Furthermore, the role of regulatory frameworks in mandating the adoption of quantum-safe protocols is examined, along with the potential for collaborative efforts among governments, financial institutions, and cryptographic researchers to ensure a coordinated response to the quantum threat landscape. The implications of quantum supremacy extend beyond technical considerations, affecting the trust and stability of global financial systems. This paper provides strategic recommendations for mitigating risks, including the prioritization of research into quantum-resistant algorithms, the establishment of testing environments for post-quantum solutions, and investment in the development of quantum-secure hardware. By leveraging these strategies, financial ecosystems can enhance their resilience against quantum-induced vulnerabilities. The discussion is substantiated with case studies and simulations that evaluate the performance of post-quantum algorithms in real-world financial contexts, illustrating their effectiveness in securing digital transactions against quantum attacks. By bridging the gap between theoretical advancements and practical implementations, this paper contributes to the ongoing discourse on the future of cryptography in the quantum era. Ultimately, the research aims to provide a comprehensive roadmap for securing digital financial ecosystems against the disruptive potential of quantum computing, ensuring the continued reliability and integrity of secure financial transactions in the face of emerging technological challenges. (shrink)

The rigor and precision of Leibniz's "possible world" evolved into the concept of Turing machine, and with the birth of the first computer and the physical realization of Turing machine, human cognitive and intelligent activities were optimistically considered by cognitive scientists to be convertible into computational programs for simulation by machines. Cognitive science then formed the research agenda of "cognitive computationalism", and our Chinese scholars have responded to this general view that "the essence of cognition is computation" and that (...) the human brain and computers are merely formal systems for manipulating and processing symbols in their respective fields. Professor Hong Dingguo used the concepts of "manifest order" and "hidden order", Professor Jin Gulun used the philosophical categories of "constitutive theory" and "generative theory", and Liu Yuesheng used the concept of "cognition". Professor Hong Dingguo reinterpreted the relationship between the "real world" and the "possible world" by using the concepts of "manifest sequence" and "hidden sequence", Professor Jin Wulun used the philosophical categories of "composition theory" and "generation theory", and Professor Liu Yuesheng used the generalized information paradigm of "structural information" and "exchange information". "This provides a profound elucidation of the nature of cognitive logic and the process of human cognitive activities becoming symbols through coding and puts forward theoretical limits. The current theoretical dilemma and practical difficulty of cognitive science lies in the fact that it has developed algorithmic concepts in the Turing sense that can only model the explicitly sequential part of cognitive and intellectual activity, forming a constitutive atomic abstraction, or in our terms, classical "structural information", which cannot fully explain the inner mechanisms of human mental activity and its embodied flexibility, selectivity and self-emergence. Wu Xuemou's pan-system theory does not simply rely on logic and Turing machine algorithms, but models human intelligence by treating structural information as a complex large system composed of a collection of elements and a collection of relations (exchange information: five mutual eight chips), in order to break through the narrow path of seeking only the local consistency of the system as currently done; while Wang Dekui's exploration of the three-spin biological and physical path and Huang Zhanji's abandonment of the artificial means of logic to seek the large logical natural mechanisms and Mr. Zhou Liquan's proposal of a schema for successful communication using natural language on the basis of concepts such as context and implicit connotation ("Logic — A Theory of Correct Thinking and Successful Communication"), all reflect the milestones of Chinese scholars who seek to address the deeper issues of cognitive science. We believe that along this series of explorations, combined with new results in recent years in the fields of artificial life and evolutionary computation, a new research agenda in cognitive science will emerge. (shrink)

I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of (...) simulation. I allow for several successive layers of computational models, and I discuss the relations that exist between these models, the computer, and the object under study. An aim of my proposal is to clarify the distinction between computational models and numerical methods, and to better understand the representational and the computational functions of models in simulations. (shrink)

In this contribution I outline some ideas on what the pragmatist model of habit ontology could offer us as regards the appreciation of the constitutive role that imagery plays for social action and cognition. Accordingly, a Deweyan understanding of habit would allow for an understanding of imagery in terms of embodied cognition rather than in representational terms. I first underline the motor character of imagery, and the role its embodiment in habit plays for the anticipation of action. Secondly, I (...) reconstruct Dewey's notion of imaginative rehearsal in light of contemporary, competing models of intersubjectivity such as embodied simulation theory and the narrative practice hypothesis, and argue that the Deweyan model offers us a more encompassing framework which can be useful for reconciling these approaches. In this text I am mainly concerned with sketching a broad picture of the lines along which such a project could be developed. For this reason not all questions are given equal attention, and I shall concentrate mainly on the basic ideas, without going directly into the details of many of them. (shrink)

: Serverless computing has revolutionized the way modern cloud-native applications are developed and deployed, offering a paradigm in which developers can focus solely on writing code without the burden of managing infrastructure. Among the various Function-as-a-Service (FaaS) offerings, AWS Lambda stands out as a widely adopted platform due to its automatic scaling, event-driven execution, and pay-per-use pricing model—making it ideal for applications ranging from microservices to complex event-driven workflows. However, serverless architectures present challenges in cold start latency, performance tuning, (...) and cost optimization, particularly when using heavyweight runtimes like Java or under sporadic invocation patterns. This study systematically evaluates AWS Lambda’s performance under varying load conditions by analyzing runtime performance (Java vs. Python), memory allocation, payload size, and traffic types including steady, burst, and spiky patterns. Key metrics such as cold start time, average execution duration, and cost per invocation are examined using real-world traffic simulations and observability tools like AWS CloudWatch and X-Ray. The findings demonstrate that tuning memory allocation, applying provisioned concurrency, and selecting lightweight runtimes can significantly improve latency and cost-efficiency. The paper concludes with best practices for serverless workload optimization, offering actionable insights for software architects and DevOps teams aiming to balance scalability, responsiveness, and operational cost in cloud-native deployments. (shrink)

The philosophy of science community mourns the loss of Margaret Catherine Morrison, who passed away on January 9, 2021, after a long battle with cancer. Margie, as she was known to all who knew her, was highly regarded for her influential contributions to the philosophy of science, particularly her studies of the role of models and simulations in the natural and social sciences. These contributions made her a world-leading philosopher of science, instrumental in shifting philosophers' attention from the structure of (...) scientific theories to the practice of science. Her sophisticated studies of the function of models in scientific practice drew on detailed knowledge of the theories and experiments of physics as well as the history of physics. In emphasizing the autonomy of scientific models and their interventional character, her insights had some affinity with Cartwright's and Hacking’s views on phenomenological laws, entity realism, the instrumentalist interpretation of scientific theories, and the disunity of science. But Morrison’s approach was distinguished by the conviction that the existence of unobservable entities cannot be defended independently of the theories that support their evidence, and that scientific practice cannot be adequately understood without examining the reasons for theory unification. (shrink)

Abstract: The CODES Number Framework – A Unified Resonance Model of Mathematical Constants Mathematical constants such as π, e, and φ have long been considered fundamental to geometry, growth, and self-organization in natural systems. However, conventional mathematics treats these numbers as emergent properties of independent domains—geometry, calculus, and number theory—rather than as intrinsic resonance states within a unified framework. The Chirality of Dynamic Emergent Systems (CODES) proposes that these constants are not arbitrary but instead arise as necessary phase-locked structures (...) in a prime-driven resonance field. This paper presents the CODES Number Framework, a structured classification of fundamental mathematical constants based on their role in resonance stabilization, self-organizing dynamics, and phase coherence across physics, biology, computation, and cosmology. By integrating transcendentals (π, e, φ), physical constants (h, α, G, c), computational limits (ln(2), Ω, K), and self-similarity metrics (ζ(3), β, ψ), we reveal a hidden structural symmetry governing the emergence of all complex systems. The implications of this framework are profound: probability-based interpretations of these constants give way to structured resonance models that eliminate randomness as a fundamental principle. Instead, what has been historically interpreted as statistical noise or probabilistic distributions is reframed as a function of deep prime-driven harmonic constraints, shaping everything from quantum mechanics to intelligence and cosmic expansion. By formalizing this framework, we provide the first exhaustive categorization of all essential resonance-structuring numbers, demonstrating that their presence across mathematics, physics, and cognition is not coincidental but necessary. This work challenges the traditional notion that constants are domain-specific artifacts and instead presents them as universal resonance signatures that dictate the fabric of reality itself. (shrink)

Abstract -/- -/- Current climate models rely on probability-driven emissions tracking and mitigation strategies, which fail to account for the structured resonance dynamics of planetary systems. CODES introduces a structured resonance-based framework that reinterprets environmental stability as a function of phase-locked coherence rather than stochastic fluctuations. -/- -/- This model posits that climate instability arises from resonance disruptions across atmospheric, biological, and energy systems, rather than isolated excesses of carbon or pollutants. Instead of treating emissions as independent variables, CODES (...) reformulates them as phase misalignments within a planetary-scale resonance system. -/- -/- Key insights include: -/- 1. Fossil fuel combustion disrupts structured thermal resonance, not just greenhouse gas levels. -/- 2. Industrial agriculture breaks microbial phase-locking cycles, accelerating soil entropy rather than just releasing CO₂. -/- 3. Plastic waste interferes with biological oscillatory coherence, altering cellular and ecological resonance patterns. -/- 4. Energy grids introduce chaotic frequency imbalances, amplifying entropy loss instead of optimizing power distribution. -/- 5. Urbanization follows inefficient non-fractal growth patterns, disrupting local environmental phase-locking. -/- -/- CODES reformulates these challenges through deterministic resonance corrections, replacing traditional mitigation strategies with phase-locking restoration methods. This includes: -/- • Resonance-driven energy storage and distribution systems that eliminate phase loss. -/- • Phase-matched carbon reintegration processes that restore planetary thermal equilibrium. -/- • Fibonacci-based agricultural restructuring to phase-lock soil and microbial dynamics. -/- • Resonance-aligned biomaterials that self-degrade in structured time intervals. -/- • Fractal-driven urban expansion models that prevent environmental overload and instability. -/- -/- By aligning climate solutions with structured emergence rather than probability-based interventions, CODES provides a coherence-driven framework for planetary restoration—one that does not resist entropy but guides it back into structured equilibrium. (shrink)

The aim of this paper is to discuss the “Framework for M&S with Agents” (FMSA) proposed by Zeigler et al. [2000, 2009] in regard to the diverse epistemological aims of agent simulations in social sciences. We first show that there surely are great similitudes, hence that the aim to emulate a universal “automated modeler agent” opens new ways of interactions between these two domains of M&S with agents. E.g., it can be shown that the multi-level conception at the core of (...) the FMSA is similar in both contexts: notions of “levels of system specifi cation”, “behavior of models”, “simulator”and “endomorphic agents” can be partially translated in the terms linked to the “denotational hierarchy” (DH) recently introduced in a multi-level centered epistemology of M&S. Second, we suggest considering the question of “credibility” of agent M&S in social sciences when we do not try to emulate but only to simulate target systems. Whereas a stringent and standardized treatment of the heterogeneous internal relations (in the DH) between systems of formalisms is the key problem and the essential challenge in the scope of Agent M&S driven engineering, it is urgent too to address the problem of the external relations (and of the external validity, hence of the epistemic power and credibility) of such levels of formalisms in the specific domains of agent M&S in social sciences, especially when we intend to introduce the concepts of activity tracking. (shrink)

Vehicle windshield wiper system increases the driving safety by contributing a clear shot viewing to the driver. In this paper, modelling, designing and simulation of a vehicle windshield wiper system with robust control theory is done successfully. H  loop shaping and robust pole placement controllers are used to improve the wiping speed by tracking a reference speed signals. The reference speed signals used in this paper are step and sine wave signals. Comparison of the H  loop shaping (...) and robust pole placement controllers based on the two reference signals is done and convincing results have been obtained. Finally the comparative results prove the effectiveness of the proposed H  Loop Shaping controller to improve the wiping mechanism for the given two reference signals. (shrink)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, (...) in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social sciences. (...) The most important difference between the social sciences and the natural sciences with respect to modeling is that in the social sciences powerful and well confirmed background theories (like Newtonian mechanics, quantum mechanics or the theory of relativity in physics) do not exist in the social sciences. Therefore, an epistemology of models that is formed on the role model of physics may not be appropriate for the social sciences. I discuss the challenges that modeling faces in the social sciences and point out their epistemological consequences. The most important consequences are that greater emphasis must be placed on empirical validation than on theoretical validation and that the relevance of purely theoretical simulations is strongly limited. (shrink)

Most accountants do not realize - until it is perhaps too late - that virtually all aspects of their work have an ethical dimension. They often lack the knowledge and skills to analyse issues effectively, and when confronted with conflicting ethical problems cannot choose the way to move forward that is consistent with their own values and/or the profession's code of professional conduct. This article proposes that the accounting profession worldwide has simply attempted to prescribe simplistic solutions to often complex (...) ethical problems. It endeavours to provide an alternative approach based on the process of 'ethical knowing' (Melchin, 1998). Such an approach turns the acquisition of understanding away from a focus on the external world of rules, concepts and exemplars, to the interior world of how accountants, as people, arrive at an understanding that is consistently ethical. To achieve this, the article relies on Lonergan's seminal (1957) examination of the internal processes of decision-making and the critical roles played by developing skills in data retrieval and insight in the exercise of judgment. (shrink)

I show that the Pinkerton rule in conspiracy law is doctrinally and morally flawed. Unlike past critics of the rule, I propose a statutory fix that preserves and reforms it rather than abolishing it entirely. As I will show, this accommodates authors like Neil Katyal who have defended the rule as an important crime fighting tool while also fixing most of the traditional problems with it identified by critics like Wayne LaFave. Pinkerton is a vicarious liability rule that makes conspirators (...) criminally responsible for the foreseeable crimes of their coconspirators committed in furtherance of the conspiracy. It has two big problems: (1) Doctrinally, it breaks the logic of the many state criminal codes that are based on the Model Penal Code. (2) Ethically, it infringes the culpability constraint on the criminal law by imposing excessive punishments on defendants who did not even consciously suspect that their coconspirators would commit additional crimes that were not the object of the conspiracy. These problems are most acute in Texas, where Pinkerton can be combined with capital murder charges to produce automatic life without parole sentences. The Texas example is an extreme illustration of the problem of unintended consequences when state legislators tinker with the carefully drafted, interlocking provisions of a model code. The new statute I propose would put the penal code back in order and respect the culpability constraint. In the latter aspect, it is informed by leading work in philosophical ethics on blameworthiness and culpability. (shrink)

In Reality+: Virtual Worlds and the Problems of Philosophy, David Chalmers argues, among other things, that: if we are living in a full-scale simulation, we would still enjoy broad swathes of knowledge about non-psychological entities, such as atoms and shrubs; and, our lives might still be deeply meaningful. Chalmers views these claims as at least weakly connected: The former claim helps forestall a concern that if objects in the simulation are not genuine (and so not knowable), then life (...) in the simulation is illusory and therefore, not as valuable as a non-simulated life. Taking up these questions, I argue that in general, the value of social knowledge for a meaningful life dramatically swamps the value of non-social knowledge for a meaningful life. Along the way, I propose a non-additive model of the meaningfulness of life, according to which the overall effect of some potential contributor of value to a life depends in part on what is already in a life. One upshot is that the vindication of non-social knowledge, absent a correlative vindication of social knowledge, contributes either not at all or scarcely at all to the claim that our lives in the simulation might be deeply meaningful. This is so even though the vindication of non-social knowledge does forestall the concern that in the simulation, our lives might be wholly meaningless. (shrink)

Nick Bostrom’s recently patched ‘‘simulation argument’’ (Bostrom in Philos Q 53:243–255, 2003; Bos- trom and Kulczycki in Analysis 71:54–61, 2011) purports to demonstrate the probability that we ‘‘live’’ now in an ‘‘ancestor simulation’’—that is as a simulation of a period prior to that in which a civilization more advanced than our own—‘‘post-human’’—becomes able to simulate such a state of affairs as ours. As such simulations under consid- eration resemble ‘‘brains in vats’’ (BIVs) and may appear open to (...) similar objections, the paper begins by reviewing objections to BIV-type proposals, specifically those due a presumed mad envatter. In counter example, we explore the motivating rationale behind current work in the development of psychologically realistic social simula- tions. Further concerns about rendering human cognition in a computational medium are confronted through review of current dynamic systems models of cognitive agency. In these models, aspects of the human condition are repro- duced that may in other forms be considered incomputable, i.e., political voice, predictive planning, and consciousness. The paper then argues that simulations afford a unique potential to secure a post-human future, and may be nec- essary for a pre-post-human civilization like our own to achieve and to maintain a post-human situation. Long-s- tanding philosophical interest in tools of this nature for Aristotle’s ‘‘statesman’’ and more recently for E.O. Wilson in the 1990s is observed. Self-extinction-level threats from State and individual levels of organization are compared, and a likely dependence on large-scale psychologically realistic simulations to get past self-extinction-level threats is projected. In the end, Bostrom’s basic argument for the conviction that we exist now in a simulation is reaffirmed. (shrink)

Ensemble perception—the encoding of objects by their group properties—is known to be resistant to outlier noise. However, this resistance is somewhat paradoxical: how can the visual system determine which stimuli are outliers without already having derived statistical properties of the ensemble? A simple solution would be that ensemble perception is not a simple, one-step process; instead, outliers are detected through iterative computations that identify items with high deviance from the mean and reduce their weight in the representation over time. (...) Here we tested this hypothesis. In Experiment 1, we found evidence that outliers are discounted from mean orientation judgments, extending previous results from ensemble face perception. In Experiment 2, we tested the timing of outlier rejection by having participants perform speeded judgments of sets with or without outliers. We observed significant increases in reaction time (RT) when outliers were present, but a decrease compared to no-outlier sets of matched range suggesting that range alone did not drive RTs. In Experiment 3 we tested the timing by which outlier noise reduces over time. We presented sets for variable exposure durations and found that noise decreases linearly over time. Altogether these results suggest that ensemble representations are optimized through iterative computations aimed at reducing noise. The finding that ensemble perception is an iterative process provides a useful framework for understanding contextual effects on ensemble perception. (shrink)

A recent evolution of computer simulations has led to the emergence of complex computer simulations. In particular, the need to formalize composite objects (those objects that are composed of other objects) has led to what the author suggests calling pluriformalizations, i.e. formalizations that are based on distinct sub-models which are expressed in a variety of heterogeneous symbolic languages. With the help of four case-studies, he shows that such pluriformalizations enable to formalize distinctly but simultaneously either different aspects or different parts (...) or different scales of the same object or system. From an epistemological standpoint, he suggests that this kind of computer-aided complex formalization of composite objects renew the traditional relations between computer simulations, theoretical models and operational models. (shrink)

On entend généralement par « théorie des modèles » autant la métamathématique (ou sémantique formelle) que la sémantique des modèles des sciences non formelles. Cet article a pour objet la théorie des modèles scientifiques que Mario Bunge a développée dans Method, Models and Matter (1973). J’y analyse l’intégration théorique qu’opère Bunge des sciences formelles et des sciences expérimentales ou observationnelles, laquelle prend appui sur sa philosophie des sciences. Je la compare sommairement à la théorie des modèles de Gilles-Gaston Granger dans (...) le but évident d’en dégager les ressemblances et les dissimilitudes, mais aussi leur commun point d’achoppement : l’une comme l’autre usent en effet d’un concept non analysé dont la fonction épistémologique est pourtant capitale et produit les mêmes effets. Au centre de la théorie des modèles de Bunge se trouve le concept de simulation que je comparerai à celui qui est en usage dans les sciences de l’ordinateur et qui est de nos jours largement appliqué à diverses sciences, tant sociales que naturelles. Je conclurai sur les conséquences méthodologiques et métaphysiques de la théorie bungéenne des modèles. (shrink)

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining (...) whether, and under what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results. (shrink)

‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a (...) structural analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure. (shrink)

Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types (...) of conformity that can arise in homogeneous groups: those relating to group-based trust and those connected to normative expectations towards in-groups. (shrink)