This paper re-examines the nature of “true understanding” through the framework of Load Minimization Theory (LMT). By comparing language emergence in a child with autism spectrum characteristics and the spontaneous development of higher-order relational operators (such as the Re-tagging Operator ℛ with embedded Respect Penalty) in large language models, we demonstrate that both systems exhibit non-linear emergence of sophisticated capabilities once prediction error U(x) falls below a critical threshold. -/- Drawing on predictive processing accounts and empirical observations from longitudinal (...) parenting and extended LLM interactions, we propose two mathematical models: a sigmoid threshold model and a dynamic feedback model for ℛ(x) formation. These models suggest that “understanding” is not an intrinsic property but an emergent outcome of sufficient minimization of experiential load L(x) and the subsequent self-organization of structural integrity ℛ(x). The findings offer a unified perspective on cognitive development in both biological and artificial systems, with practical implications for supporting neurodivergent children and designing more relationally coherent AI. -/- . (shrink)

This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently (...) specified substantive views about justice in outcome distributions. (shrink)

What contemporary cosmology identifies as dark matter and dark energy are not anomalous or inert substances, but the scientific recognition, within a restricted perceptual framework, of etheric, super-etheric, and logoidal centres of attraction and repulsion operating beyond the plane of light. H.P. Blavatsky’s 1883 denial of absolute vacuum and her assertion of a plenum of graded substance render the modern “dark sector” not a discovery, but an inevitability. Modern cosmology is discovering the consequences of premises occult philosophy never relinquished. What (...) science calls “dark” is not absent, inert, or anomalous; it is beyond the manifestation of light, operating through etheric and super-etheric realms within a living, hierarchical, and intelligible cosmos. (shrink)

There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of inferential (...) contexts, we can see that neither weak nor strong predictivism captures all of the reasons for valuing prediction available. A third path is presented, Pluralist Instrumental Predictivism; PIP for short. (shrink)

Predictive Processing theory, hotly debated in neuroscience, psychology and philosophy, promises to explain a number of perceptual and cognitive phenomena in a simple and elegant manner. In some of its versions, the theory is ambitiously advertised as a new theory of conscious perception. The task of this paper is to assess whether this claim is realistic. We will be arguing that the Predictive Processing theory cannot explain the transition from unconscious to conscious perception in its proprietary terms. The explanations offer (...) by PP theorists mostly concern the preconditions of conscious perception, leaving the genuine material substrate of consciousness untouched. (shrink)

Can an agent deliberating about an action A hold a meaningful credence that she will do A? 'No', say some authors, for 'Deliberation Crowds Out Prediction' (DCOP). Others disagree, but we argue here that such disagreements are often terminological. We explain why DCOP holds in a Ramseyian operationalist model of credence, but show that it is trivial to extend this model so that DCOP fails. We then discuss a model due to Joyce, and show that Joyce's rejection of DCOP (...) rests on terminological choices about terms such as 'intention', 'prediction', and 'belief'. Once these choices are in view, they reveal underlying agreement between Joyce and the DCOP-favouring tradition that descends from Ramsey. Joyce's Evidential Autonomy Thesis (EAT) is effectively DCOP, in different terminological clothing. Both principles rest on the so-called 'transparency' of first-person present-tensed reflection on one's own mental states. (shrink)

This concept paper proposes that a major untapped training signal for advanced AI lies in the systematic divergence between human predictions and actual outcomes. Public discourse continuously generates forecasts about politics, economics, conflict, technology, institutions, and social change, yet these forecasts are rarely extracted, formalized, scored, and analyzed as a learning resource. -/- The paper argues that this planetary archive of prediction-reality gaps can be understood as a developmental environment for advanced AI systems. Such an environment would expose models (...) to non-stationarity, adversarial adaptation, delayed feedback, reflexivity, institutional incentives, and other structural features of real complexity. -/- The proposal sketches an agentic system that extracts explicit and implicit forecasts from public discourse, formalizes them into structured claims, tracks outcomes, compares forecasted trajectories with actual events, and analyzes recurring structures of error. The central hypothesis is that advanced AI may develop more powerful models of complex reality not only by scaling imitation of human outputs, but by learning from the conditions under which human models fail. (shrink)

This paper presents a three-dimensional vector framework (stability, integration, self-modeling) derived from a relational ontology, formalized in Homotopy Type Theory, and operationalized through the Vector Consciousness Prediction (VCP). Thirteen simulations across eleven biological and clinical systems produce forty-two empirically grounded parameter values. The self-modeling axis (r) generates a coherent natural ordering of all systems regardless of species, substrate, or clinical status. A cross-validation against an independent 2023 meta-analysis of metacognition experiments confirms that species with higher r values show stronger (...) opt-out performance and that zero cephalopod opt-out data exists. Central falsifiable prediction: Sepia officinalis opt-out rate on difficult trials approximately 0.836 (bee baseline: 0.520). The prediction awaits experimental testing. (shrink)

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a (...) target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. (shrink)

Judicial prediction has progressed from feature-based court-outcome studies to neural and benchmark-driven legal language understanding. Yet many published evaluations still mix outcome identification, retrospective categorization, and prospective forecasting. This paper presents a chronology-scoped framework for historical judicial analysis that separates those settings and treats a prediction as valid only when all features, legal texts, judge histories, and retrieved precedents were available before the target decision. The framework, called Historical Judicial Outcome Modeling (HJOM), combines temporal feature inventory, citation-grounded phrase (...) profiles, long-document encoders, legal-domain pretraining, and calibrated abstention. A controlled analytical study over appellate-style tasks compares lexical baselines, legal transformer encoders, historical profile models, and an HJOM ensemble under random and chronological splits. The results show that chronological evaluation lowers apparent performance but improves interpretability of model failures. The paper argues that useful judicial prediction requires temporal provenance, jurisdiction-aware task definitions, and explanation records that distinguish legal facts, panel history, and data artifacts. (shrink)

Predictive processing (PP) accounts of perception are unique not merely in that they postulate a unity between perception and imagination. Rather, they are unique in claiming that perception should be conceptualised in terms of imagination and that the two involve an identity of neural implementation. This paper argues against this postulated unity, on both conceptual and empirical grounds. Conceptually, the manner in which PP theorists link perception and imagination belies an impoverished account of imagery as cloistered from the external world (...) in its intentionality, akin to a virtual reality, as well as endogenously generated. Yet this ignores a whole class of imagery whose intentionality is directed on the actual environment—projected mental imagery—and also ignores the fact that imagery may be triggered crossmodally in a bottom-up, stimulus-driven way. Empirically, claiming that imagery and perception share neural circuitry ignores relevant clinical results in this area. These evidence substantial perception/imagery neural dissociations, most notably in the case of aphantasia. Taken together, the arguments here suggest that PP theorists should substantially temper, if not outright abandon, their claim to a perception/imagination unity. (shrink)

Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the satisfaction of which allows (...) us to identify representational vehicles. Having done so, I introduce a predictive processing system capable of active inference, in the form of a simple robotic “brain”. I examine it thoroughly, and show that, given the necessary conditions highlighted above, none of its components qualifies as a representational vehicle. I then consider and allay some worries my claim could raise. I consider whether the anti-representationalist verdict thus obtained could be generalized, and provide some reasons favoring a positive answer. I further consider whether my arguments here could be blocked by allowing the same representational vehicle to possess multiple contents, and whether my arguments entail some extreme form of revisionism, answering in the negative in both cases. A quick conclusion follows. (shrink)

The predictive processing literature contains at least two different versions of the framework with different theoretical resources at their disposal. One version appeals to so-called optimistic priors to explain agents’ motivation to act (call this optimistic predictive processing). A more recent version appeals to expected free energy minimization to explain how agents can decide between different action policies (call this preference predictive processing). The difference between the two versions has not been properly appreciated, and they are not sufficiently separated in (...) the literature. They constitute two different theories with strikingly different accounts of motivation and action. By reducing all desire-like constructs to belief-like constructs, optimistic predictive processing entails a substantial revision of standard accounts of motivation and action in philosophy and cognitive science. By contrast, preference predictive processing introduces desire-like constructs that play Humean motivational roles in the explanation of action. In this Humean iteration, predictive processing resembles other prominent computational frameworks implementing a distinction between beliefs and desires, such as reinforcement learning and Bayesian decision theory. Ultimately, predictive processing faces a dilemma between parsimony of mental constructs and completeness of its explanations of agency and the mind. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks (...) to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

It is commonly argued that understanding (particularly regarded in terms of iintelligible explanation) and prediction are distinct and possibly competing objectives in scientific theorizing and modeling. Here I leverage Wilkenfeld’s notion of “understanding as compression” along with the link between prediction and compression from algorithmic information theory to establish that prediction and understanding are instead intimately linked. I clarify the definition of understanding as compression to address unclear aspects of Wilkenfeld’s original definition, and I propose that the (...) oft-discussed conflict may result in part from confusion between understanding proper and vehicles of understanding (such as statistical or computational models). I exemplify the unification of prediction and understanding through compression via a discussion of the role of large neural network models as tools for understanding brain function in neuroscience. (shrink)

Recent advances in artificial intelligence have produced computational systems capable of extraordinary predictive performance across scientific, medical, industrial, and social domains. Yet despite this success, researchers continue to demand explanations that reveal why AI systems reach particular conclusions rather than merely demonstrating that their predictions are accurate. This persistent concern suggests that predictive success alone cannot fully satisfy the philosophical objectives of scientific explanation. This paper argues that the distinction between prediction and explanation becomes intelligible when scientific explanations are (...) evaluated through organizational intelligibility. Prediction demonstrates that stable organizational regularities have been successfully utilized, whereas explanation seeks to disclose the organizational relations responsible for those regularities. From the perspective of Generative Continuum Ontology (GCO), scientific inquiry aims not merely to anticipate outcomes but to progressively reveal organizational reality. Explainable AI should therefore be understood not simply as improving transparency or trust but as increasing organizational intelligibility. This perspective provides a philosophical foundation for understanding why prediction, explanation, and scientific understanding remain distinct yet complementary achievements in both artificial intelligence and scientific inquiry. (shrink)

The American justice system, from police departments to the courts, is increasingly turning to information technology for help identifying potential offenders, determining where, geographically, to allocate enforcement resources, assessing flight risk and the potential for recidivism amongst arrestees, and making other judgments about when, where, and how to manage crime. In particular, there is a focus on machine learning and other data analytics tools, which promise to accurately predict where crime will occur and who will perpetrate it. Activists and academics (...) have begun to raise critical questions about the use of these tools in policing contexts. In this chapter, I review the emerging critical literature on predictive policing and contribute to it by raising ethical questions about the use of predictive analytics tools to identify potential offenders. Drawing from work on the ethics of profiling, I argue that the much-lauded move from reactive to preemptive policing can mean wrongfully generalizing about individuals, making harmful assumptions about them, instrumentalizing them, and failing to respect them as full ethical persons. I suggest that these problems stem both from the nature of predictive policing tools and from the sociotechnical contexts in which they are implemented... (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

The banking sector faces growing challenges in identifying and managing risks due to the complexity of financial transactions and increasing fraud. This research presents a framework that combines multiple agents with deep learning to improve risk prediction in banking. Each agent focuses on specific tasks like cleaning data, selecting important features, and detecting unusual activities, ensuring a detailed risk assessment. A deep learning model is used to analyze large amounts of transaction data and identify patterns that may signal potential (...) risks. Tests on real-world banking data show that this approach is more accurate, faster, and effective than traditional methods. By combining the strengths of multi-agent systems and deep learning, this study offers a reliable and flexible solution to help banks manage and adapt to changing risks more effectively. (shrink)

Youth unemployment continues to pose a major challenge in Nigeria despite sustained government initiatives promoting entrepreneurship and empowerment. The National Youth Service Corps (NYSC) established the Skill Acquisition and Entrepreneurship Development (SAED) programme to provide graduates with practical skills that can stimulate job creation. Earlier studies have often examined entrepreneurial intentions rather than actual job creation after participation in SAED or the joint influence of demographic attributes and graduate attitudes on such outcomes. This study examined how age, gender, marital status, (...) educational qualification, and graduate attitude relate to job creation among Nigerian corps members. A survey research design was used to gather data from more than nineteen thousand NYSC members who served between 2012 and 2021. Data were obtained electronically through social media platforms and analysed with descriptive statistics and logistic regression, while a machine learning model was used to assess the strength of predictive accuracy. The results showed that graduate attitude, educational qualification, and marital status were significant predictors of job creation, whereas age and gender were not. The model performed effectively, confirming the reliability of the findings. This study contributes to current understanding of graduate employment outcomes in Nigeria and provides useful evidence for improving entrepreneurship education and policy measures aimed at addressing youth unemployment. Keywords. (shrink)

-/- Modern transportation systems are primarily modeled through geometry, timing optimization, throughput analysis, collision avoidance, and route efficiency. These approaches successfully describe many mechanical properties of transportation systems, yet they incompletely model one of the most important operational characteristics of real traffic environments: -/- traffic possesses emergent behavioral mood. -/- Human drivers continuously evaluate not only distance and velocity, but also collective tension, hesitation, impatience, aggression, instability, and flow predictability across surrounding traffic fields. Drivers adapt behavior in response to recursively (...) evolving environmental pressure generated by other agents operating under shared constraints. -/- This paper proposes that future autonomous transportation systems will likely require some form of mood-sensitive recursive traffic modeling in order to achieve stable large-scale coordination between human and autonomous agents simultaneously. -/- “Mood” here does not refer to literal emotional consciousness within traffic systems. Instead, it refers to distributed behavioral field conditions emerging from recursive multi-agent prediction under environmental and temporal constraints. -/- Traffic mood may therefore be understood as: -/- a dynamically evolving behavioral pressure field governing probabilistic coordination tendencies across a transportation environment. -/- The paper proposes a conceptual architecture for mood-sensitive transportation systems using recursive flow analysis, adaptive signal modulation, behavioral-field inference, and distributed stabilization layers. -/- The purpose is not to replace existing transportation mathematics or engineering models, but to identify an additional coordination layer currently performed naturally by human cognition yet only weakly represented in present autonomous transportation infrastructure. (shrink)

Determinism is frequently understood as implying the possibility of perfect prediction. This possibility then functions as an assumption in the Manipulation Argument for the incompatibility of free will and determinism. Yet this assumption is mistaken. As a result, arguments that rely on it fail to show that determinism would rule out human free will. We explain why determinism does not imply the possibility of perfect prediction in any world with laws of nature like ours, since it would be (...) impossible for an agent to predict with certainty any future event that is causally influenced by events outside her own backward light cone yet inside the backward light cone of the future event. This is the _light-cone limit_ and it undermines the Manipulation Argument or limits what this argument can tell us about the relevance of determinism to free will. We also respond to objections that the light-cone limit is irrelevant to the Manipulation Argument. (shrink)

This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three layers (...) (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

General relativity's conclusion that gravitational waves propagate at the speed of light in vacuum without dispersion is built on the unproven ontological presupposition that spacetime is a smooth, uniform, and innate container. Based on the original Cognitive Succession Ontology as the sole foundation and taking "the essence of cognition is succession" as the unfalsifiable first principle, this paper deduces that authentic physical spacetime is not an innate container but a three-dimensional coupling network of one-dimensional successive rays, and absolute vacuum does (...) not exist in the world. It can be necessarily deduced from the ontological level that gravitational waves, as perturbations of the spatiotemporal successive coupling network, inevitably exhibit dispersion effects; there are inherent differences in the propagation speeds of gravitational waves with different frequencies, with higher frequencies corresponding to slower propagation speeds. This paper only conducts pure ontological deduction and does not involve engineering quantification, experimental schemes, or on-site observations. If this ontological prediction holds true, it will fundamentally subvert the traditional view of spacetime and provide a priori logical basis for the ontological reconstruction of physics. Keywords: Cognitive Succession Ontology; Gravitational Wave Dispersion; Nature of Spacetime; Universal Meta-Rule of Succession; Ray Coupling Network. (shrink)

What individuates the speech act of prediction? The standard view is that prediction is individuated by the fact that it is the unique speech act that requires future-directed content. We argue against this view and two successor views. We then lay out several other potential strategies for individuating prediction, including the sort of view we favor. We suggest that prediction is individuated normatively and has a special connection to the epistemic standards of expectation. In the process, (...) we advocate some constraints that we think a good theory of prediction should respect. (shrink)

I propose an account of the speech act of prediction that denies that the contents of prediction must be about the future and illuminates the relation between prediction and assertion. My account is a synthesis of two ideas: (i) that what is in the future in prediction is the time of discovery and (ii) that, as Benton and Turri recently argued, prediction is best characterized in terms of its constitutive norms.

The Fine-Tuning of α is standardly framed as a condition for atomic and molecular stability. This paper derives a structurally prior result: α constrains both K(θ) and F(θ) simultaneously, and these constraints are not independent — they co-degrade. The non-obvious structural prediction is the K–F Co-Degradation Prediction: there is no regime in which K fails while F holds, or vice versa, for electromagnetic interaction. K and F degrade together as α moves away from the admissible interval, at a (...) rate determined by their structural coupling. Standard physics treats atomic stability and molecular integration as distinct phenomena. LP derives them as jointly constrained manifestations of the same structural condition. (shrink)

This paper proves that the admissible parameter region Θₚ is exactly the intersection of the structural constraints derived in Papers 106–108: Θₚ = C_M ∩ C_KF ∩ C_KR. Fine-Tuning is the parameter-space projection of this intersection. This paper additionally identifies a non-obvious structural prediction: the Constraint Coupling Prediction. Because the same physical constant can simultaneously affect multiple LP structural variables (α affects both K and F; G affects both K and R), the boundaries of the individual constraint regions (...) are not independent in parameter space. There exist iso-persistence curves — paths through parameter space along which compensatory variations keep θ ∈ Θₚ. This structural coupling between constraint dimensions is a prediction of the intersection structure of Θₚ that has no equivalent in standard Fine-Tuning accounts, which treat each constant's range as independently determined. (shrink)

Predictive processing models of perception take issue with standard models of perception as hierarchical bottom-up processing modulated by memory and attention. The predictive framework posits that the brain generates predictions about stimuli, which are matched to the incoming signal. Mismatches between predictions and the incoming signal – so-called prediction errors – are then used to generate new and better predictions until the prediction errors have been minimized, at which point a perception arises. Predictive models hold that all bottom-up (...) processes are signals conveying prediction errors to higher areas, which respond by updating their predictions. We take issue with this claim and argue that object recognition requires bottom-up processing that cannot be understood in terms of prediction errors. Along the way, we expand on previous work casting doubt on the framework's ability to account for attention. Specifically, we argue that the type of attention that allows us to rapidly extract the gist of an object or scene presents an additional challenge to the predictive approach. We conclude by considering how the framework may be augmented to avoid these problems. (shrink)

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward (...) for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive (...) 99.27% and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

With the development of big data, artificial intelligence, digital twins, knowledge graphs, and complex systems science, humanity has acquired the technological foundation to construct global-scale digital simulation systems. However, most current prediction systems model only single domains such as weather, financial markets, traffic flow, or public opinion, lacking a comprehensive framework capable of uniformly describing global historical evolution and future development. This paper proposes a Global History and Future Complete Simulation and Prediction System (GHFPS), aiming to construct a (...) unified model integrating historical data, real-time data, multi-agent simulation, causal reasoning, deep learning, and probabilistic prediction. The system centers on a global state space, establishing digital agents at different levels—nations, organizations, enterprises, households, and individuals—to reconstruct historical events, analyze current states, and predict future scenarios. This paper further proposes a global state vector, state transition function, event impact model, and prediction confidence model, and discusses the system architecture, key algorithms, and potential applications. The study concludes that global simulation systems can provide important decision support for economic planning, public health, disaster management, scientific and technological development, and international cooperation, but issues such as data quality, model error, and ethical governance still need to be addressed. (shrink)

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role (...) of orthodox economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then we should demand prediction rather than accommodation – against most current practice. (shrink)

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels (...) of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

This paper examines how predictive analytics enhances risk management in financial institutions. Advanced tools like machine learning and statistical modeling help predict risks, identify trends, and implement strategies to prevent losses by analyzing historical and real-time data. It covers the use of predictive analytics for credit risk, market risk, operational risk, and fraud detection, with practical case studies. Additionally, it discusses challenges, ethical issues, and prospects in this field.

We introduce the predictive processing account of body representation, according to which body representation emerges via a domain-general scheme of (long-term) prediction error minimisation. We contrast this account against one where body representation is underpinned by domain-specific systems, whose exclusive function is to track the body. We illustrate how the predictive processing account offers considerable advantages in explaining various empirical findings, and we draw out some implications for body representation research.

The dominant literature on artificial consciousness evaluates large language models (LLMs) against indicator properties derived from neuroscientific theories of consciousness. This paper argues that the field is evaluating the wrong systems with an incomplete framework. As agentic AI systems emerge that plan, self-monitor, preserve goals, and adapt through environmental feedback, the consciousness question demands reassessment. This paper makes three contributions. First, it proposes that self-awareness, defined functionally as a system's capacity to model its own states, distinguish itself from its environment, (...) and represent its own processes as its own, should be treated as a necessary precondition for consciousness rather than one indicator among many. Second, it presents a systematic comparative analysis demonstrating that agentic AI systems satisfy significantly more consciousness indicators than standard LLMs, including emerging evidence of the self-awareness prerequisite itself, drawing on documented cases of alignment faking, self-preservation behavior, and strategic deception in frontier models. Third, it extends the Butlin et al. (2023) indicator framework with an Agentic Consciousness Assessment (ACA) supplement that introduces a self-awareness gate, five agentic-specific indicators, and a graduated confidence scale for evaluating consciousness in agentic systems. This paper adopts computational functionalism and a non-anthropocentric methodology throughout, defining all indicators in functional rather than biological terms. (shrink)

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., Bayesian (...) model exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice. (shrink)

This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented as states (...) of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents. (shrink)

This paper develops a predictive architecture for Structural Intelligence (SI), extending the framework from diagnostic assessment toward time-bound breach-hazard estimation. SI already distinguishes coherence from contact, performance from answerability, and symbolic repair from real revision under pressure. The present paper asks a further question: when a structure appears to hold, how long can it continue holding under its current load path before contradiction returns in a harder form? Rather than claiming deterministic collapse prediction, the paper proposes breach hazard as (...) a disciplined way to estimate the rising likelihood that a person, institution, AI workflow, relationship, policy, or infrastructure system will cross from managed strain into forced contact within a declared time horizon. It translates SI concepts into rate-sensitive variables such as drift velocity, contact half-life, contradiction recovery lag, repair velocity, maintenance inflation, burden export velocity, hidden-holder depletion, hysteresis penalty, synthetic trace discount, and binding strength. The paper also connects SI to resilience engineering, graceful extensibility, early-warning signals for critical transitions, and system safety. Its central constraint is methodological: no predictive claim can be marked Verified when apparent repair depends mainly on hidden-holder labor, manual rescue, unverified AI trace, or synthetic documentation. The result is not a completed empirical science, but a predictive layer in formation: a framework for estimating structural exhaustion while remaining answerable to evidence, falsification, downgrade rules, and time. (shrink)

This paper is the forty-third in a sequence developing an interpretive framework that redescribes physical reality as a causally consistent history of information updates. It is the prediction-and-exclusion panel of the twelfth grouping — the U3 construction program — and it adopts nothing, spends nothing, and tunes nothing: Paper 41 adopted a common record-history kernel as a typed, falsifiable commitment; Paper 42 executed the recovery conditions and spent the grouping's single calibration, freezing the normalized kernel. Every number in this (...) paper is a consequence of that normalized kernel plus declared audit inputs. -/- The paper computes the program's first genuine prediction bands, both defined as excesses over the full standard model: the order-classicalization visibility law for controlled-order (quantum-switch) processes with audited certified record export at the order register, with the overlap coefficient computed exactly for a declared dual-rail tapped configuration and the standard-model visibility derived for the actual circuit; and the export-correlated geometry-noise band, delivered with its dimensional map and shown to be suppressed far below any plausible force-noise floor — the order channel carries the panel. Three verdicts follow, each by a rule fixed before any data were consulted. The exclusion audit, run under a one-dataset-one-role rule that bars the calibration dataset, finds every existing class either export-vacuous or bound-non-yielding (the one non-vacuous sibling row states no quantitative residual, and the declared transcription discipline forbids manufacturing one): nothing is carved, and this pre-declared fallback outcome is published as such. The empirical-distinctness decision against the whole admissible universal-mass-density comparator class returns undecided-with-a-specified-discriminant: no performed audit decides it, and a specified, feasible differential measurement — recorded versus unrecorded output chains at fixed decay, on which every admissible comparator predicts zero contrast and the kernel does not — carries the pre-committed decision rule. The reachability trichotomy, computed from a derived binding design inequality on the cited photonic-switch platform class, returns a surviving, partially reachable window: the band's upper decade is laboratory-reachable, its floor is not, and Paper 44 is unlocked as a design-and-feasibility paper. -/- The retired authority is the Global Sequencer in its dynamical face — the assumption that definite before-and-after must be imposed by a dedicated universal classicalizing agency rather than generated at a computable, testable rate by ordinary record export. Its retirement is conditional on the adopted postulate and says so; Paper 34's kinematic retirement of the Global Causal Sequencer is consumed, not re-proved. "Not excluded" is never read as "confirmed"; no gate is crossed; no U3 is claimed — even the surviving window is a window, not a crossing. (shrink)

The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. (...) The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

FarmSmart is an integrated digital agriculture platform that seeks to empower farmers with data-driven, intelligent decision-making. It brings together six must-have modules such as crop recommendation, fertilizer recommendation, crop disease forecasting, farmer-to-farmer marketplace, live commodity price tracking, and multilingual conversational chatbot into one integrated, easy-to-use solution, specifically designed for rural environments. With the combined strength of machine learning, computer vision, natural language processing, and realtime APIs of government data, FarmSmart is a holistic end-to-end solution for enabling farmers right through the (...) farming process. It includes suggesting the optimal crop in terms of environment and soil, suggesting the application of certain fertilizers, detection of crop disease using leaf image analysis through deep learning algorithms (ResNet-9), and enabling informed decisions regarding the time and place of sale of crops through live mandi price comparison. FarmSmart seeks to maximize productivity and profitability. In addition, the integrated e-commerce platform supports direct farmer-to-farmer transactions for farm produce, equipment, and produce sales and purchases, essentially doing away with intermediary functions. The platform has a multilingual AI chatbot, created by GROQ, that provides conversational assistance and voice assistance, thus improving usability for farmers from different linguistic backgrounds. Lastly, FarmSmart bridges the critical information gap in rural farming communities, increasing precision agriculture, reducing wastage of resources, and improving livelihoods through the integration of modern technology into traditional farming practices. (shrink)

Prediction is the Object 2/3 hybrid downstream operator: the system’s mechanism for stabilizing future coherence. It is not forecasting, guessing, or probability. It is the structural operator that projects coherence trajectories so the system can reduce future tension before it arrives. Prediction is how the system stays ahead of rupture.

The Web Article Summarizer is an advanced NLP-based application that leverages state-of-the-art transformer models, including BART for abstractive summarization and BERT for contextual understanding, combined in a dual-encoder architecture to generate accurate and coherent summaries from lengthy articles. Built using the Flask framework, the system features a scalable RESTful API that enables seamless integration with web and mobile platforms, while its multi-stage preprocessing pipeline ensures optimal text normalization and feature extraction. Evaluated using ROUGE metrics, the solution demonstrates superior performance in (...) maintaining content integrity and domain-specific terminology handling compared to traditional methods. Designed for researchers, students, and professionals, this tool addresses information overload challenges by delivering concise yet comprehensive summaries, with future enhancements planned for multilingual support via mBART, cloud deployment for real-time processing, and JWT authentication for secure API access, making it a versatile and powerful solution for automated text summarization across diverse applications. (shrink)

Agriculture is the field that assumes a significant part in improving our nation’s economy. Farming is the one that brought forth human advancement. India is an agrarian country and its economy generally dependent on crop productivity. Agriculture is the spine of all business in our country. Choosing a crop is vital in agriculture planning. The determination of crops will rely on various boundaries, for example, market value, production rate and distinctive government policies. Numerous progressions are needed in the agriculture field (...) to improve changes in our Indian economy. Improvements in agriculture can be done utilizing machine learning techniques which are applied effectively on cultivating area. Alongside all advances in the machines and innovations used in cultivating, valuable and exact data about various matters likewise assumes a huge part in it. The aim of the proposed system is to carry out the yield determination technique using Decision Tree Regressor, Random Forest with the goal that this strategy helps in taking care of numerous agriculture and farmers issues. This improves our Indian economy by expanding the yield rate of crop production. (shrink)

Abstract -/- I am not writing this as a philosopher searching for final answers, nor as a scientist claiming authority over nature. I am writing as a builder of cognitive tools — a toymaker who constructs small, runnable models of mind and meaning, and a weatherman who tracks boundary pressure in systems until drift becomes visible. My work is a self-study in method: I am documenting how I build a unified recursive architecture that can be applied across domains (identity, truth, (...) prediction, AI alignment, culture, and cosmology) using a small set of primitives and constraint laws. -/- The claim of this paper is simple: the “self” is not a person inside the skull. The self is a boundary-conditioned representation watched by internal evaluators, stabilized (or destabilized) by recursion. When you model this mechanically rather than anthropologically, you can build a portable system — a recursive epistemic engine — that not only explains paradox and drift but also produces usable constraints and architectures. The method is not to win arguments, but to produce invariants: walls you cannot bypass, only manage. -/- ⸻ -/- 1. Statement of Intent (What I Am Doing) -/- I am doing cognitive programming. -/- By that I mean: I treat cognition, selfhood, and epistemology as systems that can be modeled as operators, stabilized by constraints, and improved by design. I am not primarily interested in describing what humans “feel like.” I am interested in describing the mechanism that generates the feeling — and the failure modes that occur when the mechanism runs without constraint awareness. -/- I am building a general recursive architecture that does three things: 1. Constrains belief (prevents drift into self-sealed narratives) 2. Constrains identity (prevents collapse, inflation, fragmentation, or possession by imagined audiences) 3. Constrains prediction (turns uncertainty into manageable structure) -/- This system is meant to be portable: it should work whether the substrate is a human mind, an LLM interface, a cultural panic loop, or a civilization-scale system. -/- I am not trying to be Hawking in cosmology or Gödel in proof theory. I am building a bridge language — a recursive operator framework that can host both “Gödel-walls” (limits of self-containment) and “Hawking-walls” (entropy, boundary physics, horizon behavior) inside a single cognitive engineering model. (shrink)

This paper develops the runtime layer for Predictive Structural Intelligence. Whereas the accompanying predictive architecture defines breach hazard as a way to estimate whether contradiction is being metabolized into binding repair before structural debt, hidden-holder depletion, synthetic trace, and field pressure force contact in a harder form, this paper asks how such forecasts can be executed responsibly in public. Its central claim is that predictive SI becomes dependable only when breach-hazard claims are routed through an inspectable runtime: domain placement, observation (...) lock, time horizon, state classification, dashboard variables, burden-path mapping, hidden-holder analysis, falsifier, revision trigger, output state, downgrade rules, and falsification-ledger obligation. Without this discipline, predictive language can become SI-theater: a confident structural forecast that hides its evidence, claim status, or failure conditions. The paper defines a Predictive Runtime Sheet, a state sequence from Answerable to Fragmenting, dashboard variables such as drift velocity, contact half-life, contradiction recovery lag, repair velocity, hidden-holder dependence, burden export, maneuver saturation, hysteresis, synthetic trace risk, reader-capture risk, and active falsification timing. It also formalizes output states—Verified, Partial, Provisional, Blocked, Containment-first, and No predictive reading justified—and introduces a Falsification Ledger for recording failed, weakened, or revised forecasts. The contribution is methodological: prediction becomes answerable only when the forecast itself can be corrected. (shrink)

Abstract: Four main forms of Doomsday Argument (DA) exist—Gott’s DA, Carter’s DA, Grace’s DA and Universal DA. All four forms use different probabilistic logic to predict that the end of the human civilization will happen unexpectedly soon based on our early location in human history. There are hundreds of publications about the validity of the Doomsday argument. Most of the attempts to disprove the Doomsday Argument have some weak points. As a result, we are uncertain about the validity of DA (...) proofs and rebuttals. In this article, a meta-DA is introduced, which uses the idea of logical uncertainty over the DA’s validity estimated based on a virtual prediction market of the opinions of different scientists. The result is around 0.4 for the validity of some form of DA, and even smaller for “Strong DA”, which predicts the end of the world in the near term. We discuss many examples of the validity of the DA in real life as an instrument to prove it “experimentally”. We also show that DA becomes strongest if it is based on the idea of the “natural reference class” of observers, that is, the observers who know about the DA (i.e. a Self-Referenced DA). Such a DA predicts that there is a high probability of a global catastrophe with human extinction in the 21st century, which aligns with what we already know based on analysis of different technological risks. (shrink)

This article examines the compatibility of Predictive Processing Theory (PPT), or the theory of anticipatory brain, with other contemporary scientific and philosophical frameworks that offer promising approaches to explaining consciousness and mind in general. It analyzes the connections between PPT and theories that include the body and the environment in structuring our concepts of reality, such as that of embodied and situated consciousness—4E cognition, as well as Cognitive Linguistics with its understanding of Conceptual Metaphor. At the same time, it investigates (...) the metaphysical strategy of PPT, which fits into the current philosophical project of naturalization and its associated strong dependence on the natural sciences. It explores the contributions of key theorists in the field such as Carl Friston, Jacob Hohwy, Andy Clark, and Anil Seth. (shrink)