COLLECTIONS

Documents published in Scipedia

• Colonization of Time: How Predictive Models Replace the Future as a Social Structure

  A. Startari

  Colonization of Time: How Predictive Models Replace the Future as a Social Structure (Select a year).

  
  

  Abstract

  This article proposes that contemporary predictive models do not merely anticipate the future but structurally replace it. Drawing from computational epistemology, formal grammar theory, and digital sociology, we introduce the concept of algorithmic colonization of time. In this framework, predictive algorithms function not as neutral tools of foresight but as agents that overwrite the horizon of uncertainty with pre-modeled sequences. Where human temporal experience traditionally revolved around openness, chance, and decision, algorithmic systems reduce the future to a field of executable outcomes. This replacement is not metaphoric: it manifests in social platforms, recommendation engines, and predictive analytics in justice, health, and security. The model becomes a closure device, optimizing trajectories rather than interpreting possibilities. We present a formal operator to describe this transition: ΔS(x)⇒Af(x) where ΔS(x) represents the syntactic derivation of input structures and Af(x) their optimized anticipatory execution. This formulation accounts for the structural shift from contingency to iteration, from human agency to algorithmic deployment. Finally, the paper argues that while this colonization is expansive, it is not total. Anomalous margins—spaces not yet computed—persist. These residual gaps are the new sites of resistance against temporal closure. Thus, the future no longer arrives: it is executed.\

  Abstract
  This article proposes that contemporary predictive models do not merely anticipate the future but structurally replace it. Drawing from computational epistemology, formal [...]

  • 0
  •  read

• Expense Coding Syntax: Misclassification in AI-Powered Corporate ERPs

  A. Startari

  Grammars of Power (2025). 2

  
  

  Abstract

  This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transformerbased classifiers on labeled accounting data to predict expense categories and observed that these models frequently relied on grammatical form rather than financial semantics. We extracted syntactic features including nominalization frequency, defined as the ratio of deverbal nouns to verbs; coordination depth, measured by the maximum depth of coordinated clauses; and subordination complexity, expressed as the number of embedded subordinate clauses per sentence. Using SHAP (SHapley Additive exPlanations), we identified that these structural patterns significantly contribute to false allocations, thus increasing the likelihood of audit discrepancies. For interpretability, we applied the method introduced by Lundberg and Lee in their seminal work, “A Unified Approach to Interpreting Model Predictions,” published in Advances in Neural Information Processing Systems 30 (2017): 4765–4774. To mitigate these syntactic biases, we implemented a rule-based debiasing module that reparses each narrative into a standardized fair-syntax transformation, structured around a

  Abstract
  This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transformerbased classifiers [...]

  • 0
  •  read

• Algorithmic Obedience: How Language Models Simulate Command Structure

  A. Startari

  Algorithmic Obedience: How Language Models Simulate Command Structure (2025).

  
  

  Abstract

  This paper introduces the concept of algorithmic obedience to describe how large language models (LLMs) simulate command structures without consciousness, intent, or comprehension. Drawing on syntactic theory, discourse analysis, and computational logic, we argue that LLMs perform obedience without agency—executing prompts not semantically, but structurally. We formalize this through the Theorem of Disembedded Syntactic Authority, which states that authority in language models arises from structural executability, not truth, belief, or referential grounding. Using a mathematical formulation, we model prompt-response cycles as syntactic command structures and apply the theory to major systems such as ChatGPT, Claude, and Gemini. The paper concludes by outlining the epistemological, ontological, and political risks of treating structurally obedient outputs as authoritative knowledge.

  Abstract
  This paper introduces the concept of algorithmic obedience to describe how large language models (LLMs) simulate command structures without consciousness, intent, or comprehension. [...]

  • 0
  •  read

• Crypto Whitepaper Syntactic Sovereignty: Persuasive Grammar as Financial Authority

  A. Startari, A. Startari

  The Artificial Intelligence in Energy Revolution: Understanding the Market and Its Impact (2025). 1

  
  

  Abstract

  This article investigates how persuasive syntactic structures embedded in AI-generated crypto whitepapers function as a vehicle of financial authority. Drawing from a curated corpus of 10,000 whitepapers linked to token launches between January 2022 and March 2025, we apply transformer-based dependency parsing to extract high-weighted grammatical features, including nested conditionals, modality clusters, and assertive clause chaining. We operate these patterns via a ''Deceptive Syntax Anomaly Detector'' (DSAD), which computes a syntactic risk index and identifies recurrent grammar configurations statistically correlated with anomalous capital inflows and subsequent collapses (Spearman correlation, ρ > 0.4, p < 0.01). Unlike prior studies focused on semantic deception or metadata irregularities, we model ''syntactic sovereignty'', the systematic use of syntax to establish non-human authority, as the groundwork of investor persuasion. We find that abrupt shifts in syntactic entropy, especially in modal intensifiers and future-perfect projections, consistently occur in documents associated with short-lived or fraudulent tokens. The article concludes by proposing a falsifiable governance framework based on fair-syntax enforcement (the principled correction of misleading grammatical patterns), including a corrective rewrite engine and syntactic risk disclosures embedded in compiled registration rules (''reglas compiladas'').

  Abstract
  This article investigates how persuasive syntactic structures embedded in AI-generated crypto whitepapers function as a vehicle of financial authority. Drawing from a curated [...]

  • 2
  •  read