AI Agents in the Trough: The Reality Check on Agentic AI

Posted on by
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18865601  33stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,174✓Minimum 2,000 words for a full research article. Current: 2,174
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18865601
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (20 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The enterprise AI landscape in early 2026 is undergoing a critical inflection point. After two years of proclamations about the "Year of the Agent," empirical evidence now paints a sobering picture: only 5 percent of enterprise-grade generative AI systems reach production, agentic AI pilots exhibit failure rates approaching 70 percent on complex multi-step tasks, and Goldman Sachs finds "no mea...

Show moreHide
Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18865601 33stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,174✓Minimum 2,000 words for a full research article. Current: 2,174
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18865601
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (20 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Future of AIRead More
Read more

Observability for AI Systems: Why OpenTelemetry Is Not Enough and What the Community Needs

Posted on by
Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18864333  32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted20%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,815✓Minimum 2,000 words for a full research article. Current: 2,815
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18864333
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (19 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Modern AI systems deployed in production remain fundamentally opaque to the engineers who operate them. While OpenTelemetry has emerged as the de facto standard for distributed systems observability, its extension to AI and large language model (LLM) workloads e[REDACTED]ses critical gaps: latency traces do not capture hallucination rates, infrastructure metrics do not surface semantic drift, a...

Show moreHide
Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18864333 32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted20%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,815✓Minimum 2,000 words for a full research article. Current: 2,815
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18864333
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (19 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI ObservabilityRead More
Read more

Apple Siri Reimagined: Economics of On-Device AI at Scale

Posted on by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18862953  29stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted12%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,148✓Minimum 2,000 words for a full research article. Current: 2,148
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18862953
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The 2026 reimagining of Apple's Siri represents one of the most economically significant deployments of artificial intelligence in history — not because of its technical novelty alone, but because of the unprecedented scale at which on-device inference economics operate. With over 2.5 billion active Apple devices and 1.5 billion iPhones serving as a distributed inference platform, Apple's archi...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18862953 29stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted12%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,148✓Minimum 2,000 words for a full research article. Current: 2,148
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18862953
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI EconomicsRead More
Read more

Tech Cold War 2026 — Microsoft, AWS, and the Geopolitics of AI Infrastructure

Posted on by
Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18860354  31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic4%○≥80% from journals/conferences/preprints
[f]Free Access12%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]2,572✓Minimum 2,000 words for a full research article. Current: 2,572
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18860354
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (17 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The year 2026 marks a decisive inflection point in the global contest over artificial intelligence infrastructure. With the "Big Five" hyperscalers — Amazon, Microsoft, Google, Meta, and Oracle — collectively forecast to exceed $600 billion in capital expenditure, representing a 36% increase over 2025, the construction of data centers, GPU clusters, and regional cloud regions has become a prima...

Show moreHide
Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18860354 31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic4%○≥80% from journals/conferences/preprints
[f]Free Access12%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]2,572✓Minimum 2,000 words for a full research article. Current: 2,572
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18860354
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (17 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Geopolitical RiskRead More
Read more

HPF Experimental Validation: Multi-Strategy Portfolio Optimization for Ukrainian Pharmaceutical Markets

Posted on by
DOI: 10.5281/zenodo.18855073  55stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef73%○≥80% indexed in CrossRef
[i]Indexed9%○≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access64%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,448✗Minimum 2,000 words for a full research article. Current: 1,448
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855073
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This chapter presents the full experimental validation of the Holistic Portfolio Framework (HPF-P) on a synthetic but econometrically realistic pharmaceutical portfolio dataset representing the Ukrainian market. The experimental design employs five distinct company scenarios spanning the breadth of market conditions encountered by domestic manufacturers — from the stable generics environment of...

Show moreHide
DOI: 10.5281/zenodo.18855073 55stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef73%○≥80% indexed in CrossRef
[i]Indexed9%○≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access64%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,448✗Minimum 2,000 words for a full research article. Current: 1,448
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855073
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Read More
Read more

HPF-P Platform Architecture: From Theoretical Framework to Production System

Posted on by
Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18855053  40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,839✓Minimum 2,000 words for a full research article. Current: 3,839
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855053
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

HPF-P transforms the abstract DRI/DRL framework into a concrete computational system that ingests real-world pharmaceutical data, computes decision readiness diagnostics, applies conditionally permitted optimisation strategies, and produces auditable portfolio recommendations. The platform targets commercial pharmaceutical portfolios — the inventory allocation and revenue optimisation decisions...

Show moreHide
Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18855053 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,839✓Minimum 2,000 words for a full research article. Current: 3,839
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855053
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
HPF-P FrameworkRead More
Read more

HPF-P Platform Technical Overview: From Specification to Deployment

Posted on by
Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845469  25stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,587✗Minimum 2,000 words for a full research article. Current: 1,587
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845469
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

HPF-P is the reference implementation of the Holistic Portfolio Framework (HPF), providing a web-based platform for pharmaceutical portfolio decision support through DRI computation, DRL assignment, and strategy-appropriate optimization. This paper provides a technical overview of HPF-P: its architecture, API design, core algorithms, and deployment configuration. We describe the spec-driven dev...

Show moreHide
Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845469 25stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,587✗Minimum 2,000 words for a full research article. Current: 1,587
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845469
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
HPF-P FrameworkRead More
Read more

Environmental Entropy and Pharma Portfolio Stability: Ukraine Market Analysis

Posted on by
Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845461  31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,599✗Minimum 2,000 words for a full research article. Current: 1,599
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845461
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Portfolio decision quality degrades when environmental entropy — the degree of unpredictability in the market system — exceeds the capacity of available information to characterize it. This paper formalizes the concept of environmental entropy in the context of pharmaceutical portfolio management and demonstrates its impact on Decision Readiness Index (DRI) dimension R5 (temporal stability). We...

Show moreHide
Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845461 31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,599✗Minimum 2,000 words for a full research article. Current: 1,599
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845461
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
HPF-P FrameworkRead More
Read more

Five-Level Portfolio Optimization: From Abstention to Multi-Objective AI

Posted on by
Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845442  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,756✗Minimum 2,000 words for a full research article. Current: 1,756
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845442
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The Decision Readiness Levels (DRL) framework prescribes one of five optimization strategies for each pharmaceutical portfolio segment, conditioned on that segment's Decision Readiness Index (DRI) score. This paper provides a complete specification of DRL-1 through DRL-5: the conditions under which each level is appropriate, the optimization methods employed at each level, the mathematical form...

Show moreHide
Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845442 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,756✗Minimum 2,000 words for a full research article. Current: 1,756
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845442
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
HPF-P FrameworkRead More
Read more

Decision Readiness Index (DRI): Measuring Information Sufficiency for Portfolio Decisions

Posted on by
Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845429  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,752✗Minimum 2,000 words for a full research article. Current: 1,752
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845429
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Effective pharmaceutical portfolio optimization requires not only capable algorithms but also information of sufficient quality to support those algorithms. This paper provides a formal specification of the Decision Readiness Index (DRI), the core diagnostic component of the Holistic Portfolio Framework (HPF). DRI quantifies information sufficiency across five dimensions — data completeness (R1...

Show moreHide
Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845429 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,752✗Minimum 2,000 words for a full research article. Current: 1,752
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18845429
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
HPF-P FrameworkRead More
Read more