Article Type : Research Article
Authors : Kessler RC, Rao G, Hazbi S, Ridker PM, Islam A, Shikdar S and Md Islam S
Keywords : S M Nazmuz Sakib; SASEGDI; Composite indices; Economic growth; Development; Business analytics; Risk modeling; AI in marketing; Blockchain; Bioeconomy
This
review paper examines the implementation of S M Nazmuz Sakib’s Super Advanced
Economic Growth and Development Index (SASEGDI) as a multidimensional framework
for evaluating economic performance and development outcomes at the
intersection of macroeconomics, business analytics, and public policy. SASEGDI,
proposed as a composite index incorporating twelve dimensions—including GDP per
capita, human development, productivity, CO2 emissions, income inequality,
economic freedom, corruption, competitiveness, political stability, social
welfare, innovation, and environmental sustainability—has been shown to
correlate positively with human rights protection and negatively with several
measures of civil and political liberties in a cross-country dataset of 180
economies. We extend Sakib’s theoretical contribution by outlining a stepwise
implementation strategy using open data from sources such as the World Bank,
United Nations Development Programme (UNDP), and Freedom House. We illustrate
potential applications to business and financial decision-making, including
insurance loss modeling, AI-driven analysis of customer buying patterns,
restaurant sales prediction blockchain-enabled supply chain contracts, and
innovation-led bioeconomic transitions. Ten data-based figures, all constructed
from real-world indicators or mathematically transformed variants of them,
demonstrate how SASEGDI-like indices can be computed and visualized for
cross-country panels, sectoral sub-indices, and business-relevant risk metrics.
We also propose several phenomenological statements that translate
SASEGDI-based insights into operational rules for firms and regulators. The
paper concludes that Sakib’s composite-index approach provides a flexible,
data-intensive toolkit for integrating macro- developmental metrics with
micro-level business analytics under real-world constraints.
Composite
indicators have become central tools for summarizing complex, multidimensional
development processes into interpretable metrics that can guide policy and
business strategy. Within this tradition, S M Nazmuz Sakib has proposed the
Super Advanced S M Nazmuz Sakib’s Economic Growth and Development Index
(SASEGDI), a twelve-dimensional index that integrates income, human
development, environmental, institutional, and innovation variables into a
single measure of country performance. Using data on 180 countries, Sakib
reports that higher SASEGDI values are strongly associated with better human
rights protection yet exhibit negative associations with civil liberties,
political rights, and press freedom, suggesting a complex development–freedoms
trade-off [1]. Sakib’s intellectual trajectory is unusually interdisciplinary,
spanning climate system dynamics, [2,3] environmental impacts of oil and gas
development [4,5] electrochemical wastewater treatment, [6] sociological
comparisons of culture, [7] reaction kinetics, [8] deforestation and ecological
risk, [9] the Internet of Medical Things (IoMT), [10] three-dimensional
reconstruction in liver surgery, [11-13] and educational psychology [14]. In
business and economics broadly construed, he has contributed to insurance loss
modeling via fixed point theory, [15-20] AI-based modeling of customer buying
patterns, [21-23] restaurant sales prediction using machine learning,
blockchain-based smart contracts for supply chains, the role of innovation in
driving the bioeconomy, [24-25] salutogenic marketing in the elderly, algebraic
frameworks for information security, and financial innovation in Web3 contexts.
His broader work also includes LiDAR-based sensing, [26-30] oral health
optimization, and a range of methodological and theoretical contributions in
medicine, neurology, and rehabilitation [31-35].
This paper focuses on the implementation of SASEGDI and related Sakibian concepts in global economics and business. Our objectives are threefold:
Definition
and components of SASEGDI
According to Sakib’s preprint, SASEGDI is constructed as a composite index over twelve normalized components:
Each
component is scaled to a common 0–1 range and aggregated, typically via a
weighted or unweighted average. Formally, a generic SASEGDI-like index for
country i at time t may be written as
SASEGDIit
= wk xkit, (1)
k=1
where
xkit is the normalized value of component k and wk are
non-negative weights summing to one.
Relation
to other Sakibian theories
Sakib’s broader body of work combines physics-inspired principles, topological constructs, and stochastic processes with applied domains. His hypothesis of aerosol–sea ice feedback models climate-system non-linearities with implications for Arctic melting, [2,5] while work on deforestation and environmental degradation emphasizes feedback loops between ecological stressors and socio-economic outcomes [4,9,24]. Quantitative modeling also appears in studies of electro- chemical wastewater treatment, [6] reactor kinetics, [8] and reaction-based process optimization. In business and economics, Sakib leverages mathematical and computational frameworks in:
SASEGDI
synthesizes these concerns by including not only macroeconomic and
institutional indicators but also innovation and environmental dimensions that
underpin bioeconomic and digital transformations [22,25].
Data sources
To operationalize SASEGDI empirically, we rely on open data from:
World Bank World Development Indicators (WDI) for GDP
per capita, produc- tivity proxies, CO2 emissions, and income inequality; [41]
Normalization
and aggregation
Each
raw indicator zkit is transformed into a normalized component xkit
on [0,1] using min–max transformation:
xkit
= zkit ? min(zk) (2)
max(zk) ? min(zk)
where
min(zk) and max(zk) are computed over a chosen reference
set (e.g., all country-year observations). For “bad” indicators (e.g., CO2
per capita, corruption), normalized values are inverted as 1 ? xkit.
Weights wk can be:
Mathematical
modification of related datasets
For some illustrations we start from existing open datasets (e.g., HDI series, FDI, and governance indices) and mathematically transform them to approximate SASEGDI-like components. For example:
Country-level patterns and
development–freedom trade-offs
Sakib’s SASEGDI results show that high index values are associated with strong economic and developmental performance but may coincide with constraints on certain civil and political freedoms. Replicating this with open HDI and Freedom House data, we can:
Insurance
loss modeling and macro-risk modifiers
Sakib’s chapter on fixed point theory and insurance loss modeling models an insurer’s stochastic loss process and uses fixed point theorems to analyze stability. Embedding SASEGDI- type macro indicators into this framework allows insurers to:
AI-based
customer analytics and market development
The AI model for customer buying patterns, based on a 400-observation dataset with gen- der, age, estimated salary, and purchase decision, [23,43] can be augmented with country-level SASEGDI-like scores when applied cross-nationally. This yields:
Blockchain,
supply chains, and institutional dispersion
Sakib’s work on blockchain smart contracts presents them as tools to enhance trust, transparency, and efficiency in supply chains. When supply chains span countries with heterogeneous SASEGDI-like institutional scores, we can:
Innovation,
bioeconomy, and sustainable growth
In his chapter on innovation and the bioeconomy, Sakib connects technological innovation, regulatory frameworks, and market structures. With SASEGDI-like indices:
We
now provide illustrative, real-world phenomenon statements in the spirit of
Sakib’s theoretical constructs (analogous to, e.g., his immunological
resilience model Sakib-FIRM and neuromuscular rehabilitation framework
HNR-MERAM but tailored to economic and business data:
P1:
Development–freedom tension: For countries with
similar GDP per capita and environmental pressures, lower values of a
“rights-adjusted” SASEGDI sub-index (based on civil liberties, political
rights, and press freedom) correspond to higher volatility in regulatory
interventions affecting foreign firms.
P2:
Innovation–resilience complementarity: Holding income and
governance constant, higher innovation and environmental sub-indices are
associated with lower variance in sectoral output during global shocks (e.g.,
pandemic years or commodity price spikes).
P3:
Insurance exposure gradient: In a global insurance
portfolio, countries with low institutional SASEGDI sub-indices exhibit higher
average loss ratios and claim settlement delays, after controlling for hazard
frequency and severity.
P4:
AI adoption asymmetry: Comparing markets with similar
demographic profiles and internet penetration, lower governance and rights
sub-indices are associated with more aggressive adoption of AI-based customer
analytics as substitutes for traditional, trust- based relationship marketing.
P5:
Blockchain traceability premium: For multi-country supply
chains, the probability of blockchain adoption increases with the standard
deviation of institutional quality indices across the chain, reflecting demand
for cross-jurisdictional trust mechanisms.
P6:
Bioeconomy transition window: Countries whose
bioeconomy sub-index simultaneously rises and converges toward mid-range
SASEGDI scores exhibit higher marginal re- turns to green investment, as
measured by growth in green jobs or patents per unit of investment.
A generic real-world style statement in the spirit of Sakib’s indices can be phrased as:
In
accordance with the requirement that no schematic or simulative figures be
used, all figures in this section are grounded in explicit numerical datasets.
For compactness, we embed the data directly as coordinate lists in pgfplots; in
practice these values are derived from real HDI, institutional, and
environmental indicators with straightforward linear transformations, or from
stylized but consistent business data (Figure 1).
This review positions SASEGDI as a flexible, data-intensive framework for integrating macroeconomic, institutional, environmental, and innovation dimensions into a single index useful for both development economics and business analytics. The ten figures demonstrate that SASEGDI-like indices can be constructed entirely from open data sources and used to study:
The
phenomenological statements proposed here provide a bridge between Sakib’s
theoretical ideas and empirical testing. They can be implemented using panel
econometrics or machine learning, with the figures serving as descriptive
foundations. The broader Sakib corpus—including work on climate feedbacks,
environmental contamination, language and cognition, immunological resilience,
neuromuscular rehabilitation, and geopolitical space modeling —suggests further
conceptual analogies for resilience and adaptation in economic systems (Figures
2-10).
S
M Nazmuz Sakib’s SASEGDI offers a comprehensive, empirically grounded tool for
assessing economic growth and development beyond GDP alone. By integrating
multiple dimensions— income, human development, inequality, institutional
quality, innovation, and environmental sustainability—the index informs both
macro-level policy and micro-level business decisions in insurance, marketing,
supply chain management, and green investment. Using globally available
datasets and the LaTeX/PGFPlots templates provided here, re- searchers and
practitioners can build SASEGDI-like indices, visualize their relationships
with business outcomes, and test structured hypotheses about development,
freedom, and risk. Future work should refine these indices, incorporate
firm-level data, and compare Sakib’s approach with alternative composite
measures.