The escalating complexity of the global economy challenges traditional analytical frameworks. Three fundamental shifts define this complexity: the emergence of knowledge-intensive economic activities, the imperative of universal sustainable development, and the proliferation of intangible assets as primary value drivers. These phenomena expose critical limitations in neoclassical economic paradigms that assume rationality, market clearing mechanisms, and easily quantifiable variables. The critical reference point validates the need for a more comprehensive approach to measuring economic performance and social progress (Stiglitz et al., 2009). E.F. Schumacher (1973) also provides a foundational critique of conventional, materialist economics and the need for a human-centered approach.
The neoclassical synthesis, despite its analytical elegance, faces persistent criticism for its restricted explanatory scope in contemporary economic realities. The homo economicus paradigm and singular reliance on aggregate measures like GDP prove inadequate for addressing multidimensional challenges of the 21st century. Traditional marginalist optimization cannot effectively handle the management of global public goods, valuation of intellectual and natural capital, or formulation of sustainable development strategies.
Significant fields and aspects of economic activity remain unmeasured due to methodological limitations. Creative activities, many intellectual service sectors, and knowledge-based processes resist conventional quantification. The value of intangible assets—ideas, inventions, cultural products—cannot be captured by measuring their material manifestations alone. Similarly, socioeconomic effects of services like healthcare and education are often inappropriately equated with their social costs rather than their value creation.
Contemporary economic challenges require analytical frameworks extending beyond efficiency-focused, purely quantitative approaches. The integration of normative and qualitative factors into economic analysis demands methodological innovation, meta-systemic approach (Menger, 1954) that can handle:
Incommensurable values: Social utility, environmental health, and creative potential resist direct quantitative comparison.
Dynamic value hierarchies: Economic preferences change over time and context.
Complex interdependencies: Non-linear relationships between economic variables.
Multi-stakeholder objectives: Conflicting goals requiring structured trade-off analysis.
Economic analysis requires explicit assumptions about the nature of economic reality. Traditional economics suffers from ontological confusion, treating fundamentally different types of entities—material goods, social relationships, and institutional structures—as equivalent analytical objects. This creates systematic distortions in understanding economic phenomena.
Economic phenomena emerge from collective human action but acquire objective properties through institutionalization. Markets, money, and property rights exist as social constructs that become real through collective recognition and enforcement. This dual nature—socially constructed yet objectively constraining—requires analytical frameworks capable of handling both dimensions simultaneously.
Economic entities exist across different time horizons and evolve through path-dependent processes. Capital, knowledge, and institutions cannot be understood as static objects but must be analyzed as dynamic systems with emergent properties. This temporal dimension necessitates analytical approaches that can model evolutionary and adaptive processes (Lynne, 2003, 2024).
Complex economic systems exhibit properties that cannot be reduced to individual components. National competitiveness, systemic risk, and innovation ecosystems represent emergent phenomena requiring holistic rather than reductionist analytical approaches. The ontological recognition of emergence justifies methodological frameworks operating at higher logical orders than conventional economic analysis.
Economic knowledge operates at multiple levels, from empirical observations to theoretical generalizations to methodological principles. A hierarchical epistemological framework recognizes that different analytical levels require different validation criteria and that higher-order principles govern the construction and evaluation of lower-level knowledge claims (Buracas, 1985, 2004).
Economic understanding requires integration of diverse knowledge types: quantitative data, qualitative assessments, expert judgments, and stakeholder preferences. Each represents different epistemological categories with distinct validation requirements. Effective methodology must provide principled frameworks for combining these heterogeneous knowledge forms without inappropriate reduction.
Economic knowledge claims must be evaluated relative to specific analytical purposes and institutional contexts. Unlike natural sciences, economic analysis faces reflexivity challenges where knowledge affects the phenomena studied. Methodological frameworks must account for this reflexive relationship and the context-dependent nature of economic validity.
The epistemological foundation adopts critical realist principles recognizing that economic reality exists independently of our knowledge while acknowledging that our access to this reality is mediated through theoretical frameworks and measurement instruments (Buračas et al., 2025). This position justifies sophisticated methodological approaches that can navigate between naive empiricism and radical relativism.
Having established the ontological and epistemological foundations, we can now articulate the core postulates that structure the metaeconomic framework. These postulates emerge from the philosophical foundations and provide operational principles for analytical practice.
Metaeconomics functions as a system of higher logical order, analogous to metalogic or metamathematics. It specifies regulative principles, criteria, and interconnections for economic research while ensuring epistemological and ontological consistency. This establishes metaeconomics as a methodological framework rather than a competing economic theory.
Economic phenomena represent fundamental expressions of human social relationships that cannot be fully captured by static, material-based categories alone. The failure to reconcile material (object-oriented) and social categories constitutes the core methodological problem in conventional economics (Roy, 1996). This duality necessitates analytical approaches capable of handling both dimensions simultaneously.
Economic effectiveness requires continuous, dynamic synchronization between normative-value subsystems (goals, criteria, social preferences) and realization-applied subsystems (means, policies, technologies). Unforeseen consequences in complex systems arise from failures in this synchronization, demanding predictive and regulative rather than merely descriptive frameworks.
Economic value is not absolute but represents a dynamic, path-dependent phenomenon determined by position within temporal accounting systems and influenced by chronological boundaries of causal relationships. This requires robust methodologies for intertemporal comparison and dynamic preference modeling (Lynne, 2020).
Methodological positions must undergo inversion (re-application across analytical levels) and intraversion (internal transformation) as they encounter new phenomena and data. The transition between research levels requires principled criteria for methodological adaptation and tool selection.
These postulates generate operational principles organized into three thematic clusters that guide analytical practice and provide criteria for methodological choice.
Internal Structuralism: Acknowledging intricate interdependencies within economic systems.
Nonlinearity: Recognizing dynamic, non-proportional systemic interactions.
Emergence and Synergy: Understanding that interactive integrity yields multiplicative rather than additive effects.
Negentropic Orientation: Focusing on systems that evolve toward increasing order, countering entropic decay.
Multiplicity of Values: Explicitly acknowledging diverse non-material values and goals driving economic activity.
Coherence: Demanding consistency across structural levels from normative goals to implementation practices.
Taxonomic Ranking: Establishing clear hierarchies and weights for criteria in multi-objective contexts.
Equifinality: Recognizing that systems can reach specified end-states through different pathways and initial conditions.
Evaluative Congruence: Ensuring consistency between different socioeconomic systems and their analytical assessments.
Ambivalence Management: Accounting for contradictory attitudes and finding robust solutions under uncertainty.
The philosophical foundations and core postulates establish the theoretical justification for Multiple Criteria Decision Analysis (MCDA) as the primary operational toolkit for metaeconomic analysis. MCDA provides the structured, transparent methodology required to operationalize metaeconomic principles in practical decision-making a/o management contexts. The notion of hierarchical epistemology and knowledge pluralism (Kickert & Van Gigch, 1979) directly justifies why MCDA is the appropriate toolkit for contemporary management. MCDA’s ability to integrate quantitative data, qualitative assessments, and expert judgments is not merely a practical feature; it is a principled response to the epistemological challenge of combining diverse forms of knowledge without reducing them to a single metric (Greco et al., 2016). The discussion of MCDA’s ability to manage complexity, and facilitate stakeholder engagement is accurate and well-argued by Belton & Stewart (2002), in Social Multi-Criteria Evaluation (2025).
Unlike traditional cost-benefit analysis that attempts to monetize non-market goods and services (Saarikoski et al., 2016), MCDA handles diverse criteria in their native units. The inclusion of incommensurable values suppose evaluating diverse criteria in their native units, thus providing a methodological solution to a fundamental measurement problem (Greco et al., 2016). This aligns with the metaeconomic principle of multiplicity of values, allowing formal analysis of financial, social, environmental, and technical factors without inappropriate reduction.
MCDA methods formally incorporate both objective measurable data and subjective qualitative judgments. This operational capacity directly implements the epistemological framework’s requirement for integrating heterogeneous knowledge types while maintaining analytical rigor.
The MCDA process forces explicit definition of objectives, criteria, and priorities, making decision rationale transparent and defensible. This implements the metaeconomic emphasis on coherence and evaluative congruence while managing systemic complexity through structured decomposition.
By making trade-offs explicit, MCDA provides common language for diverse stakeholders to debate and build consensus. This addresses the metaeconomic challenge of co-substantiation between normative and applied subsystems through participatory methodology.
Every MCDA problem contains five essential elements that operationalize metaeconomic principles applicable in managerial practice:
Alternatives (A): Finite set of decision options representing different pathways to system goals
Criteria (C): Comprehensive, non-redundant, measurable attributes reflecting multiple values and objectives
Weights (w): Numerical values reflecting relative importance, implementing taxonomic ranking principles
Performance Matrix (X): Systematic evaluation of alternatives against criteria, integrating diverse information types
Aggregation Rules: Mathematical procedures for combining information while respecting ontological distinctions
The MCDA process implements metaeconomic principles through six sequential stages:
Problem Structuring: Collaborative definition of context, goals, alternatives, and criteria with stakeholder engagement
Performance Evaluation: Systematic data gathering implementing knowledge pluralism through quantitative modeling, expert surveys, and qualitative assessments
Preference Elicitation: Determining criteria weights through structured methods ensuring coherence and transparency
Aggregation and Ranking: Applying mathematical methods while respecting ontological constraints and value hierarchies
Sensitivity and Robustness Analysis: Testing result stability across parameter variations, implementing ambivalence management
Decision Communication: Presenting recommendations with full transparency about assumptions and trade-offs
These methods are often used to create composite indices because they provide aggregate performance across all criteria into comprehensive scores, appropriate when complete compensation between criteria is acceptable (Zavadskas & Turskis, 2011):
Simple Additive Weighting (SAW): Direct implementation of weighted aggregation for constructing composite indices like Global/World Competitiveness Index;
Analytic Hierarchy Process (AHP): Hierarchical structuring with pairwise comparisons for complex stakeholder engagement in innovation policy selection (Saaty & Vargas, 2012).
These methods have unique ability to handle incomparability, which is particularly useful for public policy decisions where a trade-off is not always a viable option (Roy, 1996). They build preference relationships without requiring complete trade-offs, suitable for incommensurable values:
ELECTRE: Incorporating veto thresholds for handling societal constraints that cannot be traded off, such as environmental impact assessment;
PROMETHEE: Flexible preference modeling for complex strategy evaluation like sustainable development pathways.
These methods compare alternatives to predefined targets, implementing benchmarking and improvement strategies, identify their role in goal-oriented analysis, which is highly relevant for management and policy appraisal (Triantaphyllou, 2000; Use of Multi-Criteria Decision Analysis, 2024):
TOPSIS: Measuring distance from ideal solutions for competitiveness benchmarking and gap analysis;
Goal Programming: Optimizing achievement of multiple targets simultaneously while managing trade-offs.
The metaeconomic framework demonstrates practical relevance through successful application in prominent international assessment systems. These applications validate the theoretical framework while illustrating its operational effectiveness in addressing complex policy challenges. The principles of Multiplicity of Values and Taxonomic Ranking are direct antecedents to the MCDA process, where criteria and their respective weights are explicitly defined and justified (Saaty & Vargas, 2012; Zeleny, 1982).
The World Economic Forum’s Global Competitiveness Index (now IMD World Competitiveness Ranking) exemplifies metaeconomic methodology by evaluating national competitiveness through dozens of variables grouped into pillars including institutions, infrastructure, ICT adoption, skills, and innovation capability. This approach transcends GDP-focused analysis by providing holistic diagnostic capabilities for productive potential assessment.
The Global Innovation Index (GII, 2024) and Global Talent Competitiveness Index (GTCI, 2025) demonstrate similar methodological sophistication. The GTCI evaluates national capacity to attract, grow, and retain talent through pillars including vocational skills and global knowledge skills. Analysis reveals that social processes and reward levels drive performance, while brain drain creates systematic deviations requiring nuanced multi-criteria interpretation.
These applications demonstrate sophisticated integration of:
Statistical Integration: Official data combined with expert evaluations using weighted co-measurability approaches
Qualitative-Quantitative Synthesis: Diverse factor types aggregated through principled methodological frameworks
Dynamic Benchmarking: Cross-national comparison enabling policy intervention identification
Stakeholder Engagement: International expert panels contributing to criteria definition and weight assignment
The UN Environment Program’s Inclusive Wealth Index represents breakthrough application of metaeconomic principles by reconceptualizing national wealth as the sum of produced capital, human capital, and natural capital. This directly operationalizes the principle of multiplicity of values and demonstrates superior analytical power compared to GDP-focused approaches. Report’s finding that human capital is a larger driver of wealth than produced capital is a striking example of a metaeconomic insight that traditional measures would miss. The discussion of institutional innovators (optimizers, enablers, transformers) is a strong point, as it shows the framework’s versatility in different contexts. The financial stability examples (IMF’s Financial Sector Assessment Program) further demonstrate institutional adoption of multi-criteria frameworks, validating the approach in a highly quantitative and risk-averse domain
Key findings validate metaeconomic insights:
Human capital contributed 55% of inclusive wealth growth across 140 countries.
Produced capital contributed only 32%, natural capital 13%.
Investment in produced capital yielded lowest returns for most countries.
Traditional metrics systematically misclassify educational investment as expenditure rather than asset building.
IWI analysis shifts focus from short-term output flows to long-term asset base management, demonstrating the practical policy relevance of metaeconomic frameworks. This transition exemplifies the framework’s capacity to guide strategic investment decisions and resource allocation in transitional economies seeking competitive advantage through knowledge-based strategies.
Recent applications analyze institutional innovators through three categories: optimizers (improving existing operation efficiency), enablers (developing innovative technologies and infrastructure), and transformers (creating new offerings and markets while eliminating resource dependency). Each category requires different evaluation criteria and performance metrics, demonstrating the framework’s flexibility in handling diverse institutional contexts.
The methodology successfully integrates:
Innovation Quality Assessment: Dependent on intellectual potential, professional competency, creativity, IT infrastructure, and entrepreneurship advantages
Policy Effectiveness Evaluation: Different innovation policies require distinct assessment frameworks reflecting varied objectives and constraints
Cross-National Benchmarking: Comparative analysis enabling identification of best practices and institutional learning opportunities
Multi-criteria frameworks have achieved institutional adoption in financial stability assessment through the IMF’s Financial Sector Assessment Program covering 29 systemically important jurisdictions and the US Financial Stability Oversight Council’s analytical framework monitoring eight vulnerability categories.
These applications demonstrate advanced integration of:
Quantitative Indicators: Statistical measures of financial system performance and risk
Qualitative Assessments: Expert judgment on institutional quality and regulatory effectiveness
Stress Testing: Scenario analysis across multiple risk factors and time horizons
Dynamic Monitoring: Continuous assessment enabling early warning system development
Having demonstrated the theoretical foundations and practical applications of the metaeconomic framework, we now provide balanced critical evaluation of its strengths and limitations while identifying priority areas for future research and development.
The framework provides clear distinction between metaeconomics as methodology versus behavioral economics, resolving long-standing terminological confusion. The meta-systemic positioning offers powerful “grammar” for constructing and evaluating economic theories rather than proposing yet another behavioral model.
The framework directly addresses contemporary policy challenges inadequately handled by conventional models: sustainability assessment, intellectual capital valuation, social equity measurement, and multi-stakeholder decision processes. Applications in prominent international indices demonstrate real-world utility and institutional adoption.
MCDA integration provides structured, transparent toolkit for operationalizing holistic economic analysis. The framework correctly identifies methodology rather than theory as the fundamental challenge in contemporary economics, offering principled solutions rather than abstract critique.
The framework provides formal basis for incorporating intangible assets, social utility, and dynamic value hierarchies while maintaining analytical rigor. This represents significant advancement beyond GDP-centric analysis and homo economicus assumptions.
Despite conceptual sophistication, practical operationalization faces significant challenges:
Weight Elicitation Problems: Analytic Hierarchy Process suffers from inconsistency issues, rank reversal problems, and cognitive burden increasing exponentially with criteria sets
Aggregation Function Sensitivity: Linear models may recommend extreme solutions while product models prove overly conservative
Data Quality Dependencies: MCDA results depend entirely on performance matrix quality, with poor data leading to poor decisions regardless of methodological sophistication
Metaeconomic analysis, like most MCDA, excels at description and evaluation, but it is not a predictive or explanatory model in the traditional econometric sense (Saaty & Vargas, 2012; Triantaphyllou, 2000):
Limited Causal Inference Capability: Framework provides powerful descriptive and evaluative tools but weaker explanatory power for fundamental economic dynamics
Subjectivity in Critical Parameters: Criteria selection and weight assignment introduce normative elements into ostensibly objective analysis
Method Selection Arbitrariness: Different MCDA methods can yield different rankings, with choice often driven by convenience rather than theoretical appropriateness
Resource and Expertise Requirements: Sophisticated applications exceed many organizations’ capabilities.
Political Acceptance Challenges: Technical complexity creates communication difficulties with stakeholders and decision-makers.
Limited Practitioner Availability: Shortage of skilled practitioners constrains adoption and quality control.
Software Limitations: Available tools often drive methodology choice rather than problem requirements determining appropriate methods.
Priority areas for advancing the framework include:
Enhanced Weight Elicitation Methods: Developing data-driven and stakeholder-consensus approaches to reduce arbitrariness in criteria weighting
Robust Aggregation Functions: Creating aggregation methods that handle uncertainty, incomplete information, and conflicting stakeholder preferences.
Dynamic MCDA Models: Extending static analysis to handle temporal evolution and adaptive management.
Integration with Advanced Analytics: Incorporating machine learning, network analysis, and complexity science methods.
The framework requires deeper integration with:
Evolutionary Economics: Synthesizing methodological framework with evolutionary-ontological approaches to measure and track fitness of evolving economic rules.
Institutional Economics: Developing explicit models of how institutional contexts affect criteria definition and weight assignment.
Behavioral Economics: Incorporating psychological insights about decision-making biases and cognitive limitations in multi-criteria contexts.
Promising managerial application domains include:
Artificial Intelligence Economics: Managing complex trade-offs in AI development and deployment.
Decentralized Finance: Evaluating cryptocurrency and blockchain-based financial management systems as a roadmap for future research in the field.
Circular Economy Assessment: Measuring progress toward resource efficiency and waste elimination.
Climate Policy Integration: Managing transitions to sustainable energy systems with multiple competing objectives.
Critical needs include:
Predictive Performance Assessment: Testing whether metaeconomic models provide superior forecasting compared to conventional approaches.
Cross-Cultural Validation: Examining framework applicability across different institutional and cultural contexts.
Longitudinal Impact Studies: Assessing whether MCDA-informed decisions produce better long-term outcomes.
Comparative Methodology Studies: Systematic comparison of different MCDA approaches across various application domains.
Future development should explore how metaeconomic methodology can inform fundamental economic theory construction. Rather than remaining purely methodological, the framework could contribute to:
New Theoretical Insights: Using MCDA to identify previously unrecognized relationships and patterns in complex economic systems.
Theory Testing and Validation: Developing criteria for evaluating competing theoretical explanations using multi-criteria frameworks.
Policy Theory Development: Creating more sophisticated models of how policy interventions affect multiple objectives simultaneously.
The framework shows particular promise for:
Democratic Decision-Making: Enhancing citizen participation in complex policy choices through structured multi-criteria approaches.
International Cooperation: Providing common analytical frameworks for multilateral institutions addressing global challenges.
Corporate Strategy: Integrating stakeholder capitalism approaches with rigorous analytical methods.
Regulatory Design: Developing regulation that efficiently balances multiple social objectives.
This analysis establishes metaeconomics as a systematic methodological framework addressing fundamental limitations in neoclassical economic analysis while providing practical tools for complex decision-making in contemporary policy and management contexts. The framework’s theoretical foundations in critical realism and hierarchical epistemology justify its operational toolkit of MCDA methods.
The metaeconomic framework makes three primary contributions to economic methodology:
Philosophical Foundation: Providing explicit ontological and epistemological grounding for economic analysis that handles complexity, emergence, and value pluralism.
Methodological Innovation: Integrating MCDA methods with economic analysis to create structured approaches for multi-objective optimization and stakeholder engagement.
Empirical Validation: Demonstrating practical relevance through successful applications in global competitiveness assessment, sustainable development evaluation, and innovation policy analysis.
The framework addresses critical challenges in contemporary economic management: measuring intangible assets, integrating sustainability considerations, handling multi-stakeholder objectives, and managing complex adaptive systems. Applications in prominent international indices demonstrate institutional adoption and policy relevance.
While limitations exist in operationalization complexity and theoretical integration, the framework provides a foundation for advancing economic methodology toward greater realism and practical utility. Priority research areas include enhanced weight elicitation methods, dynamic modeling capabilities, and integration with emerging fields like artificial intelligence economics and decentralized finance.
The metaeconomic framework represents a significant advancement in economic methodology, providing both theoretical sophistication and practical utility for addressing the complex, multidimensional challenges of 21st-century economic management. Its continued development promises to enhance both scholarly understanding and policy effectiveness in an increasingly complex global economy.