 |
| Photo by Tima Miroshnichenko: https://www.pexels.com/photo/a-woman-in-plaid-blazer-using-her-laptop-6693661/ |
When I researched value models for my own work, I came across Anatoly V. Kondratenko’s Physical Economics: Stationary Quantum Economies in the Price-Quantity Space. As a person highly interested in physics and mathematics, I immediately liked the idea of quantum calculations and dug deep into it. But as I delved further, I realised that while the mathematics was fascinating, it would never really improve anything meaningful in economic forecasting. Today, I am surprised to see that this fallacy is not only widespread but also a trend that continues to grow, as evidenced by Ivanov and Petrova (2024), which itself falls into the same trap, further demonstrating its prevalence in financial thinking.
While the mathematical underpinnings of these models may be impeccable, their application to dynamic, reflexive systems such as financial markets often leads to unintended consequences and misplaced expectations. This article unpacks the fallacy of precision in social science forecasting, examines why adoption drives success rather than accuracy, and proposes better ways to understand and screen factors in such complex systems.
The Illusion of Precision
In Physical Economics, Kondratenko applied principles from quantum mechanics to economic modelling, seeking to create a framework where economic behaviours could be understood as quantum phenomena. His work is a striking example of the ongoing trend to impose the deterministic rigor of physical sciences onto social systems. Similarly, the 2024 Systematic Literature Review on Quantum Economics summarises the evolution of this field from 1978 to today, highlighting numerous attempts to leverage quantum concepts like superposition and uncertainty to model financial behaviours.
However, the fundamental challenge lies in the inherent difference between physical and social systems. While precision enhances predictive accuracy in deterministic systems like weather patterns, financial markets and economies are reflexive and driven by interest-oriented actors motivated by existential considerations. It is the interplay of personhood and agency that fundamentally alters their dynamics, making them distinct from deterministic systems. This creates a dynamic where precision does not necessarily equate to better forecasting outcomes. Even when the results appear improved, it is not the precision of the method itself that directly causes better forecasts, but rather other factors such as broader adoption or systemic alignment.
The Real Driver: Adoption, Not Accuracy
Financial forecasts succeed not because of their precision, but because of their widespread adoption. This phenomenon can be explained by:
Self-Fulfilling Prophecies: Forecasts shape expectations and behaviours. For instance, a prediction of rising interest rates can prompt businesses and investors to act in ways that cause the rates to rise.
Coordination Mechanisms: Forecasts serve as tools to align the actions of disparate market participants, creating a shared narrative that influences outcomes.
Systemic Influence: The broader the adoption of a forecast, the more it shapes collective decision-making, reinforcing the predicted trends.
As the literature review suggests, the mathematical elegance of quantum economic models often obscures their inability to capture this reflexivity. Their success, paradoxically, lies in how well they influence behaviour, not in how accurately they predict outcomes.
Toward Better Forecasting: Understanding and Screening Factors
To move beyond the illusion of precision, financial and economic forecasting must embrace methodologies that prioritise adaptability, systemic understanding, and behavioural insight. Key approaches include:
Systems Thinking: Analysing how factors interact within broader systems, identifying feedback loops and cascading effects.
Scenario Planning: Exploring multiple potential futures to understand how key drivers might influence different outcomes.
Agent-Based Modelling (ABM): Simulating the behaviours and interactions of individual agents to observe emergent phenomena.
Behavioural Analysis: Focusing on the psychological and social factors that drive economic decision-making.
From a philosophical perspective, incorporating the foundational ideas of Carl Menger and Manfred Max-Neef offers a complementary lens:
Carl Menger’s Principles of Economics: Menger’s focus on individual needs and subjective value highlights the importance of understanding how individual preferences and marginal utility drive economic behaviours. This approach emphasises the dynamic and contextual nature of value creation, aligning well with the reflexivity of financial systems (Menger, 1871/1981).
Manfred Max-Neef’s Fundamental Human Needs: Max-Neef’s framework of fundamental human needs (e.g., subsistence, protection, identity, participation) shifts the focus from mere economic efficiency to human well-being. Screening factors based on their impact on these needs could provide a more holistic understanding of economic systems and their drivers (Max-Neef, as cited in Dag Hammarskjöld Foundation, 1989).
These philosophies encourage forecasters to examine the underlying human behaviours, values, and needs that shape economic activity, rather than relying solely on abstract mathematical models.
Wrong Incentives and Market Instability
The wrong application of forecasting methods often perpetuates practices that reaffirm inefficient market behaviour. Over time, this can lead to systemic crises, instability, and eventual displacement by more efficient systems. This dynamic is particularly relevant in the context of ongoing financial competition, such as the current dollar-based monetary system versus the multi-currency framework proposed by BRICS. In such scenarios, the failure to address inefficiencies within the dominant system may accelerate its decline, making it crucial to reconsider the incentives and mechanisms that shape forecasting practices.
Conclusion
The intersection of quantum mechanics and economics presents an intriguing frontier, exemplified by the works of Kondratenko and the 2024 systematic review. However, the relentless pursuit of precision in financial forecasting often misses a crucial truth: success depends more on influence, adoption, and adaptability than on mathematical exactitude.
Building on my earlier work (Hirzel, 2017a, 2017b), which critiqued the structural disconnection between obligations and rights in financial systems, I now shift focus to the concept of value within the same structural framework. The instability stems not only from disconnection but also from the fundamental challenge of defining and calculating value. Together, these perspectives provide a more nuanced understanding of structural imbalances and suggest pathways to address them effectively.
This critique aligns with the libertarian tradition of questioning centralised interventions in financial systems, particularly through the subjective theory of value developed by the School of Salamanca (see Francisco de Vitoria, Luis de Molina, and Domingo de Soto, as discussed in Álvarez, 2020). Their emphasis on the decentralised nature of value determination underscores the importance of individual preferences and localised knowledge in shaping market dynamics.
By integrating insights from systems thinking, behavioural analysis, and philosophical approaches—such as those of Menger and Max-Neef—we can design tools that better prioritise adaptability and alignment with the complexities of economic systems. This shift not only fosters innovation but also embraces the inherent unpredictability of human behaviour, offering a framework for navigating uncertainty with clarity and purpose.
References
Álvarez, F. (2020). La Escuela de Salamanca y la teoría subjetiva del valor. Retrieved from https://www.academia.edu/42932625/La_Escuela_de_Salamanca_y_la_teor%C3%ADa_subjetiva_del_valor
Dag Hammarskjöld Foundation. (1989). Development dialogue: A journal of international development cooperation. Retrieved from https://www.daghammarskjold.se/wp-content/uploads/1989/05/89_1.pdf
Hirzel, T. (2017a). The potential of Islamic banking from the perspective of the Austrian school. [Video]. Instituto Juan de Mariana. Retrieved from https://juandemariana.org/ijm-media/video/panel-de-teoria-economica-y-monetaria-bloque-3/
Hirzel, T. (2017b). The potential of Islamic banking from the perspective of the Austrian school. Academia.edu. Retrieved from https://www.academia.edu/37429170/The_potential_of_Islamic_banking_from_the_perspective_of_the_Austrian_school
Ivanov, V., & Petrova, O. (2024). Quantum economics: A systematic literature review. Retrieved from https://armgpublishing.com/wp-content/uploads/2024/04/SEC_1_2024_5.pdf
Kondratenkov, I. (2021). Economics of the future: Principles and predictions. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2363874
Mises, L. von. (2008). Principles of economics. Retrieved from https://cdn.mises.org/Principles%20of%20Economics_5.pdf