By FERNANDO NOGUEIRA DA COSTA*
The transdisciplinary method is best suited to integrate different approaches and foster systemic thinking
I graduated from FACE-UFMG at the end of 1974. I was in the second class of the Master's in Economics at Unicamp. I went on strike so that I could only take courses with Marxist content…
Today, I find the transdisciplinary method more suitable for integrating different approaches and fostering systemic thinking, both top-down [from top to bottom] how much bottom-up [bottom-up], in a holistic approach. I adopt Complex Systems Theory (CST) because this approach offers a conceptual framework capable of enabling the integrated analysis of economic and social phenomena as interconnected and dynamic systems, where multiple disciplines interact to explain emergent behaviors and evolutionary patterns.
CST has its roots in several areas, including Cybernetics, Information Theory, Biology and Physics, and can be applied in economic and social studies. Through this method, the economy is not understood only by the analysis of isolated parts, but rather understood as a system where agents interact in a non-linear way, creating emergent patterns and collective dynamics.
The perspective top-down allows us to analyze how macroeconomic structures and institutions shape the behavior of individual agents and the rules of the economic game. bottom-up observes how local interactions between heterogeneous agents lead to the formation of global patterns and the evolution of complex economic systems.
Transdisciplinarity aims to transcend disciplinary boundaries by combining methods and concepts from different areas to create a deeper and more comprehensive understanding. In this way, Behavioral Economics or Economic Psychology integrates insights from Psychology to understand economic decisions, highlighting how cognitive biases and heuristics affect agents' behaviors.
Institutional economics or economic sociology explores how social institutions and norms shape economic systems, considering the historical and cultural factors that influence economic development. Evolutionary economics or evolutionary biology uses concepts from Darwinian biology, such as natural selection and adaptation, to analyze how economic systems evolve and how innovations emerge and spread.
Finally, the Economy of Complexity or Econophysics uses methods from Statistical Physics and Applied Mathematics to model complex economic interactions. Examines the dynamics of economic networks and emergent behaviors.
To operationalize this transdisciplinary method, multi-scale modeling and agent-based simulations (ABM – Agent-Based Modeling) are useful tools. These models allow us to observe how individual interactions generate emergent patterns at the macro level, and how changes in institutional structures and public policies feed back into the behavior of agents. In systems, there are properties such as nonlinearity, adaptability, emergence, self-organization, reproduction.
ABM and Network Modeling are used to simulate nonlinear interactions between agents and observe how these interactions produce collective behaviors. These computational simulations allow testing scenarios and predicting policy impacts, considering the adaptability and uncertainty inherent in economic systems.
This methodology adopts a holistic view, considering the behavioral, institutional, biological and physical aspects of the economy as part of an integrated system. It avoids simplistic reductionisms and promotes an understanding that takes into account the interdependence between the components of the system, allowing both the analysis of macroeconomic structures (top-down) as well as the dynamics generated by the actions of individual agents (bottom-up).
The success of this method depends on interdisciplinary collaboration, combining expertise from economists, physicists, psychologists, sociologists, biologists, and complexity scientists to develop theories and models that capture the multifaceted essence of economic systems. Cooperation between disciplines facilitates the exchange of concepts and methodologies, enriching the analysis and enabling insights deeper.
In short, TSC and the resulting systemic thinking, supported by modeling and simulation tools, form a transdisciplinary method for integrating areas such as Behavioral, Institutional, Evolutionary and Complexity Economics. This approach provides a basis for understanding the economy, that is, economic and financial activity, as an adaptive and dynamic system, capable of responding to analyses on both a micro and macro scale, providing a truly holistic view.
In this way, TSC has a broader scope compared to the classical Marxist theory of the capitalist system, as it is able to integrate insights of several contemporary theories, including post-Keynesian macroeconomics, behavioral finance, Schumpeterian entrepreneurship and disruptive innovation, and the microeconomic theory of organizations and institutions. This integration allows for a more complete and multifaceted analysis of economic systems, with a focus on nonlinear dynamics, interdependencies, and emergent behaviors.
TSC considers the economy as a complex adaptive system, where multiple agents interact and generate emergent behaviors that cannot be predicted by analyzing individual parts alone. Unlike the Marxist approach, which focuses on a General Law of Capitalist Accumulation and class struggle with a dialectical and historically materialist view, TSC encompasses several modern theories to describe economic phenomena.
In the case of Post-Keynesian Macroeconomics, it emphasizes uncertainty, the role of investment and aggregate demand, and financial instability with the oscillation between market values and intrinsic (founded) values, enabling long-term analyses of economic and financial cycles and crises. Behavioral Finance provides a deeper understanding of how individual behaviors and irrational decisions influence markets, something not addressed in detail by traditional Marxist theory.
Schumpeterian theory focuses on the role of innovation and entrepreneurship in economic transformation, describing how disruptive innovations and cycles of creative destruction shape the dynamics of capitalism. Organization and Institution Theory explores how organizational structures, social norms, and institutions shape economic interactions and the incentives of agents, providing a more instructive microeconomic understanding.
While Marxist theory focuses on the internal contradictions of capitalism, such as the exploitation of labor and the concentration of capital, CST allows for the analysis of interactions in a more dynamic and adaptive way. It recognizes non-linear interactions when small changes in one part of the system have disproportionate effects on other parts (“butterfly effect”).
Economic systems are not static. They adapt and evolve with different emergencies over time. They require the inclusion of new theories and insights to explain how and why economic changes occur.
TSC recognizes as feedback loops (positive and negative) influence economic stability or instability. This self-organization is absent in economic analyses based on Marxist thought.
Marxist theory aims only to explain the mechanisms of exploitation, the class struggle and the dynamics of capital accumulation, but it has limitations when it comes to capturing contemporary phenomena such as the complexity of financial markets. It does not address in detail speculative financial behavior and the market mechanisms that cause bubbles and crises, except for the persistent denunciation of “financialization” without knowing the cause.
Although Marx recognizes the importance of developing the productive forces, his analysis of innovation does not capture the modern notion of disruptive technological innovations. It clearly gives no importance to entrepreneurship as a driver of change – and it does not address the popular desire for social mobility.
Marxist analysis tends to treat social classes homogeneously with a binary and exclusionary reductionism (workers versus capitalists or “us” versus “them”), while TSC and behavioral theories emphasize the heterogeneity and diversity of behavior among economic agents.
TSC not only incorporates but also expands the understanding of economic systems, enabling multi-scale analysis. It integrates both microeconomic and macroeconomic analysis, capturing how individual actions generate collective consequences and how macro phenomena contextualize and affect micro decisions.
Tools such as computer simulations and agent-based modeling help test complex scenarios and understand the dynamic emergence of economic behaviors and patterns. Their approach allows incorporating insights of new theories, keeping up to date with social, technological and economic changes.
TSC surpasses the Marxist theory of the capitalist system in terms of analytical capacity and comprehensiveness, as it manages to integrate a range of theories and disciplines that emerged after the 19th century. It provides a more dynamic and adaptive understanding of economic phenomena, taking into account both behavioral and institutional aspects as well as technological innovations and complex interactions between agents. This approach is better suited to capturing the complexity and volatility of modern economies, allowing for a more holistic and realistic analysis.
*Fernando Nogueira da Costa He is a full professor at the Institute of Economics at Unicamp. Author, among other books, of Brazil of banks (EDUSP). [https://amzn.to/3r9xVNh]
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