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In Stephen Hawking’s terms, the 21st century will be regarded as “the century of complexity”, implying the ability to unify accepted definitions of complexity from mixed fields, under the auspices of an exhaustive theory which is built under the fundamental laws of operation principles, in common conditions, of matter.

Building a unified theory for the complex systems and how these laws can be affected under extreme situations remains an open research topic, in the context of shifting paradigms, that have already determined “the end of certainty”. 

The concept of complexity 

From an epistemological point of view, the concept of complexity derives from complexus, with the meaning of “holding together to form a single composition”. Under this aspect, complexity is not a new concept, what is new is the perception that is attached to the complexity, focusing on identifying problems rather than solutions, on the multiple possibilities of knowledge, with new rules accommodated to concrete conditions.

Research from different fields attempts to identify the causes and sources of complexity, to understand the issues of complexity and its associated syntagma – complex systems, theory of complexity, science of complexity, theory of adaptive complex systems etc. and to build, through a rather holistic approach, a unanimously accepted definition of complexity, so that the negative effects generated by the growth of the complexity may be limited/mitigated (if not eliminated).           

In this discussion we did not propose a hierarchy of the definitions of complexity, each of these capturing different facets of the concept, nor commented on the multiple criteria for classifying complexity. Following the relevance of the aspects for our discussion, we consider that the conceptual essence of complexity is synthesized, in original manners, in the works of Niklas Luhmann and Emil Dinga.

Niklas Luhmann analyzes complexity from the worldwide perspective, which becomes, in his view, the biggest reference unit – the last frontier, final limit – summing up all possible events and circumstances, everything that happens, because everything happens in the world, thus becoming a paradoxical concept, composed of unity – of the past and of the future – and difference, determination and non-determination, observer and observed, ego and alter ego.

By accepting the idea of the inability of human consciousness to understand the complexity of the world, Luhmann charges the social systems – which he defines by their difference in relation with the environment – with the mission of acting towards the reduction of complexity.

By acting in the direction of reducing the complexity of the environment, the systems – operationally closed, autopoietic and self-referential – build their own complexity, with the specification that the environment is always more complex than the system, because it incorporates all the possible connections, events and processes. The degree of complexity outlines the relationship between the system and its environment (ambient).

The most important characteristic of the economic field, the complexity, is analyzed in Emil Dinga’s works from the perspective of its distinctive features, demonstrating, including by introducing the sufficient predicates:

- the manifestation of the complexity exclusively within the networks, respectively of the systems with contingent synergy (which produce novelty);

- the contingent synergy brings unpredictability in the spectrum of complexity; a system is complex where the synergistic effect is not entirely predictable, so it is unpredictable – in whole or in part – within a causal model; unpredictability appears only where novelty appears, of a contingent nature;

- the complexity is given by the presence of the subject, as the sole depository of the novelty, i.e., of the norm, as the result of the free will, which is of the novelty nature, i.e., of unpredictability;

- complexity cannot be conditioned by the non-linearity, distinguishing itself from most of the researchers’ opinions on complexity. 

Complexity economics – a new paradigm 

Complexity economics was conceived in the beginning of the 1980s, as part of an interdisciplinary approach to economics problems, as a response of a research group in economics, physics and computer science, members of the Santa Fe Institute (USA), in the face of the major imbalances humanity struggles with, in a context in which classical economic science, fixated on the principles of equilibrium, certainty and predictability, has reached its limits.

A theoretical approach focused on the analysis of the complexity of the economic system, complexity economics, changes the paradigm in economics and in the sciences of complexity and approaches the economy as a complex system, in which various economic entities, which are not organized on equilibrium principles, react to the aggregate result, trying to solve the challenges of uncertainty by exploring and identifying new solutions, instruments and methods, as well by nonlinear approaches to conceptualizing of the economy.

The complexity economics approach differs from those of neoclassical theory, in the aspects as:

- the dynamic of the economics – static, thermodynamically closed and linear economics is replaced by complexity economics, characterized by thermodynamically openness, far from equilibrium, dynamic and nonlinear;

- the cost of information of agents – while in classical economics the cost of information is low and the agents benefit from perfect information, in complexity economics, the costs of information processing are high and the agents can benefit from heuristic methods of operation, depending on the environmental complexity in which they operate;

- the organization principles - complexity economics works on non-equilibrium principles, while classic economics agents work on equilibrium principle;

- the agents behavior – it is consistent with the aggregate results in classical economics, under optimization conditions, while functioning with uncertainty and reacting to the aggregate results in the complexity economy conditions;

- the networks to which they belong – sophisticated and overlapping, as a compensation of limited information in complexity economics and the lack of interactions between agents in the neoclassical economics;

- the evolution – in the neoclassical; economy there are no mechanisms for creating novelty and for increasing complexity; in complexity economics, the mechanism for creating novelty facilitates the increase of complexity;

- the economies as a system – closed to new behaviors in neoclassical economies and open in complexity economics, which allows the possibility for systems to be exploited;

- the evolution of economics – self-creative and perpetually new in complexity economics, while neoclassical economies do not evolve. 

Today’s complexity economics challenges 

Complexity economics, with its continuously reinventing potential, the organic change of its complex dynamic focused on imperfectly informed agents and self-organization, is evolving rapidly especially in research components designed to develop actions, strategies, beliefs and to offer explanations, quantification and modeling of economic phenomena and processes.

The challenges of complexity economics lie both in the impact of assessing the role of system structures and macroeconomic dynamics on systemic adjustments and long-term policy formulation, and in emphasizing the interpretive dimension, on issues of intersubjectivity and modeling complex phenomena in the real economy.

In this context, the fundamental uncertainty, the incomplete and high-cost information, require a hybrid approach to complexity-system thinking, allowing to formulate flexible, adaptable and operationally reviewable societal policies and actions in line with understanding the complexity of the economy, especially today, when extreme events, such as societal disasters, financial market turmoil or technical constraints, become increasingly the norm in complex economies. Identifying the causal explanatory sources of these undesirable events remains a challenge for complexity economics.