This Doctoral Programme implements a systematic attempt to elaborate upon the achievements of complexity theory and the history of economic analysis to articulate a comprehensive economics of complexity. In their dissertation work students may implement agent based simulation methodologies that enable to provide basic and simple economic foundations at the same time to analyzing the outcome of the intentional economic action of agents endowed with some levels of creativity, both at the micro and macro level within different levels of organized complexity. Econometric analysis, historical and institutional approaches are also common.Students can specialize in:

  • Complexity and simulation models for economic analysis
  • Cognitive economics
  • Economics of knowledge and innovation
  • History of economics thoughts

The conception of economics prevailing after the Second World War has maintained that economics must be based on deductive models, consistent with standard economic principles and incorporating high doses of mathematics, in order to be econometrically tested and directly applied to reality, and to search for general results able to hold for all times and institutional contexts.

Such mainstream economic theory, while holding that an economy is a system composed by interacting agents, has adopted some very strict assumptions concerning the functioning of the economic process and the behaviour of the decision makers. These assumptions tend to guarantee predictability and certainty of results to the price of expunging from the subject-matter of economic theory the phenomena of evolution, change, growth and development.

Twentieth-century idea of economics has neglected complexity and traced precisely defined borders where in the real world borders are uncertain, and concepts unable to be captured in one precise definition. In the last decades there has been an increasing awareness of the fact that economic data provide little evidence of linear, simple dynamics, and of lasting convergence to stationary states or regular cyclical behaviour, and that economic reality is rife with nonlinearity, discontinuity, and a variety of phenomena that are not so easily predicted or understood, and that the order of the economy appears to emerge from the complex interactions that constitute the evolutionary process of the economy.

This body of literature has come to be known as “complexity theory”. It has resulted in a critical reassessment of the twentieth century’s economic theory that has led to question the “Walrasian” mainstream approach in favour of a Marshallian-type view of economic phenomena. In fact, the roots of this new conception of economics can be found in economists like Marshall, Keynes, Schumpeter, Hayek and Simon, who had a live sense of the complexity of the interrelation between economic phenomena and of the role of history and institutions in their evolution.

They expressed a reasonable preference for a discursive, sophisticatedly informal and context-based style of exposition and felt that dealing with economic complexity fundamentally implies using a plurality of languages. Since the 1960s the increasing availability of new computational techniques and the development of non-linear mathematics has allowed to explore the implications of such awareness of the complexity of economic phenomena.

Complexity is emerging as a new unifying theory to understand endogenous change and transformation across a variety of disciplines, including economics, mathematics, physics, biology. Complexity thinking is primarily a systemic and dynamic approach according to which the outcome of the behaviour of each agent and of the system into which each agent is embedded, is intrinsically dynamic and can only be understood as the result of multiple interactions among heterogeneous agents embedded in evolving structures.