From economics to physics

The role of physicists in finance is changing, as quantitative trading opens an exciting alternative to traditional financial modelling, and data science lures would-be 'quants' away. But the void is being steadily filled by a new type of analyst.

Taking the expectation value of an observable is not the same as averaging over time.

The economic turmoil of 2020 seems likely to continue into 2021, putting economics at the forefront of discussions. But what can physicists learn from economists?

Inspired to methods developed for the study of complex systems, a framework for predicting gross domestic product growth outperforms the accuracy of the five-year forecast of the International Monetary Fund.

The interconnectedness of the financial system is increasing over time, and modelling it as a network captures key interactions between financial institutions. This Review surveys the most successful applications of statistical physics and complex networks to the description and understanding of financial networks.

Complexity economics relaxes the assumptions of neoclassical economics to assume that agents differ, that they have imperfect information about other agents and they must, therefore, try to make sense of the situation they face. This Perspective sketches the ideas of complexity economics and describes how it links to complexity science more broadly.

Economic complexity methods predict changes in the geography of economic activities and explain differences in economic growth, inequality, greenhouse emissions and labour market outcomes. This Review summarizes a decade of research on economic complexity and its applications.

This Perspective argues that ergodicity — a foundational concept in equilibrium statistical physics — is wrongly assumed in much of the quantitative economics literature. By asking the extent to which dynamical problems can be replaced by probabilistic ones, many economics puzzles are resolved in a natural and empirically testable fashion.

Faced with an economic crisis as large and rapid as that precipitated by the COVID-19 pandemic, economists have turned to new ‘fast indicators’ based on big data, as Andy Haldane and Shiv Chowla of the Bank of England explain.

János Kertész and Johannes Wachs discuss how complexity science and network science are particularly useful for identifying and describing the hidden traces of economic misbehaviour such as fraud and corruption.

Technological innovation seems to be dominated by chance. But a new mathematical analysis suggests we might be able to anticipate when seemingly useless technologies become keystones of more complex environments.