Crises and collective socio-economic phenomena: simple models and challenges

TL;DR

This paper reviews models incorporating heterogeneities and interactions, particularly the RFIM, to explain socio-economic crises, highlighting the importance of feedback loops, herding, and time-scale effects in understanding collective phenomena.

Contribution

It demonstrates how the RFIM framework unifies various socio-economic phenomena and discusses testable predictions and the limitations of current models assuming detailed balance.

Findings

RFIM captures collective socio-economic phenomena leading to crises

Herding and trending effects can destabilize markets

Long time-scales can prevent reaching optimal equilibria

Abstract

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.