Crises and Collective Socio-Economic Phenomena: Simple Models and Challenges

By Jean-Philippe Bouchaud. 2012

232 citations recorded by the source corpus.

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 so-called Random Field Ising model (rfim) 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 destabilizing self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and that 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 fail badly 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 from being 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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2ef8068d3b7242cea24eb6c7bfa40c688e9317fa

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Citations in the corpus

Ising formulations of many NP problems (2013, background): “Conversely, computational hardness of solving glassy problems has implications for the difficulty of the solutions to important scientific problems ranging from polymer folding [25, 26] to memory [27] to collective decision making in economics and social sciences [28, 29].”
Two centuries of trend following (2014, background): “In a complex world where information is diﬃcult to decipher, trend following – together with herding – is one of the “fast and frugal” heuristics [31] that most people are tempted to use [32].”
The statistical physics of cities (2019, background): “However, this case is potentially very interesting to understand in a socio-economical context, and in particular the meaning of local equilibrium currents remains to be elucidated [28].”