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

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.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment

The value of a smile: game theory with a human face

Many economists and biologists view cooperation as anomalous: animals (including humans) that pursue their own self-interest have superior survival odds to their altruistic or cooperative neighbors. However, in many situations there are substantial gains to the group that can achieve cooperation among its members, and to individuals who are members of those groups. For an individual, the key to successful cooperation is the ability to identify cooperative partners. The ability to signal and detect the intention to cooperate would be a very valuable skill for humans to posses. Smiling is frequently observed in social interactions between humans, and may be used as a signal of the intention to cooperate. However, given that humans have the ability to smile falsely, the ability to detect intentions may go far beyond the ability to recognize a smile. In the present study, we examine the value of a smile in a simple bargaining context. 120 subjects participate in a laboratory experiment consisting of a simple two-person, one-shot "trust" game with monetary payoffs. Each subject is shown a photograph of his partner prior to the game; the photograph is taken from a collection that includes one smiling and one non-smiling image for each of 60 individuals. These photographs are also rated by a separate set of subjects who complete a semantic differential survey on affective and behavioral interpretations of the images. Results lend some support to the prediction that smiles can elicit cooperation among strangers in a one-shot interaction. Other characteristics of faces also appear to elicit cooperation. Factor analysis of the survey data reveals an important factor, termed "cooperation", which is strongly related to trusting behavior in the game. This factor is correlated with smiling, but is somewhat more strongly predictive of behavior than a smile alone. In addition, males are found to be more cooperative, especially towards female images, whereas females are least cooperative towards female images