21 democratic paradigm where everybody was fooled. And unlike the externalities paradigm, it does not require a vengeful god to intervene exogenously with tail-risk events to unleash the dynamics of a downward spiral. Instead, it has its own fully endogenous dynamics, with favorable returns and optimism feeding each other on the way up, adverse returns and pessimism on the way down. The dynamics are akin to Schumpeter’s creative destruction, where cycles are a natural part of the evolutionary process. However, unlike the traditional Schumpeterian process, where some do well while others perish at every point in the cycle, the dynamics in the mood swings paradigm are more like “Schumpeter on steroids”, as financial innovation cycles can have a devastating systemic impact because everyone follows the same path, up the bubble and down the abyss.

The mood swings paradigm, however, is not free of puzzles and difficulties. In particular, uncertainty-driven mood swings are easy to invoke but harder to model.<sup>55</sup> To be sure, one would expect rationality (even if bounded) and path dependence to constrain feasible outcomes. However, modeling options range widely, from rationally updated priors in a world of imperfect information to heuristically updated beliefs stemming from biased perceptions (personal or group-based) of an excessively complex reality. Thus, unlike incentive distortions under the moral hazard and externalities paradigms, which are firmly grounded in traditional economic theory, modeling mood swings may require some departure from orthodox theory. In any event, it is also rather surprising that market participants were seemingly oblivious to the risks underlying the process of financial innovation. Did such obliviousness simply reflect a difficulty to look outside the box and connect the dots? Did such difficulty reflect the fact that markets do not reward systemic risk gazing (a theme to which we will come back in the next section)? Or was something more sinister at play, either moral hazard or non internalized externalities? In particular, absent externalities, one wonders whether uncertainty alone could pack so much punch, particularly on the way down.

55 The importance of mood swings for financial bubbles and panics has been widely recognized. It finds its roots in Keynes’ animal spirits and Hyman Minsky’s writings on financial crises (see Minsky, 1975). More recently, it was popularized by Kindleberger (1996) and Shiller (2006). While many attempts have been made to model mood driven-cycles within the traditional world of rational expectations with full information (see the seminal contribution of Azariadis, 1981), the conditions for such rational bubbles to exist have been shown to be rather limited (Santos and Woodford, 1997). However, moods play a much more important role once one assumes problems with the information (imprecision or uncertainty) or the way one deals with it, which, in turn, may (or may not) require abandoning the assumption of full rationality. Epstein and Wang (1994), and more recently Fostel and Geneakoplos (2008), showed that multiple priors can lead to models where beliefs influence asset prices in a fully rational world. In addition, Geweke (2001) and Weitzman (2007) showed that, when there is too much uncertainty, fully rational human behavior may not conform to the precepts of traditional economic theory as defined by the standard expected-utility framework. Abandoning the assumption of full rationality opens up the scope for innate biases in the way economic agents process information and make decisions (see the recent surveys of behavioral finance in Barberis and Thaler, 2003, and Della Vigna, 2007). Attempts to explore the implications of such limitations for finance and credit cycles are making some headway. For example, Shleifer and Vishny (1997) showed that inefficient asset pricing driven by noise traders can persist despite the presence of rational arbitrageurs. Lo (2004) proposed an evolutionary approach to economic interactions. De Grauwe (2008) showed that it is possible to generate endogenous cycles when agents use simple heuristic rules to interpret the dynamics of a model they do not fully comprehend.