Both cascades and herds can result in socially inefficient and/or fragile equilibria, where a small piece of new information can lead to a radically different equilibrium (Banerjee, 1992; Chari and Kehoe, 2000; Chamley, 2004). For example, risk managers may use the same risk management model simply because it has become the norm in the industry. Since all react similarly to news, unexpected news can all of a sudden lead to system-wide readjustments that unhinge markets. New signals can also lead to inefficient contagion (Gorton, 1988; Kodres and Pristker, 2002; Rochet and Vives, 2004). For example, the failure of one bank can boost expectations of failure for other banks with similar characteristics, even though the latter are perfectly solvent. Thus, one isolated failure (for example Lehman Brothers) can have devastating systemic consequences.

The policy response to such market failures may involve official supervision and monitoring of individual financial intermediaries as well as systemically oriented information, both of which are likely to be under-produced by the private sector (auditors, rating agencies or private analysts). In the case of central banks, well-funded and well-researched _financial stability reports_ (a public good) acquire a heightened importance. In the case of rating agencies, altering compensation schemes (i.e., whether bond issuers or investors pay for the ratings) can have important implications for the quality of the ratings (Kashyap and Kovrijnykh, 2013).

# **6. Animal spirits: Tempering mood swings**

We conclude our journey with an exploration of the GBC domain, which lies at the core of our density map and features the interactions between all the frictions and sources of aggregate volatility that we have been examining so fat. In this domain, bounded rationality manifests itself in two ways: (i) as a limited ability to process information due to information processing frictions, or (ii) as a deviation from the classical behavioral postulates of traditional economic theory, much along the biases identified by behavioral finance.<sup>68</sup> While both routes can lead to similarly looking deviations from rational, welfare maximizing paths, there are basic conceptual differences between the two. In the first case, the problem is “processing capacity”. Being unable to process information optimally, the boundedly rational agent takes short-cuts (heuristic approximations) that lead to systematic behavioral biases.<sup>69</sup> In the second case, the problem is “preference wiring”. A boundedly rational agent may correctly assess the pay-offs and implications of risk-taking behavior but may not value the pay-offs based on conventional expected utility maximization. Thus, he may follow seemingly reckless risk-taking behavior not because he is unable to calculate the odds but simply because he is mesmerized by the gamble. Alternatively, he may become excessively risk averse when his behavior becomes guided solely by the wish to avoid at all costs the worse possible outcome.

> 68 For comprehensive surveys on behavioral finance and sentiment-based asset pricing see Hirshleifer (2001) and Barberis and Thaler (2003). Kahneman (2011) presents an excellent compendium on the state of the art in behavioral economics. Pesendorfer (2013) argues that many (if not all) of the biases documented by behavioral economics can be explained by traditional economics once additional constraints are introduced. Pesendorfer’s view is more congenial to the characterization of bounded rationality as cognition frictions-induced.

> 69 While in the agency paradigm, the emphasis was on _bilateral_ , contract-specific information and rationality, the emphasis here is on the limits of _aggregate_ information and rationality that can get in the way of players’ ability to ascertain whether asset prices are shaped by fundamentals or following a bubble.

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