Working papers results

We use decision theory to confront uncertainty that is sufficiently broad to incorporate 'models as approximations'.We presume the existence of a featured collection of what we call 'structured models' that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend min-max analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns.

We use a recently developed right-tail variation of the Augmented Dickey-Fuller unit root test to identify and date-stamp periods of mildly explosive behavior in the weekly time series of eight U.S. fixed income yield spreads between September 2002 and April 2018. We find statistically significant evidence of mildly explosive dynamics in six of these spreads, two of which are short/medium-term mortgagerelated spreads. We show that the time intervals characterized by instability that we estimate from these yield spreads capture known episodes of financial and economic distress in the U.S. economy. Mild explosiveness migrates from short-term funding markets to medium- and long-term markets during the Great Financial Crisis of 2007-09. Furthermore, we statistically validate the conjecture that the initial panic of 2007 migrated from segments of the ABX market to other U.S. fixed income markets in the early phases of the financial crisis.

Policymaking during a pandemic can be extremely challenging. As COVID-19 is a new disease and its global impacts are unprecedented, decisions need to be made in a highly uncertain, complex and rapidly changing environment. In such a context, in which human lives and the economy are at stake, we argue that using ideas and constructs from modern decision theory, even informally, will make policymaking more a responsible and transparent process.

This paper investigates the effect of terrorism financing and recruitment on attacks. A Sharia-compliant institution in Pakistan induces exogenous variation in the funding of terrorist groups through their religious affiliation. I isolate the supply of terrorist attacks by following multiple terrorist groups with different affiliations operating in various cities. Higher terrorism financing, in a given location and period, generates more attacks in the same location and period. This effect increases in recruitment, measured through darkweb data, inputs by two judges and machine-learning. This evidence is consistent with terrorist organizations facing financial frictions to their internal capital market.

Negative advertising is frequent in electoral campaigns, despite its ambiguous effectiveness: negativity may reduce voters' evaluation of the targeted politician but have a backlash effect for the attacker. We study the effect of negative advertising in electoral races with more than two candidates with a large scale field experiment during an electoral campaign for mayor in Italy and a survey experiment in a fictitious mayoral campaign. In our field experiment, we find a strong, positive spillover effect on the third main candidate (neither the target nor the attacker). This effect is confirmed in our survey experiment, which creates a controlled environment with no ideological components nor strategic voting. The negative ad has no impact on the targeted incumbent, has a sizable backlash effect on the attacker, and largely benefits the idle candidate. The attacker is perceived as less cooperative, less likely to lead a successful government, and more ideologically extreme.

We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation (see below picture) where pt (a; A) is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, is a time dependent noise parameter measuring the unit cost of information, u is a time independent utility function, and a is an alternative-specific bias that determines the initial choice probabilities and possibly reflects prior information. Our axiomatic analysis provides a behavioral foundation of softmax (also known as Multinomial Logit Model when a is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behavior. Jointly, the two approaches provide a thorough understanding of soft-maximization in terms of internal causes (neurophysiological mechanisms) and external effects (testable implications).

We study agents in a social network who receive initial noisy signals about a fundamental parameter and then, in each period, solve a robust non-parametric estimation problem given their previous information and the most recent estimates of their neighbors. The resulting robust opinion aggregators are characterized by simple functional properties: normalization, monotonicity, and translation invariance. These aggregators admit the linear DeGroot's model as a particular parametric specification. However, robust opinion aggregators allow for additional features such as overweighting/underweighting of extreme opinions, confirmatory bias, as well as discarding information obtained from sources perceived as redundant. We show that under this general model, it is still possible to link the long-run behavior of the opinions to the structure of the underlying network. In particular, we provide sufficient conditions for convergence and consensus and we offer some bounds on the rate of convergence. In some parametric cases, we derive the influence of the agents on the limit opinions and we stress how it depends on their centrality as well as on their initial signals. Finally, we study sufficient conditions under which a large society learns the true parameter while also highlighting why this property may fail.

Recent studies argue that major crises can have long lasting effects on individual behavior. While most studies focused on natural disasters, we explore the consequences of the global pandemic caused by a lethal influenza virus in 1918-19: the so-called "Spanish Flu". This was by far the worst pandemic of modern history, causing up to 100 million deaths worldwide. Using information about attitudes of respondents to the General Social Survey (GSS), we find evidence that experiencing the pandemic likely had permanent consequences in terms of individuals' social trust. Our findings suggest that lower social trust was passed on to the descendants of the survivors of the Spanish Flu who migrated to the US. As trust is a crucial factor for long-term economic development, our research offers a new angle from which to assess current health threats.

This paper describes price discovery and liquidity provision in a dynamic limit order market with asymmetric information and non-Markovian learning. Investors condition on information in both the current limit order book and also, unlike in previous research, on the prior order history when deciding whether to provide or take liquidity. Our analysis shows that the information content of the prior order history can be substantial. Surprisingly, the information content of equilibrium orders can differ from order direction and aggressiveness.

We study the role of perceived threats from cultural diversity induced by terrorist attacks and a salient criminal event on public discourse and voters' support for far-right parties. We first develop a rule which allocates Twitter users in Germany to electoral districts and then use a machine learning method to compute measures of textual similarity between the tweets they produce and tweets by accounts of the main German parties. Using the dates of the aforementioned exogenous events we estimate constituency-level shifts in similarity to party language. We find that following these events Twitter text becomes on average more similar to that of the main far-right party, AfD, while the opposite happens for some of the other parties. Regressing estimated shifts in similarity on changes in vote shares between federal elections we find a significant association. Our results point to the role of perceived threats on the success of nationalist parties.