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We study law enforcement guided by data-informed predictions of "hot spots" for likely criminal offenses. Such "predictive" enforcement could lead to data being selectively and disproportionately collected from neighborhoods targeted for enforcement by the prediction. Predictive enforcement that fails to account for this endogenous "datafication" may lead to the over-policing of traditionally high-crime neighborhoods and performs poorly, in particular, in some cases as poorly as if no data were used. Endogenizing the incentives for criminal offenses identifies additional deterrence benefits from the informationally efficient use of data.

This paper studies sequential information acquisition by an ambiguity-averse decision maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM's stopping rule is non-monotonic in beliefs and features randomized stopping.

We consider auction environments in which at the time of the auction bidders observe signals about their ex-post value. We introduce a model of novice bidders who do not know know the joint distribution of signals and instead build a statistical model relating others' bids to their own ex post value from the data sets accessible from past similar auctions. Crucially, we assume that only ex post values and bids are accessible while signals observed by bidders in past auctions remain private. We consider steady-states in such environments, and importantly we allow for correlation in the signal distribution. We first observe that data-driven bidders may behave suboptimally in classical auctions such as the second-price or first-price auctions whenever there are correlations. Allowing for a mix of rational (or experienced) and data-driven (novice) bidders results in inefficiencies in such auctions, and we show the inefficiency extends to all auction-like mechanisms in which bidders are restricted to submit one-dimensional (real-valued) bids.

We consider a dynamic model of Bayesian persuasion in which information takes time and is costly for the sender to generate and for the receiver to process, and neither player can commit to their future actions. Persuasion may totally collapse in a Markov perfect equilibrium (MPE) of this game. However, for persuasion costs sufficiently small, a version of a folk theorem holds: outcomes that approximate Kamenica and Gentzkow (2011)'s sender-optimal persuasion as well as full revelation and everything in between are obtained in MPE, as the cost vanishes.

We consider a decision maker (DM) who, before taking an action, seeks information by allocating her limited attention dynamically over different news sources that are biased toward alternative actions. Endogenous choice of information generates rich dynamics: The chosen news source either reinforces or weakens the prior, shaping subsequent attention choices, belief updating, and the final action. The DM adopts a learning strategy biased toward the current belief when the belief is extreme and against that belief when it is moderate. Applied to consumption of news media, observed behavior exhibits an `echo-chamber' effect for partisan voters and a novel `anti echo-chamber' effect for moderates.