How did the launch of Sputnik 1 affect NASA's process of developing an artificial Earth satellite? How do nuclear programs in countries attempting to acquire nuclear weapons compare to the Manhattan Project? Whether agencies attempt an original discovery or redicovery, they undergo a process of search in an unfamiliar environment. They converge upon successful designs through a process of trial and error. However, an agency attempting rediscovery searches with the comfort of knowing that the technology it hopes to reproduce is feasible. Meanwhile, the agency attempting an original discovery has no such guarantee. We study how simply knowing that something is discoverable affects the process of search in unfamiliar environments. Intuitively, the process of rediscovery seems simpler than original discovery, and we crystallize this idea.

We examine how well people learn when information is noisily relayed from person to person, subject to: dropping, mutation, and deliberate manipulation. This allows us to explore how communication platforms can improve learning without censoring or even examining messages, but purely by limiting the number of times a message can be relayed or the number of people to whom someone can forward a message. In particular, we analyze learning as a function of the network depth (length of relay chains) and breadth (how many chains a person has access to). Noise builds up as depth increases and so learning requires greater breadth, which we show to be characterized via a sharp threshold above which the receiver learns fully and below which the receiver learns nothing. Moreover, we show that small uncertainty about the rates of mis- and dis-information make learning from long chains of messages impossible. Optimizing learning requires either limiting depth (by controlling how many times a message can be forwarded), or if that is not possible then limiting breadth (by capping the number of people to whom someone can forward a message). Although limiting breadth decreases the overall amount of information a learner has access to, it increases the relative fraction of messages that are coming from nearby compared to far away in the network, and thus increases the signal to noise ratio. Such policies do not require the ability to fact-check, respect privacy, increase the fraction of true to false messages, and have been implemented by communication platforms. Finally, we extend our model to study learning from dropping rates (e.g., people are more likely to pass messages with one conclusion than another). We find that as the distance to primary sources grows, all learning comes from either the total number of messages received or from the content of received messages, but the learner does not need to pay attention to both.

I study how limited information and ex-post evaluation by third parties with the benefit of hindsight affect how regulators approve innovations. In the face of ambiguity over innovation characteristics, such a regulator limits or delays product approval, even when she is not waiting for new information to arrive. When evidence is costly for firms to generate but can be selectively reported, the regulator delegates information acquisition to the firm with the objective of minimizing max-regret. This model can explain observed patterns of correlation between firm costs and benefits of approval, why regulators drag their feet on approval decisions even in the face of strong favorable evidence, and support for regulatory sandboxes even when they do not hasten learning.

Identifying the optimal set of individuals to first receive information (`seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding S + x individuals can prompt a larger cascade than optimally targeting the best S individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.