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We develop a model of learning under uncertainty to explain when and why politicians decide to implement new policies. We estimate the model using data from the recent expansion in the number of new police forces in Brazilian municipalities. Although law enforcement in Brazil is primarily a responsibility of state governments, municipalities have constitutional power to form their own police forces. In the past two decades local administrations increasingly started to invest in this policy area, as the share of Brazilian cities with a police force increased from 10% in 2000 to 23% in 2022. In our framework, local politicians are uncertain about the electoral impact of funding local law enforcement. While it might reduce crime, it might also crowd-out resources from more popular public policies. What is more, if voters’ demands for policing are heterogeneous across the electorate, this policy is only attractive to certain parties/politicians. Without a local police force, mayors acquire their beliefs about its electoral impact based on the experience of neighboring municipalities with law enforcement and crime. These beliefs influence the choice to fund their own force. Overall, we show that learning is crucial in explaining the recent wave of police expansion in Brazil. We also show how Right- and Left-wing mayors respond differently to the electoral returns of policing and to reductions in crime.