Police and Thieves in the Stadium: Measuring the (Multiple) Effects of Football Matches on Crime

During large sporting events criminal behaviour may be affected via three main channels: (i) fan concentration, (ii) self incapacitation, and (iii) police displacement. In this paper I exploit information on football (soccer) matches for nine London teams linked to detailed recorded crime data at the area level to empirically estimate these different effects. My findings show that only property crime significantly increases in the communities hosting football matches but that they experience no changes in violent offences. These results are robust to controlling for a large number of game type and outcome characteristics. There is no evidence of temporal displacement of criminal activity. Our conceptual model suggests that the away game attendance effect on crime is due to voluntary incapacitation of potential offenders. I argue that the police displacement effect of hosting a match increases property crime by 7 percentage point for every extra 10,000 supporters.public economics ;

Police and Thieves in the Stadium: Measuring the (Multiple)Effects of Football Matches on Crime

During large sporting events criminal behaviour may be affected via three main channels: (i) fan concentration, (ii) self incapacitation, and (iii) police displacement. In this paper I exploit information on football (soccer) matches for nine London teams linked to detailed recorded crime data at the area level to empirically estimate these different effects. My findings show that only property crime significantly increases in the communities hosting football matches but that they experience no changes in violent offences. These results are robust to controlling for a large number of game type and outcome characteristics. There is no evidence of temporal displacement of criminal activity. Our conceptual model suggests that the away game attendance effect on crime is due to voluntary incapacitation of potential offenders. I argue that the police displacement effect of hosting a match increases property crime by 7 percentage point for every extra 10,000 supporters.Football, police, crime

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

Participatory Patterns in an International Air Quality Monitoring Initiative

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil

Improved Reinforcement Learning with Curriculum

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions which lead to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state - we call this an end-game-first curriculum. Currently the state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum; instead learning from the entire game at all times. By employing an end-game-first training curriculum to train an AlphaZero inspired player, we empirically show that the rate of learning of an artificial player can be improved during the early stages of training when compared to a player not using a training curriculum.Comment: Draft prior to submission to IEEE Trans on Games. Changed paper slightl