Assigning a subjective value to a contribution to a public good often requires reflection. For many reasons, this reflection may be put off, reinforcing the underprovision of public goods. We hypothesise that nudging individuals to reflect on whether to contribute to a public good leads to the formation of issue-specific altruistic preferences. The hypothesis is tested in a large-scale field experiment on blood donations. We find that an Ôactive-decisionÕ intervention substantially increases donations among subjects who had not previously thought about the importance of donating blood. By contrast, contributions of individuals who had previously engaged in such reflection are unchanged.

This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions.We also propose a recommender system based on the developed algorithm. This recommender provides recommendations to the users to reduce their energy consumption. The recommender system was deployed to a set of test homes. The test participants rated the impact of the recommendations on their comfort. We used this feedback to adjust the system parameters and make it more accurate during a second test phase. The historical dataset provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%.

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.The system was implemented and deployed to a set of test homes. The test participants were able to rate the impact of the recommendations on their comfort. This feedback was used to adjust the system parameters and make it more accurate during a second test phase. The historical data set provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%. We found out that a recommender system that uses an algorithm that mines patterns based on their confidence, independent of their frequency and periodicity, might achieve better results and a higher acceptance by users.

Camera surveillance has recently gained prominence in policy proposals on combating terrorism. We evaluate the instrument based on a comparative perspective and previous evidence on crime. We expect camera surveillance to have a relatively smaller deterrent effect on terrorism than on other forms of crime. In particular, we emphasize that: (i) terrorists have more opportunities for substitution; (ii) targets under camera surveillance might become more and not less attractive if terrorists aspire media attention; (iii) real-time interventions are limited as behaviour is only understood as suspicious in the light of hindsight; and (iv) closed-circuit television might crowd out social surveillance.

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