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a b s t r a c tHumans currently spend over 3 billion person-hours per week playing computer games. Most of these games are purely for entertainment, but use of computer games for education has also expanded dramatically. At the same time, experimental games have become a staple of social science research but have depended on relatively small sample sizes and simple, abstract situations, limiting their range and applicability. If only a fraction of the time spent playing computer games could be harnessed for research, it would open up a huge range of new opportunities. We review the use of games in research, education, and entertainment and develop ideas for integrating these three functions around the idea of ecosystem services valuation. This approach to valuation can be seen as a version of choice modeling that allows players to generate their own scenarios taking account of the trade-offs embedded in the game, rather than simply ranking pre-formed scenarios. We outline a prototype game called "Lagom Island" to test the proposition that gaming can be used to reveal the value of ecosystem services. Our prototype provides a potential pathway and functional building blocks for approaching the relatively untapped potential of games in the context of ecosystem services research.

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Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.