This overview paper examines three areas crucial to understanding why, despite clear scientific evidence for the growing environmental impacts of tourism transport, there is large-scale inertia in structural transitions and a lack of political will to enact meaningful sustainable mobility policies. These include the importance of addressing socio-technical factors, barriers posed by “technology myths” and the need to overcome “transport taboos” in policy-making. The paper seeks pathways to sustainable mobility by bridging the science–policy gap between academic research and researchers, and policy-makers and practitioners. It introduces key papers presented at the Freiburg 2014 workshop, covering the case for researcher engagement using advocacy and participatory approaches, the role of universities in creating their own social mobility policies, the power of social mechanisms encouraging long-haul travel, issues in consumer responsibility development, industry self-regulation and the operation of realpolitik decision-making and implementation inside formal and informal destination-based mobility partnerships. Overall, the paper argues that governments and the tourism and transport industries must take a more cautious approach to the technological optimism that fosters policy inertia, and that policy-makers must take a more open approach to implementing sustainable transport policies. A research agenda for desirable transport futures is suggested.
This chapter examines how the Berlin neighborhood of eastern Kreuzberg attained its mythical image as a youth culture neighborhood. It combines theories of myth-making and canonization with the concept of preference construction, thereby developing new tools to assess how certain ideas and images (e.g. from youth cultures) come to be collectively preferred and believed as more exceptional or true in spite of similar ones. It argues that through the construction of preferences, people have become invested in Kreuzberg’s historically alternative and contemporary trendsetting images. Through this case study, the chapter shows that youth cultures appeal to people because of their outspoken and elusive nature, something that continually urges people to ask themselves how they relate to them. People thus cultivate preferences when they take a position regarding youth cultures. When people become collectively invested in certain shared ideas and images, subcultural myths are born.
Social networks and news outlets use recommender systems to distribute information and suggest news to their users. These algorithms are an attractive solution to deal with the massive amount of content on the web [6]. However, some organisations prioritise retention and maximisation of the number of access, which can be incompatible with values like the diversity of content and transparency. In recent years critics have warned of the dangers of algorithmic curation. The term filter bubbles, coined by the internet activist Eli Pariser [1], describes the outcome of pre-selected personalisation, where users are trapped in a bubble of similar contents. Pariser warns that it is not the user but the algorithm that curates and selects interesting topics to watch or read. Still, there is disagreement about the consequences for individuals and society. Research on the existence of filter bubbles is inconclusive. Fletcher in [5], claims that the term filter bubbles is an oversimplification of a much more complex system involving cognitive processes and social and technological interactions. And most of the empirical studies indicate that algorithmic recommendations have not locked large segments of the audience into bubbles [3] [6]. We built an agent-based simulation tool to study the dynamic and complex interplay between individual choices and social and technological interaction. The model includes different recommendation algorithms and a range of cognitive filters that can simulate different social network dynamics. The cognitive filters are based on the triple-filter bubble model [2]. The tool can be used to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions on recommender systems can mitigate adverse side effects like the presence of filter bubbles. The resulting tool is an open-source interactive web interface, allowing the simulation with different parameters such as users' characteristics, social networks and recommender system settings (see Fig. 1). The ABM model, implemented in Python Mesa [4], allows users to visualise, compare and analyse the consequence of combining various factors. Experiment results are similar to the ones published in the Triple Filter Bubble paper [2]. The novelty is the option to use a real collaborative-filter recommendation system and a new metric to measure the distance between users' innate and final opinions. We observed that slight modifications in the recommendation system, exposing items within the boundaries of users' latitude of acceptance, could increase content diversity.References 1. Pariser, E.: The filter bubble: What the internet is hiding from you. Penguin, New York, NY (2011) 2. Geschke, D., Lorenz, J., Holtz, P.: The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology (2019), 58, 129–149 3. Möller, J., Trilling, D., Helberger, N. , and van Es, B.: Do Not Blame It on the Algorithm: An Empirical Assessment of Multiple Recommender Systems and Their Impact on Content Diversity. Information, Communication and Society 21, no. 7 (2018): 959–77 4. Mesa: Agent-based modeling in Python, https://mesa.readthedocs.io/. Last accessed 2 Sep 2022 5. Fletcher, R.: The truth behind filter bubbles: Bursting some myths. Digital News Report - Reuters Institute (2020). https://reutersinstitute.politics.ox.ac.uk/news/truth-behind-filter-bubblesbursting-some-myths. Last accessed 2 Sep 2022 6. Haim, M., Graefe, A, Brosius, H: Burst of the Filter Bubble?: Effects of Personalization on the Diversity of Google News. Digital Journalism 6, no. 3 (2018): 330–43.
