Policy framework and systems management of global climate change

Climate change is representative of a general class of environmental issues where decisions have to be taken under controversies. The policy framework for these kinds of decisions is defined by three important traits: scientific ignorance, mediatization and the need for innovation. Scientific ignorance is an issue here because decisions must be taken before the end of scientific controversies about the predictability of future climate. Mediatization is key because agents can't have a sensible experience of the global climate change, and some interest-holders (future generations, distant countries) cannot participate directly in the decision. Third, the need for innovation is crucial because today's technology offers the only alternative between fossil fuels and nuclear power as a main primary energy source.In the case of climate change, the institutional context is the United Nations Framework Convention on Climate Change. The making of global environmental policy is framed not upon a hypothetical code of international law (there is no such a thing), but upon a body of doctrine arising from consistent reference to a given set of principles. The key principles are sustainability (satisfying the need of present generations without preventing future generations to satisfy theirs), precaution (ignorance is not an excuse for inaction), the common but differentiated responsibility (developed countries take the lead in action against climate change), and economic efficiency (which lead to prefer flexible instruments over blind regulation).Given the scientific controversies and the fuzziness of guiding principles, no clear-cut demonstration could justify the choice of a theoretically optimum course of action, even in the short term. Historically, climate negotiations can be seen as an oscillation between two regulation modes. On one side is coordinated policies and measures, where countries adopt an uniform international rate of carbon tax. On the other side is emission trading, where a defined emission reduction target is allocated to each country.changement climatique; Protocole de Kyoto

Assortment Size and Performance of Online Sellers: An Inverted U-Shaped Relationship

This paper investigates the role of assortment size in sellers’ performance in the e-commerce context, which has been primarily associated with lowered search costs and switching costs. However, in contrast to the findings in the literature, our theoretical analysis postulates an inverted U-shaped association, showing that performance of online sellers increases and then decreases as the assortment size becomes larger. The nonlinear effect can be effectively explained by the interplay between the benefits derived from simultaneous consumer utility and the liabilities derived from the competition-intensifying effect. Additionally, the optimal level of assortment size is reduced when market density or product uncertainty is high. Using a data set of 10,000 online sellers from a large e-commerce platform, our hypotheses concerning the inverted U-shaped curve and moderation effects of market density and product uncertainty are statistically supported. Our research contributes to the assortment literature by revealing the special effects of assortment size in the online retailing context, and provides practical implications for online sellers’ assortment planning and optimization under both general settings and specific conditions

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Peeking into the other half of the glass : handling polarization in recommender systems.

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users\u27 interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item\u27s content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated with the same hashtag. However hashtags are not used in a consistent manner and most importantly, are completely optional to use. This makes them unreliable as the sole mechanism for searching for relevant information. We investigate mechanisms for consolidating the hashtag space using recommender systems. Our methods are general enough that they can be used for hashtag annotation in various social media services such as twitter, as well as for general item recommendations on systems that rely on implicit user interest data such as e-learning and news sites, or explicit user ratings, such as e-commerce and online entertainment sites. To conclude, we propose a methodology to extract stories based on two types of hashtag co-occurrence graphs. Our research in hashtag recommendation was able to exploit the textual content that is available as part of user messages or posts, and thus resulted in hybrid recommendation strategies. Using content within this context can bridge polarization boundaries. However, when content is not available, is missing, or is unreliable, as in the case of platforms that are rich in multimedia and multilingual posts, the content option becomes less powerful and pure collaborative filtering regains its important role, along with the challenges of polarization