10,712 research outputs found

    Experimental auctions, collective induction and choice shift: willingness-to-pay for rice quality in Senegal

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    We propose a collective induction treatment as an aggregator of information and preferences, which enables testing whether consumer preferences for food quality elicited through experimental auctions are robust to aggregation. We develop a two-stage estimation method based on social judgement scheme theory to identify the determinants of social influence in collective induction. Our method is tested in a market experiment aiming to assess consumers willingness-to-pay for rice quality in Senegal. No significant choice shift was observed after collective induction, which suggests that consumer preferences for rice quality are robust to aggregation. Almost three quarters of social influence captured by the model and the variables was explained by social status, market expertise and information

    Thought and Behavior Contagion in Capital Markets

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    Prevailing models of capital markets capture a limited form of social influence and information transmission, in which the beliefs and behavior of an investor affects others only through market price, information transmission and processing is simple (without thoughts and feelings), and there is no localization in the influence of an investor on others. In reality, individuals often process verbal arguments obtained in conversation or from media presentations, and observe the behavior of others. We review here evidence concerning how these activities cause beliefs and behaviors to spread, affect financial decisions, and affect market prices; and theoretical models of social influence and its effects on capital markets. Social influence is central to how information and investor sentiment are transmitted, so thought and behavior contagion should be incorporated into the theory of capital markets.capital markets; thought contagion; behavioral contagion; herd behavior; information cascades; social learning; investor psychology; accounting regulation; disclosure policy; behavioral finance; market efficiency; popular models; memes

    Hierarchical Attention Network for Visually-aware Food Recommendation

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    Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

    Participatory Modelling and Decision Support for Natural Resources Management in Climate Change Research

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    The ever greater role given to public participation by laws and regulations, in particular in the field of environmental management calls for new operational methods and tools for managers and practitioners. This paper analyses the potentials and the critical limitations of current approaches in the fields of simulation modelling (SM), public participation (PP) and decision analysis (DA), for natural resources management within the context of climate change research. The potential synergies of combining SM, PP and DA into an integrated methodological framework are identified and a methodological proposal is presented, called NetSyMoD (Network Analysis – Creative System Modelling – Decision Support), which aims at facilitating the involvement of stakeholders or experts in policy - or decision-making processes (P/DMP). A generic P/DMP is formalised in NetSyMoD as a sequence of six main phases: (i) Actors analysis; (ii) Problem analysis; (iii) Creative System Modelling; (iv) DSS design; (v) Analysis of Options; and (vi) Action taking and monitoring. Several variants of the NetSyMoD approach have been adapted to different contexts such as integrated water resources management and coastal management, and, recently it has been applied in climate change research projects. Experience has shown that NetSyMoD may be a useful framework for skilled professionals, for guiding the P/DMP, and providing practical solutions to problems encountered in the different phases of the decision/policy making process, in particular when future scenarios or projections have to be considered, such as in the case of developing and selecting adaptation policies. The various applications of NetSyMoD share the same approach for problem analysis and communication within the group of selected actors, based upon the use of creative thinking techniques, the formalisation of human-environment relationships through the DPSIR framework, and the use of multi-criteria analysis through a Decision Support System (DSS) software.Modelling, Public Participation, Natural Resource Management, Policy, Decision-Making, Governance, DSS

    Towards Data-Efficient Mobility Analytics in Spatial Networks

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