113 research outputs found
Data Market Design through Deep Learning
The problem is a problem in economic theory to
find a set of signaling schemes (statistical experiments) to maximize expected
revenue to the information seller, where each experiment reveals some of the
information known to a seller and has a corresponding price [Bergemann et al.,
2018]. Each buyer has their own decision to make in a world environment, and
their subjective expected value for the information associated with a
particular experiment comes from the improvement in this decision and depends
on their prior and value for different outcomes. In a setting with multiple
buyers, a buyer's expected value for an experiment may also depend on the
information sold to others [Bonatti et al., 2022]. We introduce the application
of deep learning for the design of revenue-optimal data markets, looking to
expand the frontiers of what can be understood and achieved. Relative to
earlier work on deep learning for auction design [D\"utting et al., 2023], we
must learn signaling schemes rather than allocation rules and handle
these arising from modeling the downstream
actions of buyers in addition to incentive constraints on bids. Our
experiments demonstrate that this new deep learning framework can almost
precisely replicate all known solutions from theory, expand to more complex
settings, and be used to establish the optimality of new designs for data
markets and make conjectures in regard to the structure of optimal designs
Three Essays on the Relationship between Economic Development and Environmental Quality
This thesis is concerned with examining the relationship between indicators of
economic growth and environmental quality. During this process, the analysis explores and
attempts to interlink the following theoretical and empirical frameworks: Angelsen and
Kaimowitz’s theories for deforestation, the Environmental Kuznets Curve (EKC) hypothesis
and the forest transition theory. Macro-level data are used to examine the implications of
these frameworks. The implications of the first essay suggest that different crops have a
different impact on rate of change of agricultural land use. The second analysis suggests that
the results from a Directed Acyclical Graph Approach present a uni-directional causal
relationship between income and pollution emissions. The third and final essay suggests that
property rights structures and economic incentives appear to be the most probable
explanations for the forest transition in India. The macro-level nature of the data sets
employed provides information on the broad trends and patterns. For policy
recommendations, a more detailed and specific analysis needs to be carried out concentrating
on a certain region
Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis
In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div
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