Technological change in economic models of environmental policy: a survey

This paper provides an overview of the treatment of technological change in economic models of environmental policy. Numerous economic modeling studies have confirmed the sensitivity of mid- and long-run climate change mitigation cost and benefit projections to assumptions about technology costs. In general, technical progress is considered to be a noneconomic, exogenous variable in global climate change modeling. However, there is overwhelming evidence that technological change is not an exogenous variable but to an important degree endogenous, induced by needs and pressures. Hence, some environmenteconomy models treat technological change as endogenous, responding to socio-economic variables. Three main elements in models of technological innovation are: (i) corporate investment in research and development, (ii) spillovers from R&D, and (iii) technology learning, especially learning-by-doing. The incorporation of induced technological change in different types of environmental-economic models tends to reduce the costs of environmental policy, accelerates abatement and may lead to positive spillover and negative leakage. --exogenous technological change,induced technological change,environmenteconomy models

Використання R-пакету в дистанційному курсі для моделювання і прогнозування часових рядів

In the report the R package – a programming language and software environment for statistical computing, analysis and presentation of data in graphical form, its place and role in distance learning as an example of a distance course "Modeling economic dynamics". We discuss online experience modeling and forecasting time series using R programming

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Public Systems Modeling

This is an open access book discusses readers to various methods of modeling plans and policies that address public sector issues and problems. Written for public policy and social sciences students at the upper undergraduate and graduate level, as well as public sector decision-makers, it demonstrates and compares the development and use of various deterministic and probabilistic optimization and simulation modeling methods for analyzing planning and management issues. These modeling tools offer a means of identifying and evaluating alternative plans and policies based on their physical, economic, environmental, and social impacts. Learning how to develop and use the mathematical modeling tools introduced in this book will give students useful skills when in positions of having to make informed public policy recommendations or decisions

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Technological Change in Economic Models of Environmental Policy: A Survey

This paper provides an overview of the treatment of technological change in economic models of environmental policy. Numerous economic modeling studies have confirmed the sensitivity of mid- and long-run climate change mitigation cost and benefit projections to assumptions about technology costs. In general, technical progress is considered to be a noneconomic, exogenous variable in global climate change modeling. However, there is overwhelming evidence that technological change is not an exogenous variable but to an important degree endogenous, induced by needs and pressures. Hence, some environmenteconomy models treat technological change as endogenous, responding to socio-economic variables. Three main elements in models of technological innovation are: (i) corporate investment in research and development, (ii) spillovers from R&D, and (iii) technology learning, especially learning-by-doing. The incorporation of induced technological change in different types of environmental-economic models tends to reduce the costs of environmental policy, accelerates abatement and may lead to positive spillover and negative leakage

Learning by Exporting and Productivity-investment Interaction: An Intertemporal General Equilibrium Analysis of the Growth Process in Thailand

While the discussion of Thailand and East Asian growth has been a controversy between capital accumulation and productivity stories, we analyze the general equilibrium interaction between productivity and investment in an intertemporal model. The model builds in endogenous productivity spillover effects influencing profitability and investment and produces long run growth effects of economic policy. To understand the growth process in Thailand, learning by exporting is assumed to be the main vehicle of international spillover and brings further productivity effects to the domestic economy. The dynamic simulations show how high economic growth is prolonged by multisector productivity and investment dynamics and structural shift from agriculture to exportables. The importance of trade liberalization is shown in a counterfactual analysis where protection holds back growth by serving as a barrier to productivity spillover.intertemporal growth modeling; endogenous productivity growth; learning by exporting; trade and growth; Thailand

Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model