A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Artificial economics aims to provide a generative approach to understanding problems in economics and social sciences. It is based on the consistent use of agent-based models and computational techniques. It encompasses a rich variety of techniques that generalize numerical analysis, mathematical programming, and micro-simulations. The peer-reviewed contributions in this volume address applications of artificial economics to markets and trading, auctions, networks, management, industry sectors, macroeconomics, and demographics and culture.

As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.

Artificial economics is a computational approach that aims to explain economic systems by modeling them as societies of intelligent software agents. The individual agents make autonomous decisions, but their actual behaviors are constrained by available resources, other individuals' behaviors, and institutions. Intelligent software agents have communicative skills that enable simulation of negotiation, trade, reputation, and other forms of knowledge transfer that are at the basis of economic life. Incorporated learning mechanisms may adapt the agents' behaviors. In artificial economics, all system behavior is generated from the individual agents' simulated decisions; no system level laws are a priori imposed. For instance, price convergence and market clearing may emerge, but not necessarily. Thus, artificial economics facilitates the study of the mechanisms that make the economy function. This book presents a selection of peer-reviewed papers addressing recent developments in this field between economics and computer science.

This book is based on presentations at AE’2006 (Aalborg, Denmark) – the second symposium on Artificial Economics. As a new constructive simulation method, Agent-Based Computational Economics (ACE) has in recent years proven its strength and applicability. Coverage in this volume extends to well known questions of economics, like the existence of market efficiency, and to questions raised by new analytical tools, for example networks of social interaction.

An introductory overview of the methods, models and interdisciplinary links of artificial economics. Addresses the differences between the assumptions and methods of artificial economics and those of mainstream economics. This is one of the first books to fully address, in an intuitive and conceptual form, this new way of doing economics.

As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.