This book collects important contributions in behavioral economics and related topics, mainly by Japanese researchers, to provide new perspectives for the future development of economics and behavioral economics. The volume focuses especially on economic studies that examine interactions of multiple agents and/or market phenomena by using behavioral economics models. Reflecting the diverse fields of the editors, the book captures broad influences of behavioral economics on various topics in economics. Those subjects include parental altruism, economic growth and development, the relative and permanent income hypotheses, wealth distribution, asset price bubbles, auctions, search, contracts, personnel management and market efficiency and anomalies in financial markets. The chapter authors have added newly written addenda to the original articles in which they address their own subsequent works, supplementary analyses, detailed information on the underlying data and/or recent literature surveys. This will help readers to further understand recent developments in behavioral economics and related research.

The economy is examined by the authors as a complex interactive system. The emphasis is on the direct interaction between agents rather than on the indirect and autonomous interaction through the market mechanism. Contributions from economists and physicists emphasise the consequences for aggregate behaviour of the interaction between agents with limited rationality. Models of financial markets which exhibit many of the stylised facts of empirical markets such as bubbles, herd behaviour and long memory are presented. This includes contributions on bargaining, buyer-seller relations, the evolution of economic networks and several aspects of macro-economic behaviour. This book will be of interest to all those interested in the foundations of collective social and economic behaviour and in particular, to those concerned with the dynamics of market behaviour and recent applications of physics to economics.

Evolutionary game theory studies the behaviour of large populations of strategically interacting agents & is used by economists to predict in settings where traditional assumptions about the rationality of agents & knowledge may be inapplicable.

Black women’s experiences functioning as mothers, teachers and leaders are confounding and complex. Queen Mothers from Ghanaian tradition are revered as the leaders of their matrilineal families and the teachers of the high chiefs (Müller, 2013; Stoeltje, 1997). Conversely, the influence of the British Queen Mother on Black women in the Americas translates as a powerless title of (dis)courtesy. Characterized as a deviant figure by colonialists, the Black Queen Mother’s role as disruptive agent was created by White domination of Black life (Masenya, 2014) and this branding persists among contemporary perceptions of Black women who function as the mother, teacher, or leader figure in various spaces. Nevertheless, Black women as cultural anomalies were suitable to mother others for centuries in their roles as chattel and domestic servants in the United States. Dill (2014), Lawson (2000), Lewis (1977) and Rodriguez (2016) provide explorations of the devaluation of Black women in roles of power with these effects wide-ranging from economic and family security, professional and business development, healthcare maintenance, political representation, spiritual enlightenment and educational achievement. This text interrogates contexts where Black women function as Queen Mothers and contests the trivialization of their manifold contributions. The contributed chapters explore: The myriad experiences of Black women mothering, teaching and leading their children, families and communities; how spirituality has influenced the leadership styles of Black women as mothers and teachers; and how Black women are uniquely positioned to mother, teach, and lead in personal and professional spaces.

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.

This book presents essential insights on environmental policy derived from behavioral economics. The authors demonstrate the potential of behavioral economics to drive environmental protection and to generate concrete proposals for the efficient design of policy instruments. Moreover, detailed recommendations on how to use “nudges” and related instruments to move industry and society toward a sustainable course are presented. This book addresses the needs of environmental economists, behavioral economists and environmental policymakers, as well as all readers interested in the intersection between behavioral economics and environmental policy.

Handbook of Behavioral Economics: Foundations and Applications presents the concepts and tools of behavioral economics. Its authors are all economists who share a belief that the objective of behavioral economics is to enrich, rather than to destroy or replace, standard economics. They provide authoritative perspectives on the value to economic inquiry of insights gained from psychology. Specific chapters in this first volume cover reference-dependent preferences, asset markets, household finance, corporate finance, public economics, industrial organization, and structural behavioural economics. This Handbook provides authoritative summaries by experts in respective subfields regarding where behavioral economics has been; what it has so far accomplished; and its promise for the future. This taking-stock is just what Behavioral Economics needs at this stage of its so-far successful career. - Helps academic and non-academic economists understand recent, rapid changes in theoretical and empirical advances within behavioral economics - Designed for economists already convinced of the benefits of behavioral economics and mainstream economists who feel threatened by new developments in behavioral economics - Written for those who wish to become quickly acquainted with behavioral economics

This book presents the latest findings on network theory and agent-based modeling of economic and financial phenomena. In this context, the economy is depicted as a complex system consisting of heterogeneous agents that interact through evolving networks; the aggregate behavior of the economy arises out of billions of small-scale interactions that take place via countless economic agents. The book focuses on analytical modeling, and on the econometric and statistical analysis of the properties emerging from microscopic interactions. In particular, it highlights the latest empirical and theoretical advances, helping readers understand economic and financial networks, as well as new work on modeling behavior using rich, agent-based frameworks. Innovatively, the book combines observational and theoretical insights in the form of networks and agent-based models, both of which have proved to be extremely valuable in understanding non-linear and evolving complex systems. Given its scope, the book will capture the interest of graduate students and researchers from various disciplines (e.g. economics, computer science, physics, and applied mathematics) whose work involves the domain of complexity theory.

Silicon Valley is the most salient example of high-tech industrial clusters. Public policymakersthroughouttheworldwouldliketolearnthesecretsofSiliconValley in order to build their own high-tech economies. The existing literature on ind- trial clusters, which traces back to Marshall (1920), focuses on the way in which ?rms bene?t from locating in a cluster; it suggests that once a cluster comes into existence, it tends to reinforce itself by attracting more ?rms. However, a more important question is how to reach this critical mass in the ?rst place. In contrast to the literature, evidence suggests that entrepreneurs rarely move when they est- lish high-tech start-ups (Cooper and Folta, 2000). This contradicts the notion that location choice analyses lead entrepreneurs to a high-tech cluster. A high-tech industrial cluster such as Silicon Valley is characterized by c- centratedentrepreneurship. FollowingSchumpeter,weemphasizethefactthat“the appearance of one or a few entrepreneurs facilitates the appearance of others” (Schumpeter,1934). Weproposeanagent-basedcomputationalmodeltoshowhow high-tech industrial clusters could emerge in a landscape in which no ?rms existed originally. The model is essentially a spatial version of the Nelson-Winter model: Boundedly rational agents are scattered over an explicitly de?ned landscape. Each agent is endowed with some technology, which determines his ?rm’s productivity (if he has one). During each period of time, an agent with no ?rm would make a decision as to whether he wants to start one. This decision is mostly affected by the behavior of his social contacts, who are all his neighbors.

The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.