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This report portrays the results of experimental research on dynamic duopoly markets with demand inertia. Two methods of experimentation are studied: game-playing experiments where subjects interact spontaneously via computer terminals, and computer tournaments between strategies designed by subjects. The principal aim of this study is the understanding of boundedly rational decision making in the dynamic duopoly situation. 1. 1 Motivation The experiments examine a multistage duopoly game where prices in each period are the only decision variables. Sales depend on current prices and also on past sales (demand inertia). Applying the game-theoretic concept of subgame perfect equilibrium, the game is solved by backward induction. The result is a uniquely determined system of decision rules. However, we can hardly expect that human beings behave according to the equilibrium strategy of this game. It is unlikely that subjects are able to compute the equilibrium. And even if a subject is able to compute it, he might not make use of this knowledge. Only if he expects the others to behave according to the equilibrium, it is optimal for him to play the equilibrium strategy. We have evidence from several earlier experimental studies on oligopoly markets that, even in less complex oligopoly situations where the equilibrium solutions are very easy to compute, human behavior often is different from what is prescribed by normative theory. ! Normative theory is based on the concept of ideal rationality. However, human capabilities impose cognitive limits on rationality.
The alternating double auction market institution is presented as a discrete time version of the open outcry market. The game in extensive form is analyzed in an almost perfect information setting, using the concept of subgame perfectness. By applying two new equilibrium selection criteria, a general existence result is obtained for "impatience equilibria" of the game. All such equilibria are shown to have unique properties concerning the traded quantities and prices. The most important results are that the equilibrium prices are independent of the number of traders and are always very close to - if not inside - the range of competitive prices. The latter can be evaluated as game theoretic support for the convergence of prices to the competitive price. The process of price formation is traced by applying the learning direction theory and introducing the "anchor price hypothesis".
Nonlinear Labor Market Dynamics discusses adjustment processes in labor markets. Contrary to linear-stochastic approaches this book is based on a non-linear deterministic framework. It is shown that even textbook-like-models of the labor market can generate long lasting adjustment processes, local instabilities, and chaotic movements, once nonlinear relationships and widely accepted adjustment rules are introduced. Thus, labor market dynamics may have an endogenous component that is governed by a nonlinear deterministic core. Of course, all results are tied to the particular models discussed in this book. Nevertheless, these models imply that by incorporating nonlinear relationships, one may arrive at an explanation of labor market behavior where linear stochastic approaches fell. Time series studies for German labor market data support this point of view.
This book is a collection of essays which examine how the properties of aggregate variables are influenced by the actions and interactions of heterogenous individuals in different economic contexts. The common denominator of the essays is a critique of the representative agent hypothesis. If this hypothesis were correct, the behaviour of the aggregate variable would simply be the reproduction of individual optimising behaviour. In the methodology of the hard sciences, one of the achievements of the quantum revolution has been the rebuttal of the notion that aggregate behaviour can be explained on the basis of the behaviour of a single unit: the elementary particle does not even exist as a single entity but as a network, a system of interacting units. In this book, new tracks in economics which parallel the developments in physics mentioned above are explored. The essays, in fact are contributions to the analysis of the economy as a complex evolving system of interacting agents.
In a complex and uncertain world, humans and animals make decisions under the constraints of limited knowledge, resources, and time. Yet models of rational decision making in economics, cognitive science, biology, and other fields largely ignore these real constraints and instead assume agents with perfect information and unlimited time. About forty years ago, Herbert Simon challenged this view with his notion of "bounded rationality." Today, bounded rationality has become a fashionable term used for disparate views of reasoning. This book promotes bounded rationality as the key to understanding how real people make decisions. Using the concept of an "adaptive toolbox," a repertoire of fast and frugal rules for decision making under uncertainty, it attempts to impose more order and coherence on the idea of bounded rationality. The contributors view bounded rationality neither as optimization under constraints nor as the study of people's reasoning fallacies. The strategies in the adaptive toolbox dispense with optimization and, for the most part, with calculations of probabilities and utilities. The book extends the concept of bounded rationality from cognitive tools to emotions; it analyzes social norms, imitation, and other cultural tools as rational strategies; and it shows how smart heuristics can exploit the structure of environments.
Proceedings of the NATO Advanced Study Institute, Stony Brook, New York, U.S.A., from 7 to 17 July 1999
The problem of efficient or optimal allocation of resources is a fundamental concern of economic analysis. This book provides surveys of significant results of the theory of optimal growth, as well as the techniques of dynamic optimization theory on which they are based. Armed with the results and methods of this theory, a researcher will be in an advantageous position to apply these versatile methods of analysis to new issues in the area of dynamic economics.
The Levels of Action
During the past two decades, the consideration of mUltiple objectives in modeling and decision making has grown by leaps and bounds. The nineties in particular have seen the emphasis shift from the dominance of single-objective modeling and optimization toward an emphasis on multiple objectives. The proceedings of this Conference epitomize these evolutionary changes and contribute to the important role that the tield of multiple criteria decision making (MCDM) now plays in planning, design, operational, management, and policy decisions. Of special interest are the contributions of MCDM to manufacturing engineering. For example, it has recently been recognized that optimal, single-objective solutions have often been pursued at the expense of the much broader applicability of designs and solutions that satisfy multiple objectives. In particular, the theme (MCDM and Its Worldwide Role in Risk-Based Decision Making) of the XIVth International Conference on Multiple Criteria Decision Making (Charlottesville, Virginia, USA, June 8-12, 1998) represents the growing importance of risk-cost-benefit analysis in decision making and in engineering design and manufacturing. In such systems, minimizing the of rare and extreme events emerges as an essential objective that risk complements the minimization of the traditional expected value of risk, along with the objectives attached to cost and performance. These proceedings include forty-five papers that were presented at the Conference. A variety of techniques have been proposed for solving multiple criteria decision-making problems. The emphasis and style of the different techniques largely reflect the fields of expertise of their developers.
This book deals with the omitted variable test for a multivariate time-series regression model. The empirical motivation is the homogeneity test for a consumer demand system. The consequences of using a dynamically misspecified omitted variable test are shown in detail. The analysis starts with the univariate t-test and is then extended to the multivariate regression system. The small sample performance of the dynamically correctly specified omitted variable test is analysed by simulation. Two classes of tests are considered: versions of the likelihood ratio test and the robust Wald test which is based on a heteroskedasticity and autocorrelation consistent variance-covariance estimator (HAC).