Fast and frugal heuristics - simple rules for making decisions with realistic mental resources - are presented here. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? What heuristics are in the mind's adaptive toolbox, and what building blocks compose them? Can judgments based simply on a single reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? This book explores these questions by developing computational models of heuristics and testing them through experiments and analysis. It shows how fast and frugal heuristics can yield adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop-out rates, and playing the stock market.

Simple Heuristics That Make Us Smart

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

ARCH modeling in finance: A review of the theory and empirical evidence

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.

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Agent-based modeling: Methods and techniques for simulating human systems

Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

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Early-warning signals for critical transitions

This paper introduces an order-driven market with heterogeneous investors, who submit limit or market orders according to their own trading rules. The trading rules are repeatedly updated via simple learning and adaptation of the investors. We analyze markets with and without learning and adaptation. The simulation results show that our model with learning and adaptation successfully replicates long-memories in trading volume, stock return volatility, and signs of market orders. We also discuss why evolutionary dynamics are important in generating these long memory features.

Long-Memory in an Order-Driven Market

This source code implements the BDS test statistic for independence of a time-series that was proposed by W. A. Brock, W. D. Dechert and J. A. Scheinkman and that is described both in the book "Nonlinear Dynamics, Chaos and Instability" by W. A. Brock, D. A. Hsieh and B. LeBaron (MIT Press, 1991) and the article "A Test for Independence Based on the Correlation Dimension" by W. A. Brock, W. D. Dechert, J. A. Scheinkman and B. LeBaron in Econometric Review, 15(3), 1996.

C Source for BDS Test Statistic for Independence

Heterogeneous agent models for financial markets have provided explanations for many empirical regularities of relatively high frequency (hourly/daily) financial time series. They have been much quieter when it comes to longer range features. This paper examines a simplified computational heterogeneous agent model in the context of various longer range time series properties for equity returns. The model is compared to a specially created long range data set, and is found to perform well in terms of replicating features, and even revealing some aspects of the data that have not been well quantified to date. By matching empirical properties at both short and long horizons this sets a higher standard in terms of validation which this model is able to match.

Heterogeneous Agents and Long Horizon Features of Asset Prices

Physics of the money markets

This article explores the relation between serial correlation and volatility for several different stock return series at daily and weekly frequencies. It is found that serial correlations are changing over time and are related to stock return volatility. An extension to the GARCH model is proposed and estimated, revealing parameters consistent with other findings in this article. Copyright 1992 by University of Chicago Press.(This abstract was borrowed from another version of this item.)

Blake LeBaron

Papers

Long-Memory in an Order-Driven Market

C Source for BDS Test Statistic for Independence

Heterogeneous Agents and Long Horizon Features of Asset Prices

Physics of the money markets

Some relations between volatility and serial correlations in stock market returns