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.

/pdf/early-warning-signals-for-critical-transitions-1qy2ayqlyk.pdf

Early-warning signals for critical transitions

Evolutionary metaphors have been prominent in both economics and nance. They are often used as basic foundations for rational behavior and ecient markets. Theoretically, a mechanism which selects for rational investors actually requires many caveats, and is far from generic. This paper tests wealth based evolution in a simple, stylized agent-based nancial market. The setup borrows extensively from

https://people.brandeis.edu/~blebaron/wps/wealthfcast.pdf

Wealth Evolution and Distorted Financial Forecasts

This paper explores the time series implications of introducing credit constraints into a production based asset pricing model. Simulations are performed choosing parameter values which generate reasonable values for aggregate fluctuations. These results show that mean reversion in simulated returns series, measured by variance ration tests, is enhanced with the introduction of binding credit constraints. Without these constraints there is very little evidence of mean reversion. This is consistent with financial market data where the weak evidence for mean reversion is stronger in small firm returns. Other tests are run on the simulated series including checking the standard deviation, skewness, and kurtosis. These other tests do not show strong differences between the constrained and unconstrained firms in the model.

Liquidity Constraints in Production-Based Asset-Pricing Models

A market of artificially intelligent traders is constructed to buy and sell a risky asset along with a risk free bond. Prices of the risky asset are determined endogenously from the interactions of the strategies which make trades and gather data. Each trader tries to learn about the world around it while enhancing its trading strategies. The primary purpose of this paper is to demonstrate that such a market replicates some of the basic empirical features of many asset markets including the persistence of volatility and trading volume, weak trends in prices, and leptokurtosis in returns. Also, for certain parameter values agents converge to a well defined rational expectations equilibrium.

Time Series Properties of an Artificial Stock Market

Evolutionary metaphors have been prominent in both economics and finance. They are often used as basic foundations for rational behavior and efficient markets. Theoretically, a mechanism which selects for rational investors requires many caveats, and is far from generic. This paper tests wealth-based evolution in a simple, stylized agent-based financial market. The setup borrows extensively from current research in finance that considers optimal behavior with some amount of return predictability. In the case of utility functions which differ from log, wealth selection alone converges to parameters which are economically far from the optimal forecast parameters. This serves as a strong reminder that wealth selection and utility maximization are not the same thing. Therefore, suboptimal financial forecasting strategies may be difficult to drive out of a market, and may even do quite well for some time.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1864784_code1108.pdf?abstractid=1864784&mirid=1

Wealth Dynamics and a Bias Toward Momentum Trading

This study discusses the potential applicability of Agent-based Computational Economics (ACE) for macroeconomic modeling, with a particular stress on the following three issues: (1) taxonomy - what types of agents for macroeconomic models?; (2) scale robustness - how many agents for macroeconomic models?; and (3) empirical validation - connecting to data. Annotated pointers to ACE macroeconomic research can be accessed here: http://www2.econ.iastate.edu/tesfatsi/amulmark.htm

Blake LeBaron

Papers

Wealth Evolution and Distorted Financial Forecasts

Liquidity Constraints in Production-Based Asset-Pricing Models

Time Series Properties of an Artificial Stock Market

Wealth Dynamics and a Bias Toward Momentum Trading

Modeling Macroeconomies As Open-Ended Dynamic Systems of Interacting Agents