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

This paper presents results showing an inverse relation between volatility and autocorrelation in an index of large firms. Evidence is given showing that index autocorrelation is also related to the level of trading volume. Tests are then performed on the individual fims that show much of this phenomenon is coming from cross firm correlations. Simple models with transactions costs are then simulated to see if they can replicate this phenomenon.

Transactions Costs and Correlations in a Large Firm Index

Models with small numbers of agents have recently been simplified for direct empirical estimation. Parameters are estimated at the macro level to get a best fit to the data. However, little analysis is done at the micro level to examine the choices made by agents for forecasting rules. This paper explores one of these recent models from the standpoint of micro agent behavior. It is shown that at the fitted forecasting rules, agents would prefer deviating to other nearby rules. The simple two type model is then compared with several multi-type models allowing for agents to use a broader set of rules. This can impact the dynamics of the generated time series, but it also may not if one takes the parameter estimates of the original model as an exogenous restriction on a reasonable support for the forecasting rules. This result emphasizes that these models may be imposing some hidden micro assumptions about agent behavior.

Microconsistency in Simple Empirical Agent-Based Financial Models

This paper considers the impact of heterogeneous gain learning in an asset pricing model. A relatively stylized model is shown to generate persistent swings of asset prices from their fundamental values which replicates long range samples of U.S financial data. The detailed mechanisms of the learning models are then explored. Evidence suggests that agents' perceptions of risk and its dynamics and persistence are important in generating appropriate price/fundamental dynamics. Agents putting large amounts of weight on the recent past in their volatility models control a large fraction of wealth, and are important in perpetuating the volatility magnifying dynamics of the market.

Heterogeneous Gain Learning and Long Swings in Asset Prices

This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests. Bootstrapping is used as a general framework for estimating objectives out of sample by redrawing subsets from a training sample. Evolution is used to search the large number of potential network architectures. The combination of these two methods creates a network estimation and selection procedure which nds parsimonious network structures which generalize well. The bootstrap methodology also allows for objective functions other than usual least squares, since it can estimate the in sample bias for any function. Examples are given for forecasting chaotic time series contaminated with noise.

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

An Evolutionary Bootstrap Approach to Neural Network Pruning and Generalization

Exchange rate theory: Chaotic models of foreign exchange markets: Paul DeGrauwe, Hans Dewachter, and Mark Embrechts, (Blackwells, Cambridge, 1993) pp. ix + 297, $37.50

Blake LeBaron

Papers

Transactions Costs and Correlations in a Large Firm Index

Microconsistency in Simple Empirical Agent-Based Financial Models

Heterogeneous Gain Learning and Long Swings in Asset Prices

An Evolutionary Bootstrap Approach to Neural Network Pruning and Generalization

Exchange rate theory: Chaotic models of foreign exchange markets: Paul DeGrauwe, Hans Dewachter, and Mark Embrechts, (Blackwells, Cambridge, 1993) pp. ix + 297, $37.50