This chapter surveys dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution. Key concepts include interacting agents, behavioral economics, evolutionary finance, complex adaptive systems, nonlinear dynamics, and numerical simulation. The chapter discusses fundamentalists and chartists, noise traders and behavioral finance, complex dynamics, interacting agents, heterogeneity and stylized facts, costly sophisticated versus cheap simple rules, and asset pricing models with heterogeneous beliefs. It highlights the importance of heterogeneous expectations in financial markets and the role of boundedly rational agents in explaining financial phenomena. The chapter also discusses the implications of these models for market efficiency, the behavior of rational and noise traders, and the potential for complex dynamics in financial markets. The models reviewed in this chapter may be viewed as simple, stylized versions of more complicated "artificial markets" and computationally oriented agent-based simulation models. The analysis of dynamic HAMs typically uses a mixture of analytic and computational tools.This chapter surveys dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution. Key concepts include interacting agents, behavioral economics, evolutionary finance, complex adaptive systems, nonlinear dynamics, and numerical simulation. The chapter discusses fundamentalists and chartists, noise traders and behavioral finance, complex dynamics, interacting agents, heterogeneity and stylized facts, costly sophisticated versus cheap simple rules, and asset pricing models with heterogeneous beliefs. It highlights the importance of heterogeneous expectations in financial markets and the role of boundedly rational agents in explaining financial phenomena. The chapter also discusses the implications of these models for market efficiency, the behavior of rational and noise traders, and the potential for complex dynamics in financial markets. The models reviewed in this chapter may be viewed as simple, stylized versions of more complicated "artificial markets" and computationally oriented agent-based simulation models. The analysis of dynamic HAMs typically uses a mixture of analytic and computational tools.