Ace Models - Chapter 1 Part 2.pdf
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The "agents" in ACE models can represent individuals (e.g. people), social groupings (e.g. firms), biological entities (e.g. growing crops), and/or physical systems (e.g. transport systems). The ACE modeler provides the initial configuration of a computational economic system comprising multiple interacting agents. The modeler then steps back to observe the development of the system over time without further intervention. In particular, system events should be driven by agent interactions without external imposition of equilibrium conditions.[20] Issues include those common to experimental economics in general[21] and development of a common framework for empirical validation [22] and resolving open questions in agent-based modeling.[23]
One area where ACE methodology has frequently been applied is asset pricing. W. Brian Arthur, Eric Baum, William Brock, Cars Hommes, and Blake LeBaron, among others, have developed computational models in which many agents choose from a set of possible forecasting strategies in order to predict stock prices, which affects their asset demands and thus affects stock prices. These models assume that agents are more likely to choose forecasting strategies which have recently been successful. The success of any strategy will depend on market conditions and also on the set of strategies that are currently being used. These models frequently find that large booms and busts in asset prices may occur as agents switch across forecasting strategies.[10][25][26] More recently, Brock, Hommes, and Wagener (2009) have used a model of this type to argue that the introduction of new hedging instruments may destabilize the market,[27] and some papers have suggested that ACE might be a useful methodology for understanding the 2008 financial crisis.[28][29][30]See also discussion under Financial economics § Financial markets and § Departures from rationality. 781b155fdc