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Notes
- 1.
- 2.
A multilayer neural net requires a non-linear activation function (such as a sigmoid). If the functions are linear, the multilayer net reduces to a single-layer I-O network.
- 3.
However, if an input node is wired to hidden nodes as well as output nodes, the error for this node cannot be updated until the errors for all hidden nodes that it influenced have been updated.
- 4.
The Cournot rule may be considered as a third degenerate model of belief learning. According to the Cournot rule, players assume that the behavior of the opponent in the previous round will always occur again in the present round.
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Further Reading
Further Reading
We refer readers interested in particular learning models and their application in agent-based simulation to (Macy and Flache 2002), which gives a brief introduction into principles of reinforcement learning and discusses by means of simulation models how reinforcement learning affects behavior in social dilemma situations, whereas (Macy 1996) compares two different approaches of modeling learning behavior by means of computer simulations. (Fudenberg and Levine 1998) gives a very good overview on how various learning rules relate to game theoretic rationality and equilibrium concepts.
For some wider background reading, we recommend (Macy 2004), which introduces the basic principles of learning theory applied to social behavior, (Holland et al. 1986), which presents a framework in terms of rule-based mental models for understanding inductive reasoning and learning, and Sun (2008), which is a handbook of computational cognitive modeling.
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Macy, M.W., Benard, S., Flache, A. (2013). Learning. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_17
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