Abstract
Thought experiments are widely employed in the social sciences, as many experiments are not affordable or even impossible to execute. Informal thought experiments, which are typical of classical economics, involve vagueness in the inference from their premises. On the other hand, mathematical models lack realism in their assumptions. Agent-based models are a particular kind of thought experiments, which are especially useful when our intuition is dealing with complex phenomena. We will argue that, contrary to mathematical models, agent-based models allow more realistic assumptions. On the other hand, unlike informal thought experiments, the conclusion of a simulation is the result of a strictly deductive procedure. Hence, agent-based models improve the realism of the assumptions of mathematical models, while avoiding the vagueness of informal thought experiments.
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Notes
- 1.
For a taxonomy of thought experiments, see, for example, Brown and Fehige [2].
- 2.
Interestingly enough, despite their gnoseological commitments, empiricists like Locke and Hume made large use of thought experiments. Think, for example, of Hume’s [6] “missing shade of blue”.
- 3.
For a debate on Friedman’s account, see Mäki [11].
- 4.
For an introduction to agent-base models, see, for example, Tesfatsion [16].
- 5.
For example, this is the position advocated by Epstein [3].
- 6.
A classic study of such adequacy conditions is Ullman-Margalit [17].
- 7.
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Rangoni, R. (2014). From Informal Thought Experiments to Agent-Based Models A Progressive Account of Modeling in the Social Sciences. In: Magnani, L. (eds) Model-Based Reasoning in Science and Technology. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37428-9_26
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