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A Dynamic Analysis of Interactive Rationality

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Epistemology, Knowledge and the Impact of Interaction

Part of the book series: Logic, Epistemology, and the Unity of Science ((LEUS,volume 38))

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Abstract

Epistemic game theory has shown the importance of informational contexts to understand strategic interaction. We propose a general framework to analyze how such contexts may arise. The idea is to view informational contexts as the fixed points of iterated, rational responses to incoming information about the agents’ possible choices. We discuss conditions under which such fixed points may exist. In the process, we generalize existing rules for information updates used in the dynamic epistemic logic literature. We then apply this framework to weak dominance. Our analysis provides a new perspective on a well known problem with the epistemic characterization of iterated removal of weakly dominated strategies.

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Notes

  1. 1.

    See Perea (2012), Dekel and Siniscalchi (2015), and Pacuit and Roy (2015) for surveys of this literature.

  2. 2.

    David Lewis already appreciated this general point about common knowledge when he first formulated his notion of common knowledge (Lewis 1969). See Cubitt and Sugden (2003) for an illuminating discussion and a reconstruction of Lewis’ notion of common knowledge, with applications to game theory.

  3. 3.

    See Pacuit (2015) for an extensive discussion of these different models of deliberation in games.

  4. 4.

    We assume that the reader is familiar with the basic concepts of game theory (e.g., strategic games and various solution concepts such as iterated removal of strictly/weakly dominated strategies). Consult Leyton-Brown and Shoham (2008) for an introduction to game theory.

  5. 5.

    Well-foundedness is only needed to ensure that for any set X, the set of minimal elements in X is nonempty. This is important only when W is infinite – and there are ways around this in current logics. Moreover, the condition of connectedness can also be lifted, but we use it here for convenience.

  6. 6.

    A similar idea is found in standard models of differential information from the economics literature. In such models, it is assumed that there is a prior probability measure describing the players’ initial beliefs (often it is the same probability measure for all the players). The players’ posterior probabilities are defined by conditioning their prior probability measure on their private information (typically represented by some partition over the set of states).

  7. 7.

    Consult any textbook on decision theory, such as Peterson (2009), for evidence of this fact.

  8. 8.

    See Roy et al. (2014) and Anglberger et al. (2015) for a discussion on the rational obligations and permissions in games.

  9. 9.

    Rabinovich takes this even further and argues that from the principle of indifference, players must assign equal probability to all choice-worthy options (Rabinowicz 1992).

  10. 10.

    So, we assume that the models agree about which outcomes of the game have not been ruled out.

  11. 11.

    An interesting extension would be to start with a multiagent belief model and allow players to incorporate information not only about which options are “choice-worthy”, but also about which beliefs their opponents may have. We leave this extension for future work and focus on setting up the basic framework here.

  12. 12.

    Here, it is crucial that the language \(\mathcal{L}_{G}\) does not contain any modalities.

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Pacuit, E., Roy, O. (2016). A Dynamic Analysis of Interactive Rationality. In: Redmond, J., Pombo Martins, O., Nepomuceno Fernández, Á. (eds) Epistemology, Knowledge and the Impact of Interaction. Logic, Epistemology, and the Unity of Science, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-26506-3_6

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