Learning and self-confirming long-run biases

Battigalli, Pierpaolo; Francetich, Alejandro; Lanzani, Giacomo; Marinacci, Mássimo

doi:10.1016/j.jet.2019.07.009

Cited by 25 publications

(10 citation statements)

References 37 publications

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“…While plain SCE can be justi…ed as the set of rest points of learning dynamics (e.g., Battigalli et al 1992, Battigalli et al 2016c and references therein), we are not aware of any learning foundation for rationalizable SCE. As we think about one, the very de…nition of rationalizable SCE appears questionable.…”

Section: Rationalizable Selfcon…rming Equilibriummentioning

confidence: 99%

Ambiguity attitudes and self-confirming equilibrium in sequential games

Battigalli

Catonini

Lanzani

et al. 2019

Games and Economic Behavior

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Section: Rationalizable Selfcon…rming Equilibriummentioning

confidence: 99%

Ambiguity attitudes and self-confirming equilibrium in sequential games

Battigalli

Catonini

Lanzani

et al. 2019

Games and Economic Behavior

Self Cite

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“…After actions are chosen, given the feedback received, agents update their conjectures. If conjectures are confirmed then an agent keeps past conjecture, otherwise she updates using as new conjecture the conjecture that would 11 See, for example, Battigalli et al (1992), Battigalli and Marinacci (2016), Fudenberg and Kreps (1995), and the references therein.…”

Section: Learning Processmentioning

confidence: 99%

Learning and Selfconfirming Equilibria in Network Games

Battigalli¹,

Panebianco²,

Pin³

2018

Preprint

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Consider a set of agents who play a network game repeatedly. Agents may not know the network. They may even be unaware that they are interacting with other agents in a network. Possibly, they just understand that their payoffs depend on an unknown state that in reality is an aggregate of the actions of their neighbors. Each time, every agent chooses an action that maximizes her subjective expected payoff and then updates her beliefs according to what she observes. In particular, we assume that each agent only observes her realized payoff. A steady state of such dynamic is a selfconfirming equilibrium given the assumed feedback. We characterize the structure of the set of selfconfirming equilibria in network games and we relate selfconfirming and Nash equilibria. Thus, we provide conditions on the network under which the Nash equilibrium concept has a learning foundation, despite the fact that agents may have incomplete information. In particular, we show that the choice of being active or inactive in a network is crucial to determine whether agents can make correct inferences about the payoff state and hence play the best reply to the truth in a selfconfirming equilibrium. We also study learning dynamics and show how agents can get stuck in non-Nash selfconfirming equilibria. In such dynamics, the set of inactive agents can only increase in time, because once an agent finds it optimal to be inactive, she gets no feedback about the payoff state, hence she does not change her beliefs and remains inactive.

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“…In learning by doing processes, two properties often cooccur. First, information about the environment is restricted to that resulting from actions taken by the agent, socalled "own-action dependence" [5] or endogenous sampling [6]. In such situations, information that corresponds to unchosen actions is not available to the agent.…”

Section: Introductionmentioning

confidence: 99%

Self-Confirming Biased Beliefs in Organizational “Learning by Doing”

Park

Puranam

2021

Complexity

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Learning by doing, a change in beliefs (and consequently behaviour) due to experience, is crucial to the adaptive behaviours of organizations as well as the individuals that inhabit them. In this review paper, we summarise different pathologies of learning noted in past literature using a common underlying mechanism based on self-confirming biased beliefs. These are inaccurate beliefs about the environment that are self-confirming because acting upon these beliefs prevents their falsification. We provide a formal definition for self-confirming biased beliefs as an attractor that can lock learning by doing systems into suboptimal actions and provide illustrations based on simulations. We then compare and distinguish self-confirming biased beliefs from other related theoretical constructs, including confirmation bias, self-fulfilling prophecies, and sticking points, and underscore that self-confirming biased beliefs underlie inefficient self-confirming equilibria and hot-stove effects. Lastly, we highlight two fundamental ways to escape self-confirming biased beliefs: taking actions inconsistent with beliefs (i.e., exploration) and getting information on unchosen actions (i.e., counterfactuals).

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Learning and self-confirming long-run biases

Cited by 25 publications

References 37 publications

Ambiguity attitudes and self-confirming equilibrium in sequential games

Ambiguity attitudes and self-confirming equilibrium in sequential games

Learning and Selfconfirming Equilibria in Network Games

Self-Confirming Biased Beliefs in Organizational “Learning by Doing”

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