Bayesian Identification: A Theory for State-Dependent Utilities

Lu, Jay

doi:10.1257/aer.20171534

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…In particular, suppose that we can construct 0.2 0.4 0.6 0.8 1 an experiment that yields either signal s 0 (which is known to increase the probability of θ 0 ) or signal s 1 (which is known to increase the probability θ 1 ). Then, we would be asking the subject whether she prefers to be paid the good payoff x when s 0 is realized and the bad payoff x when s 1 is realized, or vice versa (similarly to Lu, 2019). However, this alternative mechanism would rely on two very strong assumptions.…”

Section: Identifying Deviations From Actual Beliefsmentioning

confidence: 99%

“…Drèze (1987) and Drèze and Rustichini (1999) allow for the agent to be able to influence the state realization, in different ways depending on the act she faces. Lu (2019) introduces stochastic choices under different information structures. For a more complete account of this literature, we refer to the reviews of Drèze and Rustichini (2004), Karni (2008), and more recently Baccelli (2017).…”

Section: Introductionmentioning

confidence: 99%

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Identification of misreported beliefs

Tsakas¹

2021

Preprint

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It is well-known that subjective beliefs cannot be identified with traditional choice data unless we impose the strong assumption that preferences are state-independent. This is seen as one of the biggest pitfalls of incentivized belief elicitation. The two common approaches are either to exogenously assume that preferences are state-independent, or to use intractable elicitation mechanisms that require an awful lot of hard-to-get non-traditional choice data. In this paper we use a third approach, introducing a novel methodology that retains the simplicity of standard elicitation mechanisms without imposing the awkward state-independence assumption. The cost is that instead of insisting on full identification of beliefs, we seek identification of misreporting. That is, we elicit beliefs with a standard simple elicitation mechanism, and then by means of a single additional observation we can tell whether the reported beliefs deviate from the actual beliefs, and if so, in which direction they do.

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Section: Identifying Deviations From Actual Beliefsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of misreported beliefs

Tsakas¹

2021

Preprint

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“…Lu's interesting recent work(Lu, 2016) would demand a specific discussion which I cannot provide here. Let me simply remark that Lu's approach crucially relies on uncertainty being progressively resolved.…”

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confidence: 92%

Moral Hazard, the Savage Framework, and State-Dependent Utility

Baccelli

2017

SSRN Journal

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In this paper, I investigate the betting behavior of a decision-maker who can influence the likelihood of the events upon which she is betting. In decision theory, this is best known as a situation of moral hazard. Focusing on a particularly simple case, I sketch the first systematic analysis of moral hazard in the canonical Savage framework. From the results of this analysis, I draw two philosophical conclusions. First, from an observational and a descriptive point of view, there need to be no incompatibility between moral hazard and the Savage framework. This qualifies the incompatibility view, that is ubiquitous in decision theory. Second, in general, moral hazard is not sufficient to overcome the challenges posed by state-dependent utility to the behavioral identification of beliefs. This qualifies the sufficiency view, that is influential in decision theory. These two philosophical conclusions are the main contributions of my paper.

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“…Since the celebrated work of Kamenica and Gentzkow (2011) who studied them within the context of a communication game, current work has seen their applications grow dramatically. This has included decision theoretic work on identifying information (as in Lu (2019)), as well as multi-agent and mechanism design settings where it is used to motivate the solution concept of Bayes Correlated Equilibrium (see Bergemann and Morris (2016) for more on this connection). This way of modelling information is clearly influential, and its usefulness has likely still not been exhausted despite extensive recent work.…”

Section: Introductionmentioning

confidence: 99%

Hypothetical Beliefs Identify Information

Libgober¹

2021

Preprint

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After observing the outcome of a Blackwell experiment, a Bayesian decisionmaker can form (a) posterior beliefs over the state, as well as (b) posterior beliefs she would observe any given signal (assuming an independent draw from the same experiment). I call the latter her contingent hypothetical beliefs. I show geometrically how contingent hypothetical beliefs relate to information structures. Specifically, the information structure can (generically) be derived by regressing contingent hypothetical beliefs on posterior beliefs over the state. Her prior is the unit eigenvector of a matrix determined from her posterior beliefs over the state and her contingent hypothetical beliefs. Thus, all aspects of a decisionmaker's information acquisition problem can be determined using ex-post data (i.e., beliefs after having received signals). I compare my results to similar ones obtained in cases where information is modeled deterministically; the focus on single-agent stochastic information distinguishes my work.

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Bayesian Identification: A Theory for State-Dependent Utilities

Cited by 8 publications

References 33 publications

Identification of misreported beliefs

Identification of misreported beliefs

Moral Hazard, the Savage Framework, and State-Dependent Utility

Hypothetical Beliefs Identify Information

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