Randomization and dynamic consistency

Eichberger, Jürgen; Grant, Simon; Kelsey, David

doi:10.1007/s00199-015-0913-8

Cited by 19 publications

(12 citation statements)

References 32 publications

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“…Eichberger, Grant, and Kelsey (2014) show that a dynamically consistent agent has no preference for randomization. Kuzmics (2013) shows that if an agent can randomize his choice in his mind, he can commit to his randomization, and if he believes that his randomization eliminates the effects of uncertainty, then he behaves as if uncertainty-neutral.…”

Section: Preview Of Resultsmentioning

confidence: 94%

Preferences for Flexibility and Randomization under Uncertainty

Saito

2015

American Economic Review

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An uncertainty-averse agent prefers betting on an event whose probability is known, to betting on an event whose probability is unknown. Such an agent may randomize his choices to eliminate the effects of uncertainty. For what sort of preferences does a randomization eliminate the effects of uncertainty? To answer this question, we investigate an agent's preferences over sets of acts. We axiomatize a utility function, through which we can identify the agent's subjective belief that a randomization eliminates the effects of uncertainty. (JEL D11, D81)

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Section: Preview Of Resultsmentioning

confidence: 94%

Preferences for Flexibility and Randomization under Uncertainty

Saito

2015

American Economic Review

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“…It assumes that precise probabilities are available and a strategy taking is accurate. However, in real world, it is not always the case, because, for example, some out‐of‐control factors could influence the precise of the probability as well as the accuracy of the strategy. Thus, our model handles imprecise probability of payoffs, but in this paper we have no concern with the imprecise probabilities over the types of players.…”

Section: Related Workmentioning

confidence: 99%

“…However, the meaning of fuzziness is different from that of ambiguity. In fact, for each possibility of a fuzzy payoff, it is assigned an accurate value in [0, 1], indicating a degree to which it is in the fuzzy payoff set; while the meaning of ambiguity is that we are uncertain about the probability of each possibility, but we know probabilities of some subsets of these possibilities . Also, Zhang et al .…”

Section: Related Workmentioning

confidence: 99%

“…In fact, for each possibility of a fuzzy payoff, it is assigned an accurate value in [0, 1], indicating a degree to which it is in the fuzzy payoff set; while the meaning of ambiguity is that we are uncertain about the probability of each possibility, but we know probabilities of some subsets of these possibilities. 29,31 Also, Zhang et al 52 study how to find the solution to a matrix game under fuzzy constraint. However, in their game model each payoff is still an accurate point value rather than any form of ambiguous payoff that we cope with in this paper.…”

Section: Related Workmentioning

confidence: 99%

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Matrix games with missing, interval, and ambiguous lottery payoffs of pure strategy profiles and compound strategy profiles

Luo

Jiang

2017

Int. J. Intell. Syst.

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In a matrix game, the interactions among players are based on the assumption that each player has accurate information about the payoffs of their interactions and the other players are rationally self‐interested. As a result, the players should definitely take Nash equilibrium strategies. However, in real‐life, when choosing their optimal strategies, sometimes the players have to face missing, imprecise (i.e., interval), ambiguous lottery payoffs of pure strategy profiles and even compound strategy profile, which means that it is hard to determine a Nash equilibrium. To address this issue, in this paper we introduce a new solution concept, called ambiguous Nash equilibrium, which extends the concept of Nash equilibrium to the one that can handle these types of ambiguous payoff. Moreover, we will reveal some properties of matrix games of this kind. In particular, we show that a Nash equilibrium is a special case of ambiguous Nash equilibrium if the players have accurate information of each player's payoff sets. Finally, we give an example to illustrate how our approach deals with real‐life game theory problems.

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“…A discussion of this issue has also lead to the preference model inSeo (2009). See alsoBattigalli, Cerreia-Vioglio, Maccheroni, and Marinacci (2017),Eichberger, Grant, and Kelsey (2016) andKuzmics (2017) for a discussion of this commitment issue.…”

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confidence: 99%

Limit Orders under Knightian Uncertainty

Greinecker¹,

Kuzmics

2020

SSRN Journal

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Investors who maximize subjective expected utility will generally trade in an asset unless the market price exactly equals the expected return, but few people participate in the stock market. [Dow and da Costa Werlang, Econometrica 1992] show that an ambiguity averse decision maker might abstain from trading in an asset for a wide interval of prices and use this fact to explain the lack of participation in the stock market. We show that when markets operate via limit orders, all investment behavior will be observationally equivalent to maximizing subjective expected utility; ambiguity aversion has no additional explanatory power. * Examples similar to our motivating example have been studied by Dominique Päper and Peter Habiger in their respective master theses written under the supervision of Christoph Kuzmics. We are grateful to both as well as Patrick Beissner, Frank Riedel, Jan Werner, and Michael Zierhut as well as seminar audiences at the

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Randomization and dynamic consistency

Cited by 19 publications

References 32 publications

Preferences for Flexibility and Randomization under Uncertainty

Preferences for Flexibility and Randomization under Uncertainty

Matrix games with missing, interval, and ambiguous lottery payoffs of pure strategy profiles and compound strategy profiles

Limit Orders under Knightian Uncertainty

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