scholarly journals Simple bets to elicit private signals

2021 ◽  
Vol 16 (3) ◽  
pp. 777-797
Author(s):  
Aurélien Baillon ◽  
Yan Xu
Keyword(s):  
Common Prior ◽  
Private Signals ◽  
Risk Neutral ◽  
Microeconomic Foundations

This paper introduces two simple betting mechanisms—top‐flop and threshold betting—to elicit unverifiable information from crowds. Agents are offered bets on the rating of an item about which they received a private signal versus that of a random item. We characterize conditions for the chosen bet to reveal the agents' private signal even if the underlying ratings are biased. We further provide microeconomic foundations of the ratings, which are endogenously determined by the actions of other agents in a game setting. Our mechanisms relax standard assumptions of the literature, such as common prior, and homogeneous and risk neutral agents.

SEQUENTIAL CHOICE AND NON-BAYESIAN OBSERVATIONAL LEARNING

2009 ◽  
Vol 11 (03) ◽  
pp. 285-300
Author(s):  
THOMAS WISEMAN
Keyword(s):  
Observational Learning ◽  
Nearest Neighbor ◽  
Common Knowledge ◽  
Sequential Choice ◽  
Maximum Score ◽  
Common Prior ◽  
Private Signals ◽  
Score Method ◽  
Informational Cascade ◽  
Observable Actions

Standard models of observational learning in settings of sequential choice have two key features. The first is that players make decisions by using Bayes' rule to update their beliefs about payoffs from a common prior. The second is that each agent's decision rule is common knowledge, so that subsequent players can draw inferences about unobserved private signals from observable actions. In this paper, I relax the first assumption while maintaining the second. In particular, I look at observational learning by players who choose between two actions using nonparametric methods for estimating payoffs. When players are identical and make inferences using the maximum score method, an informational cascade and herd must result. If players of different payoff types use kernel or nearest-neighbor methods, there are cases in which a cascade need not arise. If one does occur, it must be one in which all players, regardless of type, choose the same action. In some situations, these alternative learning rules perform better than Bayesian updating.

Risk-Neutral and Physical Jumps in Option Pricing

2007 ◽  
Author(s):  
Jian Chen ◽  
Xiaoquan Liu ◽  
Chenghu Ma
Keyword(s):  
Option Pricing ◽  
Risk Neutral
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