Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors

Author(s):  
John R. Birge ◽  
Yifan Feng ◽  
N. Bora Keskin ◽  
Adam Schultz
2021 ◽  
Author(s):  
John R. Birge ◽  
Yifan Feng ◽  
N. Bora Keskin ◽  
Adam Schultz

How Bookies Can Outwit Sophisticated Bettors Sports-betting markets are based entirely on predictions. A bettor has to pick a winning contestant, and a market maker―a bookie―bets on the opponent. As bookies have to take the other side of every bet, it is of great value to understand the market making problem, that is, how to set the spread lines as “prices” for the bookies. Nevertheless, understanding of this problem is limited. Specifically, sophisticated bettors exist in the market, and a bookie can be manipulated by skillful bettors because of information asymmetry. In “Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors,” Birge, Feng, Keskin, and Schultz study the market-making problem under information asymmetry and market manipulation. They show that, although many popular learning and pricing algorithms, such as Bayesian policies, are effective in learning, they are vulnerable to strategic manipulations. The authors propose a dynamic learning and pricing algorithm, called the inertial policy, that collects information from the market effectively but also protects the bookie from strategic manipulations.


2018 ◽  
Author(s):  
John R. Birge ◽  
Yifan Feng ◽  
N. Bora Keskin ◽  
Adam Schultz

ASHA Leader ◽  
2009 ◽  
Vol 14 (5) ◽  
pp. 2-2
Author(s):  
Larry Boles ◽  
Amy J. Hadley ◽  
Jeanne M. Johnson ◽  
Joan A. Luckhurst ◽  
Christine Krkovich

2020 ◽  
Author(s):  
Amy K. Clark ◽  
Meagan Karvonen

Alternate assessments based on alternate achievement standards (AA-AAS) have historically lacked broad validity evidence and an overall evaluation of the extent to which evidence supports intended uses of results. An expanding body of validation literature, the funding of two AA-AAS consortia, and advances in computer-based assessment have supported improvements in AA-AAS validation. This paper describes the validation approach used with the Dynamic Learning Maps® alternate assessment system, including development of the theory of action, claims, and interpretive argument; examples of evidence collected; and evaluation of the evidence in light of the maturity of the assessment system. We focus especially on claims and sources of evidence unique to AA-AAS and especially the Dynamic Learning Maps system design. We synthesize the evidence to evaluate the degree to which it supports the intended uses of assessment results for the targeted population. Considerations are presented for subsequent data collection efforts.


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