Design of Incentive Compatible Mechanisms for Stackelberg Problems

2005 ◽  
pp. 718-727 ◽  
Author(s):  
Dinesh Garg ◽  
Yadati Narahari
Keyword(s):  
Incentive Compatible ◽  
Stackelberg Problems

scholarly journals Pareto Efficient Auctions with Interest Rates

2019 ◽  
Vol 33 ◽  
pp. 1989-1995
Author(s):  
Gagan Goel ◽  
Vahab Mirrokni ◽  
Renato Paes Leme
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Utility Functions ◽  
Limited Access ◽  
Incentive Compatible ◽  
Pareto Efficient ◽  
Available Resources

We consider auction settings in which agents have limited access to monetary resources but are able to make payments larger than their available resources by taking loans with a certain interest rate. This setting is a strict generalization of budget constrained utility functions (which corresponds to infinite interest rates). Our main result is an incentive compatible and Pareto-efficient auction for a divisible multi-unit setting with 2 players who are able to borrow money with the same interest rate. The auction is an ascending price clock auction that bears some similarities to the clinching auction but at the same time is a considerable departure from this framework: allocated goods can be de-allocated in future and given to other agents and prices for previously allocated goods can be raised.

EXPRESS: When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors

2021 ◽  
pp. 002224292199708
Author(s):  
Raji Srinivasan ◽  
Gülen Sarial-Abi
Keyword(s):  
Machine Learning ◽  
Theory Of Mind ◽  
Human Error ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Task Characteristics ◽  
Machine Learning Algorithm ◽  
Incentive Compatible ◽  
Mind Perception ◽  
Interactive Task

Algorithms increasingly used by brands sometimes fail to perform as expected or even worse, cause harm, causing brand harm crises. Unfortunately, algorithm failures are increasing in frequency. Yet, we know little about consumers’ responses to brands following such brand harm crises. Extending developments in the theory of mind perception, we hypothesize that following a brand harm crisis caused by an algorithm error (vs. human error), consumers will respond less negatively to the brand. We further hypothesize that consumers’ lower mind perception of agency of the algorithm (vs. human) for the error that lowers their perceptions of the algorithm’s responsibility for the harm caused by the error will mediate this relationship. We also hypothesize four moderators of this relationship: two algorithm characteristics, anthropomorphized algorithm and machine learning algorithm and two task characteristics where the algorithm is deployed, subjective (vs. objective) task and interactive (vs. non-interactive) task. We find support for the hypotheses in eight experimental studies including two incentive-compatible studies. We examine the effects of two managerial interventions to manage the aftermath of brand harm crises caused by algorithm errors. The research’s findings advance the literature on brand harm crises, algorithm usage, and algorithmic marketing and generate managerial guidelines to address the aftermath of such brand harm crises.

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