This paper presents an online sponsored search auction that motivates advertisers to report their true budget, arrival time, departure time, and value per click. The auction is based on a modified Multi-Armed Bandit (MAB) mechanism that allows for advertisers who arrive and depart in an online fashion, have a value per click, and are budget constrained.
In tackling the problem of truthful budget, arrival and departure times, it turns out that it is not possible to achieve truthfulness in the classical sense (which we show in a companion paper). As such, we define a new concept called δ-gain. δ-gain bounds the utility a player can gain by lying as opposed to his utility when telling the truth. Building on the δ-gain concept we define another new concept called relative Ɛ-gain, which bounds the relative ratio of the gain a player can achieve by lying with respect to his true utility. We argue that for many practical applications if the δ-gain and or the relative Ɛ-gain are small, then players will not invest time and effort in making strategic choices but will truthtell as a default strategy. These concepts capture the essence of dominant strategy mechanisms as they lead the advertiser to choose truthtelling over other strategies.
In order to achieve δ-gain truthful mechanism this paper also presents a new payment scheme, Time series Truthful Payment Scheme (TTPS), for an online budget-constrained auction mechanism. The payment scheme is a generalization of the VCG principles for an online scheduling environment with budgeted players.
Using the concepts of δ-gain truthful we present the only known budget-constrained sponsored search auction with truthful guarantees on budget, arrivals, departures, and valuations. Previous works that deal with advertiser budgets only deal with the non-strategic case.
[1]
Ashish Goel,et al.
Truthful auctions for pricing search keywords
,
2006,
EC '06.
[2]
J. Bather,et al.
Multi‐Armed Bandit Allocation Indices
,
1990
.
[3]
Nicole Immorlica,et al.
Multi-unit auctions with budget-constrained bidders
,
2005,
EC '05.
[4]
Noam Nisan,et al.
Online ascending auctions for gradually expiring items
,
2005,
SODA '05.
[5]
Shie Mannor,et al.
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
,
2002,
COLT.
[6]
Aranyak Mehta,et al.
AdWords and generalized on-line matching
,
2005,
46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).
[7]
Rica Gonen,et al.
An incentive-compatible multi-armed bandit mechanism
,
2007,
PODC '07.
[8]
Sandeep Pandey,et al.
Handling Advertisements of Unknown Quality in Search Advertising
,
2006,
NIPS.
[9]
Donald A. Berry,et al.
Bandit Problems: Sequential Allocation of Experiments.
,
1986
.
[10]
Robert D. Kleinberg.
Anytime algorithms for multi-armed bandit problems
,
2006,
SODA '06.
[11]
Éva Tardos,et al.
An approximate truthful mechanism for combinatorial auctions with single parameter agents
,
2003,
SODA '03.
[12]
Aranyak Mehta,et al.
AdWords and Generalized On-line Matching
,
2005,
FOCS.
[13]
D. Bergemann,et al.
Learning and Strategic Pricing
,
1996
.
[14]
H. Robbins.
Some aspects of the sequential design of experiments
,
1952
.
[15]
Anne Lohrli.
Chapman and Hall
,
1985
.