TY - GEN
T1 - Preventing arbitrage from collusion when eliciting probabilities
AU - Freeman, Rupert
AU - Pennock, David M.
AU - Peters, Dominik
AU - Waggoner, Bo
N1 - Funding Information:
Acknowledgments. Dominik Peters was supported by ERC under grant 639945 (ACCORD). Work conducted while Bo Wagonner was at Microsoft Research.
Funding Information:
Dominik Peters was supported by ERC under grant 639945 (ACCORD). Work conducted while Bo Wagonner was at Microsoft Research.
PY - 2020
Y1 - 2020
N2 - We consider the design of mechanisms to elicit probabilistic forecasts when agents are strategic and may collude with one another. Chun and Shachter (2011) have shown that when agents may form coalitions, many known mechanisms for elicitation permit arbitrage, allowing the coalition members to guarantee themselves higher payments by misreporting their beliefs. We consider two approaches to protect against colluding agents. First, we present a novel strictly proper mechanism that does not admit arbitrage provided that the reports of the agents are bounded away from 0 and 1, a common assumption in many settings. Second, we discover strictly arbitrage-free mechanisms that satisfy an intermediate guarantee between weak and strict properness.
AB - We consider the design of mechanisms to elicit probabilistic forecasts when agents are strategic and may collude with one another. Chun and Shachter (2011) have shown that when agents may form coalitions, many known mechanisms for elicitation permit arbitrage, allowing the coalition members to guarantee themselves higher payments by misreporting their beliefs. We consider two approaches to protect against colluding agents. First, we present a novel strictly proper mechanism that does not admit arbitrage provided that the reports of the agents are bounded away from 0 and 1, a common assumption in many settings. Second, we discover strictly arbitrage-free mechanisms that satisfy an intermediate guarantee between weak and strict properness.
UR - http://www.scopus.com/inward/record.url?scp=85099882891&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099882891&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85099882891
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 1958
EP - 1965
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI press
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -