Abstract
This paper analyzes repeated multimarket contact with observation errors where two players operate in multiple markets simultaneously. Multimarket contact has received much attention from the literature of economics, management, and information systems. Despite vast empirical studies that examine whether multimarket contact fosters cooperation/collusion, little is theo-retically known as to how players behave in an equilibrium when each player receives a noisy observation of other firms' actions. This paper tackles an essentially realistic situation where the players do not share common information; each player may observe a different signal (private monitoring). Thus, players have difficulty in having a common understanding about which market their opponent should be punished in and when punishment should be started and ended. We first theoretically show that an extension of 1-period mutual punishment (IMP) for an arbitrary number of markets can be an equilibrium. Second, by applying a verification method, we identify a simple equilibrium strategy called "locally cautioning (LC)" that restores collusion after observation error or deviation. We then numerically reveal that LC significantly outperforms IMP and achieves the highest degree of collusion.
| Original language | English |
|---|---|
| Title of host publication | Sequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report |
| Publisher | AI Access Foundation |
| Pages | 46-53 |
| Number of pages | 8 |
| Volume | FS-15-06 |
| ISBN (Electronic) | 9781577357520 |
| Publication status | Published - Jan 1 2015 |
| Event | AAAI 2015 Fall Symposium - Arlington, United States Duration: Nov 12 2015 → Nov 14 2015 |
Other
| Other | AAAI 2015 Fall Symposium |
|---|---|
| Country | United States |
| City | Arlington |
| Period | 11/12/15 → 11/14/15 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
How is cooperation/collusion sustained in repeated multimarket contact with observation errors? / Iwasaki, Atsushi; Sekiguchi, Tadashi; Yamamoto, Shun; Yokoo, Makoto.
Sequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report. Vol. FS-15-06 AI Access Foundation, 2015. p. 46-53.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - How is cooperation/collusion sustained in repeated multimarket contact with observation errors?
AU - Iwasaki, Atsushi
AU - Sekiguchi, Tadashi
AU - Yamamoto, Shun
AU - Yokoo, Makoto
PY - 2015/1/1
Y1 - 2015/1/1
N2 - This paper analyzes repeated multimarket contact with observation errors where two players operate in multiple markets simultaneously. Multimarket contact has received much attention from the literature of economics, management, and information systems. Despite vast empirical studies that examine whether multimarket contact fosters cooperation/collusion, little is theo-retically known as to how players behave in an equilibrium when each player receives a noisy observation of other firms' actions. This paper tackles an essentially realistic situation where the players do not share common information; each player may observe a different signal (private monitoring). Thus, players have difficulty in having a common understanding about which market their opponent should be punished in and when punishment should be started and ended. We first theoretically show that an extension of 1-period mutual punishment (IMP) for an arbitrary number of markets can be an equilibrium. Second, by applying a verification method, we identify a simple equilibrium strategy called "locally cautioning (LC)" that restores collusion after observation error or deviation. We then numerically reveal that LC significantly outperforms IMP and achieves the highest degree of collusion.
AB - This paper analyzes repeated multimarket contact with observation errors where two players operate in multiple markets simultaneously. Multimarket contact has received much attention from the literature of economics, management, and information systems. Despite vast empirical studies that examine whether multimarket contact fosters cooperation/collusion, little is theo-retically known as to how players behave in an equilibrium when each player receives a noisy observation of other firms' actions. This paper tackles an essentially realistic situation where the players do not share common information; each player may observe a different signal (private monitoring). Thus, players have difficulty in having a common understanding about which market their opponent should be punished in and when punishment should be started and ended. We first theoretically show that an extension of 1-period mutual punishment (IMP) for an arbitrary number of markets can be an equilibrium. Second, by applying a verification method, we identify a simple equilibrium strategy called "locally cautioning (LC)" that restores collusion after observation error or deviation. We then numerically reveal that LC significantly outperforms IMP and achieves the highest degree of collusion.
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UR - http://www.scopus.com/inward/citedby.url?scp=84964668144&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84964668144
VL - FS-15-06
SP - 46
EP - 53
BT - Sequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report
PB - AI Access Foundation
ER -