Abstract: Corruption frequently occurs in many aspects of multi-party interaction between private agencies and government employees. Past works studying corruption in a lab context have explicitly included covert or illegal activities in participants’ strategy space or have relied on surveys like the Corruption Perception Index (CPI). This paper studies corruption in ecologically realistic settings in which corruption is not suggested to the players a priori but evolves during repeated interaction. We ran studies involving hundreds of subjects in three countries: China, Israel, and the United States. Subjects interacted using a four-player board game in which three bidders compete to win contracts by submitting bids in repeated auctions, and a single auctioneer determines the winner of each auction. The winning bid was paid to an external “government” entity, and was not distributed among the players. The game logs were analyzed posthoc for cases in which the auctioneer was bribed to choose a bidder who did not submit the highest bid. We found that although China exhibited the highest corruption level of the three countries, there were surprisingly more cases of corruption in the U.S. than in Israel, despite the higher PCI in Israel as compared to the U.S. We also found that bribes in the U.S. were at times excessively high, resulting in bribing players not being able to complete their winning contracts. We were able to predict the occurrence of corruption in the game using machine learning. The significance of this work is in providing a novel paradigm for investigating covert activities in the lab without priming subjects, and it represents a first step in the design of intelligent agents for detecting and reducing corruption activities in such settings.

Citation: Ya’akov Gal, Avi Rosenfeld, Sarit Kraus, Michele Gelfand, Bo An and Jun Lin. A new paradigm for the study of corruption across cultures. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP), Maryland, MD, April 2014.

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This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper studies commitment strategies in three-player negotiation settings comprising human players and computer agents. We defined a new game called the Contract Game which is analogous to real-world market settings in which participants need to reach agreement over contracts in order to succeed. The game comprises three players, two service providers and one customer. The service providers compete to make repeated contract offers to the customer consisting of resource exchanges in the game. We formally analyzed the game and defined sub-game perfect equilibrium strategies for the customer and service providers that involve commitments. We conducted extensive empirical studies of these strategies in three different countries, the U.S., Israel and China. We ran several configurations in which two human participants played a single agent using the equilibrium strategies in various role configurations in the game (both customer and service providers). Our results showed that the computer agent using equilibrium strategies for the customer role was able to outperform people playing the same role in all three countries. In contrast, the computer agent playing the role of the service provider was not able to outperform people. Analysis reveals this difference in performance is due to the contracts proposed in equilibrium being significantly beneficial to the customer players, as well as irrational behavior taken by human customer players in the game.

Citation: Galit Haim, Kobi Gal, Bo An and Sarit Kraus. Equilibrium Strategies for Human-Computer Negotiation in 3-player market settings.

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