Book contents
- The Cambridge Handbook of Compliance
- The Cambridge Handbook of Compliance
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- 1 Introduction: Compliance as the Interaction between Rules and Behavior
- Part I Compliance Concepts and Approaches
- Part II Deterrence and Incapacitation
- Part III Incentives
- Part IV Legitimacy and Social Norms
- Part V Capacity and Opportunity
- Part VI Compliance and Cognition
- Part VII Management and Organizational Processes
- Part VIII Measuring and Evaluating Compliance
- Part IX Analysis of Particular Fields
- 55 Strengthening Tax Compliance by Balancing Authorities’ Power and Trustworthiness
- 56 Compliance in Occupational Safety and Health
- 57 Intellectual Property Compliance: Systematic Methods for Building and Using Intellectual Property
- 58 Insider Trading Compliance Programs
- 59 Antitrust Compliance: Collusion
- 60 Understanding AI Collusion and Compliance
- 61 HIPAA Compliance
- 62 Biopharmaceutical Compliance
- 63 Transnational Anti-Bribery Law
- 64 Data Security, Data Breaches, and Compliance
- 65 Doping in Sports: A Compliance Conundrum
- 66 Food Safety Compliance
- 67 Global Supply Chain Auditing
- 68 Corporations, Human Rights and Compliance
- 69 Aiming for Integrity with Integrity
- References
60 - Understanding AI Collusion and Compliance
from Part IX - Analysis of Particular Fields
Published online by Cambridge University Press: 07 May 2021
Book contents
- The Cambridge Handbook of Compliance
- The Cambridge Handbook of Compliance
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- 1 Introduction: Compliance as the Interaction between Rules and Behavior
- Part I Compliance Concepts and Approaches
- Part II Deterrence and Incapacitation
- Part III Incentives
- Part IV Legitimacy and Social Norms
- Part V Capacity and Opportunity
- Part VI Compliance and Cognition
- Part VII Management and Organizational Processes
- Part VIII Measuring and Evaluating Compliance
- Part IX Analysis of Particular Fields
- 55 Strengthening Tax Compliance by Balancing Authorities’ Power and Trustworthiness
- 56 Compliance in Occupational Safety and Health
- 57 Intellectual Property Compliance: Systematic Methods for Building and Using Intellectual Property
- 58 Insider Trading Compliance Programs
- 59 Antitrust Compliance: Collusion
- 60 Understanding AI Collusion and Compliance
- 61 HIPAA Compliance
- 62 Biopharmaceutical Compliance
- 63 Transnational Anti-Bribery Law
- 64 Data Security, Data Breaches, and Compliance
- 65 Doping in Sports: A Compliance Conundrum
- 66 Food Safety Compliance
- 67 Global Supply Chain Auditing
- 68 Corporations, Human Rights and Compliance
- 69 Aiming for Integrity with Integrity
- References
Summary
Abstract: Antitrust compliance scholarship, particularly with a focus on collusion, has been an area of study for some time (; ; ; ). Changes in technology and the rise of artificial intelligence (AI) and machine-learning have created new possibilities both for anticompetitive behavior and for detection of algorithmic collusion. To some extent, AI collusion takes traditional ideas of collusion and simply provides a technological overlay to them. However, in some instances, the mechanisms of both collusion and detection can be transformed using AI. This chapter discusses existing theoretical and empirical work, and identifies research gaps as well as avenues for new scholarship on how firms or competition authorities might invest in AI compliance to improve detection of wrongdoing. We suggest where AI collusion is possible and offer new twists to where prior work has not identified possible collusion. Specifically, we identify the importance of AI in addressing the “trust” issue in collusion. We also identify that AI collusion is possible across nonprice dimensions, such as manipulated product reviews and ratings, and discuss potential screens involving co-movements of prices and ratings. We further emphasize that AI may encourage entry, which may limit collusive prospects. Finally, we discuss how AI can be used to help with compliance both at the firm level and by competition authorities.
- Type
- Chapter
- Information
- The Cambridge Handbook of Compliance , pp. 881 - 894Publisher: Cambridge University PressPrint publication year: 2021
References
Abrantes-Metz, R., Froeb, L., Geweke, J., and Taylor, C.. 2006. “A Variance Screen for Collusion.” International Journal of Industrial Organization 24, no. 3 (May): 467–86.CrossRefGoogle Scholar
Abrantes-Metz, Rosa, Kraten, Michael, Metz, Albert, and Seow, Gim. 2012. “Libor Manipulation?” Journal of Banking and Finance 36, No. 1 (January): 136–50.CrossRefGoogle Scholar
Anderson, E. T., and Simester, D. I.. 2014. “Reviews without a Purchase: Low Ratings, Loyal Customers, and Deception.” Journal of Marketing Research 51, No. 3 (June): 249–69.CrossRefGoogle Scholar
Bajari, P., and Ye, L.. 2003. “Deciding between Competition and Collusion.” Review of Economics and Statistics 85, no. 4 (November): 971–89.Google Scholar
Beckenstein, A. R., and Gabel, H. L.. 1986. “The Economics of Antitrust Compliance.” Southern Economics Journal 52, no. 3 (January): 673–92.Google Scholar
Brown, Z. Y., and MacKay, A.. 2019. “Competition in Pricing Algorithms.” Working Paper.CrossRefGoogle Scholar
Calvano, E., Calzolari, G., Denicolò, V., and Pastorello, S.. 2020. “Artificial Intelligence, Algorithmic Pricing and Collusion.” American Economic Review 110, no. 10: 3267–97.Google Scholar
Crandall, J. W., Oudah, M., Tennom, F. Ishowo-Oloko, S. Abdallah, J. F. Bonnefon, M. Cebrian, A. Shariff, M. A. Goodrich, , and Rahwan, I.. 2018. “Cooperating with Machines.” Nature Communications 9: 233.CrossRefGoogle ScholarPubMed
Dong, C., Wang, Y., Aldweesh, A., McCorry, P., and van Moorsel, A.. 2017. “Betrayal, Distrust, and Rationality: Smart Counter-Collusion Contracts for Verifiable Cloud Computing.” Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, 30 October–3 November.Google Scholar
Ezrachi, A., and Stucke, M. E.. 2017. “Artificial Intelligence and Collusion: When Computers Inhibit Competition.” Illinois Law Review 2017: 1785–1810.Google Scholar
Genesove, D., and Mullin, W. P.. 2001. “Rules, Communication, and Collusion: Narrative Evidence from the Sugar Institute Case.” American Economic Review 91, No. 3: 379–98.CrossRefGoogle Scholar
Green, E., and Porter, R.. 1984. “Noncooperative Collusion under Imperfect Price Information.” Econometrica 52, No. 1: 87–100.Google Scholar
Greif, A. 2006. Institutions and the Path to the Modern Economy: Lessons from Medieval Trade. New York: Cambridge University Press.CrossRefGoogle Scholar
Harrington, J. 2007. “Behavioral Screening and the Detection of Cartels.” In European Competition Law Annual 2006: Enforcement of Prohibition of Cartels, eds. Ehlermann, Claus-Dieter and Atanasiu, Isabela, 51–68. Oxford: Hart Publishing.Google Scholar
Harrington, J. 2018. “Antitrust in High Speed: Colluding through Algorithms and Other Technologies.” New York, 8 January. https://joeharrington5201922.github.io/pdf/Antitrust%20in%20High%20Speed_Remarks_Harrington.pdf.Google Scholar
Harrington, J., and Chen, J.. 2006. “Cartel Pricing Dynamics with Cost Variability and Endogenous Buyer Detection.” International Journal of Industrial Organization 24, No. 6: 347.CrossRefGoogle Scholar
Hendricks, K., McAfee, R. P., and Williams, M.. 2015. “Auctions and Bid Rigging.” In Oxford Handbook on International Antitrust Economics, Vol. 2, eds. Blair, Roger D. and Sokol, D. Daniel, 498–522. New York: Oxford University Press.Google Scholar
Hylton, K. N. 2015. “Deterrence and Antitrust Punishment: Firms versus Agents.” Iowa Law Review 100: 2069–83.Google Scholar
Ishii, R. 2009. “Favor Exchange in Collusion: Empirical Study of Repeated Procurement Auctions in Japan.” International Journal of Industrial Organization 27: 137–44.CrossRefGoogle Scholar
Ittoo, A., and Petit, N.. 2017. “Algorithmic Pricing Agents and Tacit Collusion: A Technological Perspective.” Working Paper.Google Scholar
Jiménez, J. L., and Perdiguero, J.. 2012. “Does Rigidity Hide Collusion?” Review of Industrial Organization 41, no. 3: 223–48.Google Scholar
Lappas, T., Sabnis, G., and Valkanas, G.. 2016. “The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry.” Information Systems Research 27, No. 4: 940–61.Google Scholar
Levenstein, M. 1997. “Price Wars and the Stability of Collusion: A Study of the Pre-World War I Bromine Industry.” Journal of Industrial Economics 45, No. 2: 117–38.Google Scholar
Kaplow, L. 2013. Competition Policy and Price Fixing. Princeton, NJ: Princeton University Press.Google Scholar
Klein, T. 2019. “Autonomous Algorithmic Collusion: Q-Learning under Sequential Pricing.” Working Paper.CrossRefGoogle Scholar
Kühn, K. U., and Tadelis, S.. 2018. “The Economics of Algorithmic Pricing: Is Collusion Really Inevitable?” Working Paper.Google Scholar
Luca, M., and Zervas, G.. 2016. “Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud.” Management Science 62, No. 12: 3412–27.Google Scholar
Macaulay, S. 1963. “Non-contractual Relations in Business: A Preliminary Study.” American Sociological Review 28, No. 1: 55–67.Google Scholar
Maskin, E., and Tirole, J.. 1988. “A Theory of Dynamic Oligopoly, II: Price Competition, Kinked Demand Curves, and Edgeworth Cycles.” Econometrica 56, No. 3: 571–99.CrossRefGoogle Scholar
Mayzlin, D., Dover, Y., and Chevalier, J.. 2014. “Promotional Reviews: An Empirical Investigation of Online Review Manipulation.” American Economic Review 104, No. 8: 2421–55.Google Scholar
Mehra, S. K. 2016. “Antitrust and the Robo-Seller: Competition in the Time of Algorithms.” Minnesota Law Review 100: 1323–75.Google Scholar
Miklos-Thal, J., and Tucker, C.. 2019. “Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination between Sellers?” Management Science 65, no. 4: 1552–61.Google Scholar
Milgrom, P., and Roberts, J.. 1990. “Rationalizability, Learning, and Equilibrium in Games with Stochastic Complementarities.” Econometrica 58, No. 6: 1255–77.Google Scholar
Mukherjee, A. B., Wang, Liu J., Glance, N., and Jindal, N.. 2011. “Detecting Group Review Spam.” ACM Proceedings of the 20th International Conference Companion on World Wide Web: 93–4.CrossRefGoogle Scholar
Porter, R., and Zona, J.. 1999. “Ohio School Milk Markets: An Analysis of Bidding.” RAND Journal of Economics 30: 263–88.CrossRefGoogle Scholar
Riley, A., and Sokol, D. D.. 2015. “Rethinking Compliance.” Journal of Antitrust Enforcement 3: 31–57.Google Scholar
Rotemberg, J., and Saloner, G.. 1986. “A Supergame-Theoretic Model of Price Wars during Booms.” American Economic Review 76, No. 3: 390–407.Google Scholar
Schinkel, M. P., and Spiegel, Y.. 2017. “Can Collusion Promote Sustainable Consumption and Production?” International Journal of Industrial Organization 53: 371–98.Google Scholar
Sokol, D. D. 2017. “Antitrust Compliance.” In The Oxford Handbook of Strategy Implementation, eds. Hitt, Michael A., Jackson, Susan E., Carmona, Salvador, Bierman, Leonard, Shalley, Christina E., and Wright, Mike, 155–76. New York: Oxford University Press.Google Scholar
Tesauro, G., and Kephart, J. O.. 2002. “Pricing in Economies Using Multi-agent Q-learning.” Autonomous Agents and Multi-agent Systems 5: 289–304.CrossRefGoogle Scholar
Tirole, J. 1996. “A Theory of Collective Reputations (with Applications to the Persistence of Corruption and to Firm Quality).” Review of Economic Studies 63, No. 1: 1–22.Google Scholar
Waltman, L., and Kaymak, U.. 2008. “Q-learning Agents in a Cournot Oligopoly Model.” Journal of Economic Dynamics and Control 32: 3275–93.CrossRefGoogle Scholar
Wilson, N., and O’Connor, J.. 2019. “Reduced Demand Uncertainty and the Sustainability of Collusion: How AI Could Affect Competition.” Working Paper.CrossRefGoogle Scholar
Williamson, Oliver. 1983. “Credible Commitments: Using Hostages to Support Exchange.” American Economic Review 73, No. 4: 519–40.Google Scholar
- 2
- Cited by