Algorithms and collusion – OECD Competition Division – June 2017 OECD discussion
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The document discusses the implications of algorithmic collusion in markets, highlighting how algorithms can facilitate anti-competitive behaviors without explicit communication between firms. It explores the need for competition law to adapt to these new challenges, including the use of monitoring, parallel, signaling, and self-learning algorithms that enhance collusion likelihood. The text also emphasizes the importance of regulatory measures to prevent algorithmic collusion and ensure transparency and accountability in algorithm design.
Algorithms and collusion – OECD Competition Division – June 2017 OECD discussion
- 1. Antonio Capobianco (antonio.capobianco@oecd.org) Pedro Gonzaga (pedro.gonzaga@oecd.org) Anita Nyesö (anita.nyeso@oecd.org) The opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views of the OECD or OECD member countries.
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- 3. • Roundtable on Collusion and Algorithms – Discussion by a panel of experts: • Ariel Ezrachi • Michal Gal • Avigdor Gal – Background note by the OECD Secretariat 3 www.oecd.org/competition/algorithms-and-collusion.htm Short URL: oe.cd/1-0
- 4. 1. Algorithms: concepts and applications 2. Risk of algorithmic collusion 3. Challenges for competition law enforcement 4. Regulation of algorithms
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- 6. 6 “An algorithm is an unambiguous, precise, list of simple operations applied mechanically and systematically to a set of tokens or objects. (…) The initial state of the tokens is the input; the final state is the output.” Wilson and Keil (1999) Instructions Manual • Start by doing this • Then of course do that. • Yeah, that sounds good.
- 7. • Plain language • Diagrams • Voice instructions • Computer codes – Automatic – Fast processing – Complex calculation 7 Digitalisation Adoption of computer algorithms Increased productivity
- 8. • Artificial intelligence – Detailed algorithms that mimic human intelligence • Machine learning – Algorithms that iteratively learn from data • Deep learning – Artificial neural networks that replicate the activity of human neurons… 8
- 9. 9 ML requires manual features engineering, while in DL feature engineering is automatic… Input Feature Extractor Features Traditional ML Algorithm Output Input Deep Learning Algorithm Output
- 10. 10 Business Consumers Government Predictive analytics Process optimisation Increase supplier power Consumer information Decision-making optimisation Increase buyer power Crime detection Determine fines and sentences Positive impact on static and dynamic efficiency
- 11. 11 Predictive analytics Optimisation of business processes Supply-chain optimisation Target ads Recommendations Product Customisation Dynamic pricing Price differentiation Fraud prevention Risk management Product innovation
- 12. 12 • Melanoma cancer detection • Detect cancerous cells in brain surgery • Dispersion of dust in drilling • Response of buildings to earthquakes • Prediction of traffic conditions • Buy and sell stocks • Predict corporate bankruptcy • Detect and classify deep-sea animals • Estimate concentrations of chlorophyll • Colorize black and white images • Add sound to silent movies
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- 14. 14 Algorithmic collusion consists in any form of anti- competitive agreement or coordination among competing firms that is facilitated or implemented through means of automated systems.
- 15. 15 Relevant factors for collusion How algorithms affect collusion? Structural characteristics Number of firms ± Barriers to entry ± Market transparency + Frequency of interaction + Demand variables Demand growth 0 Demand fluctuations 0 Supply variables Innovation – Cost asymmetry – + positive impact; – negative impact; 0 neutral impact; ± ambiguous impact.
- 16. 16 Result: In a perfectly transparent market where firms interact repeatedly, when the retaliation lag tends to zero collusion can always be sustained as an equilibrium strategy. Intuition: If markets are transparent and companies react instantaneously to any deviation, the payoff from deviation is zero. ICC: 𝑒−𝑟𝑡 𝜋 𝑀. 𝑑𝑡 ∞ 0 ≥ 𝑒−𝑟𝑡 𝜋 𝐷. 𝑑𝑡 𝑇+𝐿 0 + 𝑒−𝑟𝑡 𝜋 𝐶. 𝑑𝑡 ∞ 𝑇+𝐿
- 17. • However, even if collusion is an equilibrium strategy, firms may fail to coordinate… • Cartel umbrella effect: – In industries with many players, each firm has an incentive not to participate in the agreement in the first place in order to benefit from the “cartel umbrella”. 17 Structural measures might still be effective…
- 18. • Any collusive arrangement requires: – A meeting of minds – A structure to implement and govern firms’ interaction: • Common policy • Monitoring • Punishment mechanism 18
- 19. • Some algorithms may eliminate the need of explicit communication during the initiation and implementation stages: – Monitoring algorithms – Parallel algorithms – Signalling algorithms – Self-learning algorithms 19
- 20. 20 C: Monitoring algorithm Description : Collect and process information from competitors to monitor their compliance and, eventually, to punish deviations. Legend : /𝑝 <collusive price> /𝑝𝑖 <price set by firm i>
- 21. 21 C: Parallel algorithm Description : Coordinate a common policy or parallel behaviour, for instance by programming prices to follow a leader Legend : /𝑝 <collusive price> /𝑝𝑖 <price set by firm i>
- 22. 22 C: Signalling algorithm Description : to disclose and disseminate information in order to announce an intention to collude or negotiate a common policy Legend : /𝑠 <tentative signal> /𝑠𝑖 <signal sent by firm i>
- 23. 23 C: Self-learning algorithm Description : maximise profits while recognising mutual interdependency and readapting behaviour to the actions of other market players …
- 24. 24 Algorithms Change market characteristics Govern collusive structures Transparency High frequency trading Signal & negotiate common policy Coordinate common policy Monitor & punish Optimise joint profits Increase likelihood of collusion Replace explicit communication Tacit collusion
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- 26. • IF algorithms are a tool used to implement or amplify an illegal conduct: – Traditional antitrust tools apply • IF algorithms create new anti-competitive behaviours not covered by antitrust rules: – Can existing antitrust tools be adapted? – Should we reconsider fundamental antitrust concepts? 26
- 27. Market studies & investigations • Obtain empirical evidence of algorithmic collusion • Identify problematic markets and sectors • Define appropriate measures Ex-ante merger control • Reconsider the threshold of intervention • Evaluate the impact of transactions on market transparency and high frequency trading • Account for multi- market contacts in conglomerate mergers Commitments & remedies • Design remedies to prevent the use of algorithms as facilitating practices • Apply “notice-and- take-down” processes • Introduce auditing mechanisms for algorithms? 27
- 29. • Competition rules do not forbid collusive outcomes, but only the means to achieve collusion • Establishing an infringement of competition law requires: – Evidence of parallel conduct AND – “Plus factors” (communication, information exchanges, signalling…) 29 01100011 01101111 01101100 01101100 01110101 01110011 01101001 01101111 01101110 00100001
- 30. Should jurisdictions review their approach towards tacit collusion, by adjusting their criteria for an infringement? 30 % Example of law enforcement algorithm if {price correlation > 0.9999 and companies use: dynamic pricing algorithm or scraping algorithm or third party data centre or machine learning} conduct = infringement else conduct = no infringement end evidence of parallel conduct possible “plus factors”
- 31. • Identifying an “agreement” is a prerequisite to enforce the law against collusion • The concept of “agreement” is usually broadly defined, in order to ensure a wide reach of competition rules – In the EU the term “agreement” involves simultaneously: • A common will • Some form of manifestation – In the US is involves solely a common will or a “meeting of minds” 31
- 32. • In practice, existing concepts provide little guidance and courts rely on explicit communication to prove an agreement… • Should legislators create a more clear definition of agreement, in order to: – Reduce legal uncertainty – Account for more subtle behaviours, such as “meetings of algorithms”? 32
- 33. (…) computer technology that permits rapid announcements and responses has blurred the meaning of 'agreement' and has made it difficult for antitrust authorities to distinguish public agreements from conversations among competitors. Borenstein (1997) 33 time price
- 34. Offer Acceptance 1 Firm intermittently sets a higher price for brief seconds (costless signal) Competitor increases price to the value signalled 2 Firm programs algorithm to mimic the price of a leader The leader, recognising this behaviour, increases the price 3 Firm publicly releases a pricing algorithm Competitor downloads and executes the same pricing algorithm 4 Firm programs an anti-competitive price to be triggered whenever the competitor’s price is below a threshold Recognising the algorithm, the competitor always keeps the price above the threshold 5 Firm uses ML algorithm to maximise joint profits (for instance, by accounting for the spillovers on competitors’ profits) Competitor reacts with the same strategy 34 Can a “meeting of algorithms” amount to an anti-competitive agreement?
- 35. • Weak link between the agent (algorithm) and the principal (human being) • Defining a benchmark for illegality requires assessing whether any illegal action could have been anticipated or predetermined 35 The challenges that automated systems create are very real. (…) So as competition enforcers, we need to make it very clear that companies can’t escape responsibility for collusion by hiding behind a computer program. Margrethe Vestager (2017)
- 36. • Who is liable for the decisions and actions of algorithms? – Creators • Programmers • Third party centres – Users • Managers • Commercials – Benefiters • Shareholders • Managers 36 Creators Users Benefiters
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- 38. 38 • The use of automated computer systems to organise and select relevant information affects fundamental structures of the society… “(…) these days, a third of all marriages start on the Internet, so there are actually children alive today that wouldn’t have been born if not for machine learning.” Domingos (2017)
- 39. 39 • “Echo chambers” • Product recommendations • Content-control software • Feedback scores • Rankings of search engines’ results • Target ads • Price discrimination • Collection of data protected by IP rights
- 40. • Can the risks of algorithmic selection be eroded through “algorithmic competition”? 40 Imperfect Information Lack of algorithmic transparency Algorithms as trade secrets Complexity of program codes Barriers to entry Scale economies of IT infrastructures Scope economies of datasets Network economies in online platforms Spill-overs Nature of knowledge as a public good Spill-overs of a variety of information
- 41. 41 Algorithmic risks Market failures Regulatory intervention? Competitive Impact Can market regulation prevent algorithmic collusion?
- 42. 42 Market solutions Self- organisation Self- regulation Co- regulation State intervention WHO? Online companies operate at the interface of many laws enforced by different agencies: Privacy law Data protection Competition law Consumer protection Transparency law IPR
- 43. • New FTC Office of Technology Research and Investigation responsible for studying algorithmic transparency • EU Commissioner Vestager’s statement advocating for compliance by design with data protection and antitrust laws • German Chancellor Merkel’s public statement: 43 The algorithms must be made public, so that one can inform oneself as an interested citizen on questions like: what influences my behaviour on the internet and that of others? (…) These algorithms, when they are not transparent, can lead to a distortion of our perception, they narrow our breadth of information.
- 45. • Public disclosure of algorithms may reduce incentives for investment and innovation • Disclosing a complex program code may not suffice as a transparency measure • Transparency and accountability are challenging when decisions are taken autonomously by the algorithm • Enforcement cost of reviewing and supervising algorithms 45 Risk that algorithmic transparency facilitates further algorithmic collusion
- 46. • Extreme forms of algorithmic collusion enabled by deep learning may be hard to prevent through competition law • As a result, some regulatory interventions to prevent algorithmic collusion might be considered in the future: 46 Price regulation Market design Algorithm design Risk of competitive impact
- 47. 47 • Regulation objective: – To restrain the ability of firms to set high prices, regardless of whether they are achieved through explicit collusion, tacit coordination or other means • Barriers to competition: – Reduces incentives to innovate or to supply high-quality products – Creates a focal point for collusion – Creates barriers to new market entrants
- 48. 48 • Regulation objective: – To make digital markets less prone to collusion, by reducing transparency and frequency of interaction • Restrictions on information disclosures • Systems of secret discounts • Enforcement of time lags to implement price adjustments • Barriers to competition: – Limits the information available to consumers – Restricts the ability of firms to adjust strategies fast and efficiently
- 49. 49 • Regulation objective: – To enforce programmers to comply with competition principles in the design of algorithms: • Restrictions on the features / market information that the algorithm can use (e.g. recent price changes) • Restrictions on the objective function (e.g. joint profit maximisation) • Barriers to competition: – Limits the ability of firms to adjust strategies efficiently – Raises entry costs by forcing firms to comply by design