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.

2

• 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

1. Algorithms: concepts and applications

2. Risk of algorithmic collusion

3. Challenges for competition law enforcement

4. Regulation of algorithms

5

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.

• Plain language

• Diagrams

• Voice instructions

• Computer codes

– Automatic

– Fast processing

– Complex calculation

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Digitalisation

Adoption of computer algorithms

Increased productivity

• 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…

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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

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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

Predictive analytics Optimisation of

business processes

Supply-chain

optimisation

Target ads

Recommendations Product

Customisation

Dynamic pricing

Price differentiation

Fraud prevention

Risk

management

Product innovation

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• 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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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.

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Relevant factors for collusionHow 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.

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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−𝑟𝑡𝜋𝑀. 𝑑𝑡∞

0≥ 𝑒−𝑟𝑡𝜋𝐷. 𝑑𝑡

𝑇+𝐿

0+ 𝑒−𝑟𝑡𝜋𝐶 . 𝑑𝑡

∞

𝑇+𝐿

• 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”.

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Structural measures might still be effective…

• Any collusive arrangement requires:

– A meeting of minds

– A structure to implement and govern firms’

interaction:

• Common policy

• Monitoring

• Punishment mechanism

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• Some algorithms may eliminate the need

of explicit communication during the

initiation and implementation stages:

– Monitoring algorithms

– Parallel algorithms

– Signalling algorithms

– Self-learning algorithms

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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>

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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>

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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

C:\ Self-learning algorithm

Description : maximise profits

while recognising mutual

interdependency and readapting

behaviour to the actions of other

market players

…

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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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• 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?

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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?

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Algorithms

Tacit collusion

Liability Agreement

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• 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…)

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01100011 01101111 01101100 01101100 01110101

01110011 01101001 01101111 01101110 00100001

Should jurisdictions review their approach towards tacit collusion, by

adjusting their criteria for an infringement?

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% 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”

• 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”

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• 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”?

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(…) 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)

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time

price

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

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Can a “meeting of algorithms” amount to an anti-competitive agreement?

• 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

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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)

• Who is liable for the decisions and actions of

algorithms?

– Creators

• Programmers

• Third party centres

– Users

• Managers

• Commercials

– Benefiters

• Shareholders

• Managers

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Creators

Users

Benefiters

37

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• 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)

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• “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

• Can the risks of algorithmic selection be eroded through

“algorithmic competition”?

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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

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Algorithmic risks

Market failures

Regulatory intervention?

Competitive

Impact

Can market regulation prevent algorithmic collusion?

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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

• 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:

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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.

Principles

Awareness

Access & redress

Account-ability

Explanation Data

provenance

Auditability

Validation & testing

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• 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

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Risk that algorithmic

transparency

facilitates further

algorithmic collusion

• 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:

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Price regulation

Market design

Algorithm design

Risk of competitive

impact

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

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• 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

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• 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