Antonio Capobianco ([email protected])
Pedro Gonzaga ([email protected])
Anita Nyesö ([email protected])
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.
• 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
7
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…
8
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
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
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
13
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
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.
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−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”.
17
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
18
• 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
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
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
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
…
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
25
• 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
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
• 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
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”
• 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
• 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
(…) 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
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?
• 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)
• 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
37
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
• “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”?
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
Algorithmic risks
Market failures
Regulatory intervention?
Competitive
Impact
Can market regulation prevent algorithmic collusion?
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
• 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.
Principles
Awareness
Access & redress
Account-ability
Explanation Data
provenance
Auditability
Validation & testing
44
• 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
• 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
• 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
• 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
• 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
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