Summary
Summary Article Harrington et al., 2018
- Course
- Institution
Harrington, Joseph E. Jr. (2018), “Developing Competition Law for Collusion by Autonomous Artificial Agents,” Journal of Competition Law & Economics, 14(3), 331-363.
[Show more]
Seller
Follow
lstokroos
Content preview
SESSION 8: COLLUSIVE PRICING LIABILITY OF ARTIFICIALAGENTS
Introduction
• Consider two online market competitors offering identical products, with price
competition being intense
• Both firms adopt a software program using past prices, sales, cost of each
product, and time of year to set prices
• Each manager notices that profits are lower initially, but eventually prices rise
and profits increase
• Prices settle down and profits are higher than before adoption of learning
algorithms, with prices being supra-competitive
• Objective of paper is to explore whether collusion is unlawful and, if not, what
would be required to make it unlawful
• Recent speeches and papers have discussed the issue, but none offer a path to
making collusion by autonomous artificial agents unlawful
• Examines the regulation of artificial intelligence in broader set of situations, with
central issue being ensuring that AAs satisfy fairness
Collusion
Official Definition:
“Collusion is when firms use strategies that embody a reward–pun- ishment scheme
which rewards a firm for abiding by the supracompetitive outcome and punishes it for
departing from it.” à Collusion is a self-enforcing contract.
• Collusion is a mode of conduct among firms in a market that is independent of
the legal regime, and is rooted in the same simple foundation
• The objective of collusion is to raise prices to supracompetitive levels to earn
higher profits
• Collusion is not the same as charging supracompetitive prices, as it does not
require coordination with rival firms
• Firms must implement a reward–punishment scheme in order to get other firms
to charge supracompetitive prices
• Collusion is the common adoption of a strategy embodying a reward–punishment
scheme, and supracompetitive prices are the product of that adoption
• Collusion ties future rewards and punishments to current behavior, and is to be
thought of as a self-enforcing contract
When is Collusion Illegal?
• Collusion is not an illegal act, but the process of agreeing or conspiring to collude
is unlawful.
• The courts require evidence of an overt act of communication to determine if
firms have acted independently.
• An overt act of communication does not need to be an explicit invitation to
collude with a corresponding acceptance.
• Conscious parallelism is a legal process by which firms can share monopoly power
to set supracompetitive prices.
• It is difficult to distinguish collusive conduct from competitive conduct, leading to
some forms of collusion being legal.
• The court focuses its attention on observable communications that facilitate
collusion rather than collusion itself.
, Collusion by Artificial Agents
• Law and courts have viewed collusion through the perspective of conspiracy,
which involves company representatives with pricing authority communicating in
some manner to coordinate on charging higher prices.
• Autonomous artificial agents (AA) have two components: a pricing algorithm
which selects a price depending on the state of the AA and a learning algorithm
that modifies the pricing algorithm based on its performance.
• AAs can collude, but the question is whether they can learn to collude.
• Reinforcement learning has the general feature that an AA will continue to use a
pricing algorithm when it has performed well and experiment with other pricing
algorithms when it has not performed well.
• In seeking to find better pricing algorithms, there is a tension between
exploration and exploitation (or “learning and earning”).
• An example of a pricing algorithm is a reward–punishment scheme with a one-
period punishment, which will sustain supracompetitive prices.
• The particular form of reinforcement learning assumed is Q-learning, which has
an AA assign a perceived performance value to each state-action combination and
adjust them in response to realized performance.
• Simulations show that, with two AAs, two prices, and a fixed environment,
collusion is more common than competition
• AAs can adapt their ways to collusive prices
Jurisprudence to Collusion by Artificial Agents
• Overview of Jurisprudence Regarding Section 1 of the Sherman Act:
• Prohibits certain processes that could result in collusion, but not collusion itself
• Must be some overt act of communication to create or sustain mutual
understanding between firms to limit competition
• Firms that collude through Artificial Agents (AAs) are not guilty of a Sherman Act
Section 1 violation because the AAs only have access to information that would
be present under competition
• Evidentiary requirements could be adapted to handle collusion by AAs
• Companies are liable for their employees, but could they be liable for software
programs?
• Current jurisprudence suggests that someone working for the firm or something
owned by the firm needs to meet definition of liability for companies to be liable
• The Chinese Room Argument suggests that computers cannot understand and
thus lack mutual understanding to limit competition
• Even if computers had understanding, it is another step to reach a state of
mutual understanding
• US Department of Justice statement suggests that independent adoption of same
or similar pricing algorithms is unlikely to lead to antitrust liability
• Taking a contrary view suggests that algorithms can be programmed to comply
with the Sherman Act, but that requires coordination through legal conscious
parallelism
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller lstokroos. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.07. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
75759 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy study notes for 14 years now