Artificial Intelligence and Collusion: A Literature Overview

Van Uytsel, Steven

doi:10.1007/978-981-13-2874-9_7

Steven Van Uytsel⁸

Part of the book series: Perspectives in Law, Business and Innovation ((PLBI))

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12 Citations
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Abstract

The use of algorithms in pricing strategies has received special attention among competition law scholars. There is an increasing number of scholars who argue that the pricing algorithms, facilitated by increased access to Big Data , could move in the direction of collusive price setting. Though this claim is being made, there are various responses. On the one hand, scholars point out that current artificial intelligence is not yet well-developed to trigger that result. On the other hand, scholars argue that algorithms may have other pricing results rather than collusion. Despite the uncertainty that collusive price could be the result of the use of pricing algorithms, a plethora of scholars are developing views on how to deal with collusive price setting caused by algorithms. The most obvious choice is to work with the legal instruments currently available. Beyond this choice, scholars also suggest constructing a new rule of reason . This rule would allow us to judge whether an algorithm could be used or not. Other scholars focus on developing a test environment. Still other scholars seek solutions outside competition law and elaborate on how privacy regulation or transparency reducing regulation could counteract a collusive outcome. Besides looking at law , there are also scholars arguing that technology will allow us to respond to the excesses of pricing algorithms. It is the purpose of this chapter to give a detailed overview of this debate on algorithms, price setting and competition law .

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Notes

1.
Ezrachi and Stucke (2016), p. 233.
2.
Mehra (2014).
3.
Mehra (2015).
4.
See Footnote 1.
5.
For a list of references discussing the topic, see infra References.
6.
For an argument in other fields, see, e.g., Lemley (2015), Jacobs (2016).
7.
Ezrachi and Stucke (2017a), p. 1782. For a summary and examples, see Schwalbe (2018), pp. 4–6.
8.
Digital eye has also been termed autonomous machine in earlier versions of the Ezrachi and Stucke’s work.
9.
Ezrachi and Stucke (2017a) p. 1782. See also Janka and Uhsler (2018), pp. 114–119.
10.
OECD (2017).
11.
OECD (2017), pp. 24–32.
12.
Colombo (2018), pp. 12–14.
13.
Ezrachi and Stucke (2017a), p. 1782.
14.
OECD (2017), p. 24.
15.
Ezrachi and Stucke (2017a), p. 1786.
16.
Colombo (2018).
17.
United States v. Airline Tariff Publ’g Co., 836 F. Supp. 9 (D.D.C. 1993); Ezrachi and Stucke (2017a), p. 1786.
18.
Colombo (2018), p. 12.
19.
Ezrachi and Stucke (2017a), p. 1782.
20.
Colombo (2018), p. 13.
21.
See Footnote 21.
22.
OECD (2017), p. 27.
23.
OECD (2017), p. 27; see also Capobianco and Gonzaga (2017), p. 4.
24.
Ezrachi and Stucke (2017a), p. 1782.
25.
Ezrachi and Stucke (2017a), p. 1793.
26.
See, e.g., Capobianco and Gonzaga (2017), p. 2.
27.
OECD (2017), p. 30.
28.
Ezrachi and Stucke (2017a), p. 1795.
29.
See Footnote 28.
30.
For a comparison between different jurisdictions, see Dabbah and Hawk (2009).
31.
Colombo (2018), p. 12; Ezrachi and Stucke (2017a), p. 1785.
32.
Ezrachi and Stucke (2017a), p. 1788.
33.
See Orbach (2016); some countries, like Japan, have a complex interpretation of their competition law in relation to hub-and-poke type of cartels (and cartel facilitating firms in general), see Kameoka (2014), pp. 44–49.
34.
Dolmans (2017).
35.
Ezrachi and Stucke (2016), p. 68. For a reference how to deal with in the German context, see Janka and Uhsler (2018), pp. 119–120.
36.
Ezrachi and Stucke (2017a), p. 1785.
37.
Ezrachi and Stucke (2016), p. 77.
38.
Ezrachi and Stucke (2016), p. 79.
39.
Ezrachi and Stucke (2017a), p. 1785, Ezrachi and Stucke (2016), p. 79.
40.
It should be noted that several scholars already questioned the theory of tacit collusion . See Posner (1976) (even though he withdrew from his critique, see Posner (2014)) and Kaplow (2013). The early critique has been part of the current scholars debate to indicate that it is not the first time that the theory of tacit collusion has been questioned. See Gal (2018), pp. 27–28.
41.
Ezrachi and Stucke (2017a), p. 1797.
42.
Ezrachi and Stucke (2017b), p. 2.
43.
Mehra (2015), p. 1363.
44.
OECD (2017), p. 35.
45.
Vestager (2017).
46.
Petit (2017a), p. 362.
47.
Stucke and Ezrachi (2017a), pp. 8–13.
48.
Stucke and Ezrachi (2017a), p. 12.
49.
Mehra (2015), p. 1343.
50.
Hovenkamp (2005).
51.
Axelrod (1988).
52.
Mehra (2015), p. 1346.
53.
Hovenkamp (2005), p. 161.
54.
See Footnote 52.
55.
Mehra (2016), pp. 1344–1351.
56.
Ittoo and Petit (2017).
57.
Ittoo and Petit (2017), p. 4.
58.
Salcedo (2015).
59.
Salcedo (2015), pp. 4–5.
60.
Ittoo and Petit (2017), p. 5; see also Schwalbe (2018), p. 9.
61.
Watkins and Dayan (1992), p. 279.
62.
Salcedo (2015), p. 5.
63.
Ittoo and Petit (2017), p. 5
64.
Deng (2018a), p. 85.
65.
Ittoo and Petit (2017), p. 6.
66.
Ittoo and Petit (2017), p. 7.
67.
Ittoo and Petit (2017), pp. 7–10.
68.
Ittoo and Petit (2017), p. 9.
69.
Ittoo and Petit (2017), p. 8.
70.
See Footnote 70.
71.
Ittoo and Petit (2017), p. 9.
72.
Ittoo and Petit (2017), p. 11.
73.
Ittoo and Petit (2017), p. 12.
74.
Ittoo and Petit (2017), pp. 12–13.
75.
Ittoo and Petit (2017), p. 13.
76.
See Footnote 76.
77.
Deng (2018a).
78.
See Footnote 76.
79.
Deng (2018a), p. 86; Deng (2018b), p. 4.
80.
Deng (2018a), p. 85.
81.
See Footnote 81.
82.
See Footnote 81.
83.
See Footnote 81.
84.
See Footnote 81.
85.
Deng (2018a), p. 86.
86.
Schwalbe (2018), p. 23.
87.
Schwalbe (2018), p. 13.
88.
Schwalbe (2018), p. 15.
89.
See Footnote 89.
90.
Schwalbe (2018), pp. 16–17.
91.
Simple algorithms can result quicker in cooperation than algorithms based on deep neural networks. See Schwalbe (2018), p. 17.
92.
Markets are not static. Changes due to market entry or exit, mergers, or innovation could complicate cooperation for algorithms. See Schwalbe (2018), p. 17.
93.
Current experiments mainly look at the self-play of an algorithms and does not take the interplay of different algorithms into consideration. See Schwalbe (2018), p. 17.
94.
Schwalbe (2018), p. 19.
95.
Schwalbe (2018), p. 21 (containing a quote of Ezrachi and Stucke (2017a), p. 1).
96.
Petit (2017a), p. 361.
97.
Geradin (2017), p. 2.
98.
Dolmans (2017), pp. 8–9.
99.
Dolmans (2017), p. 8.
100.
Petit (2017a), p. 362.
101.
See Footnote 78.
102.
Deng (2018a), p. 85.
103.
See Footnote 81.
104.
Deng (2018a), p. 84.
105.
See Footnote 105.
106.
See Footnote 105.
107.
Ezrachi and Stucke (2016), pp. 83–131.
108.
Geradin (2017), p 4.
109.
Dolmans (2017), p. 9.
110.
Dolmans (2017), p. 4.
111.
Gal (2018).
112.
See Footnote 110.
113.
See Footnote 110.
114.
See Footnote 110.
115.
Gal (2018), p. 21.
116.
Gal (2018), p. 20.
117.
See Footnote 117.
118.
See Footnote 117.
119.
See Footnote 116.
120.
See Footnote 116.
121.
See Footnote 117.
122.
Ezrachi and Stucke (2016), pp. 71–81.
123.
Blockx (2017).
124.
Blockx (2017), p. 5.
125.
See Footnote 125.
126.
See Footnote 125.
127.
On concerted practices and computers, see Heinemann and Gebicka (2016).
128.
Blockx (2017), p. 6.
129.
See Footnote 129.
130.
See Footnote 129.
131.
Janka and Uhsler (2018), pp. 120–121.
132.
Blockx (2017), p. 6. See also in the context of Article 102 Treaty on the Functioning of the European Union, Zingales (2018).
133.
Blockx (2017), p. 7.
134.
See also Marty (2017), p. 15.
135.
See Footnote 134.
136.
Colombo (2018), p. 17. See also Schwalbe (2018), p. 22.
137.
Blockx (2017), pp. 9–11.
138.
Blockx (2017), p. 10.
139.
Blockx (2017) pp. 10–11.
140.
Blockx (2017). See also Blockx (2018).
141.
See Footnote 112.
142.
Gal (2018), p. 29. See also Ballard and Naik (2017), p. 4.
143.
Gal (2018), p. 30. Gal also refers to the current practice of treating facilitating factors as a sub-category of the plus factors. See Gal (2018), p. 31.
144.
As seen above, the other ones could easily fall within the scope of competition law as all of them are linked to one or another form of an agreement.
145.
Gal (2018), p. 33.
146.
Gal (2018), p. 34.
147.
Gal (2018), p. 38.
148.
See Footnote 148.
149.
See Footnote 148.
150.
Gal (2018), p. 39.
151.
Gal (2018), pp. 41–42 (detailed explanations excluded).
152.
Gal (2018), pp. 27–18.
153.
Colombo (2018), pp. 18–20.
154.
Colombo (2018), p. 19.
155.
See Footnote 155.
156.
Gal and Elkin-Koren (2017).
157.
Gal (2017), p. 4.
158.
Gal and Elkin-Koren (2017).
159.
Gal and Elkin Koren (2017), p. 336; see also Stucke and Ezrachi (2016), p. 2.
160.
Stucke and Ezrachi (2016).
161.
Gal (2017), p. 4 (italics added).
162.
See Footnote 158.
163.
See Footnote 158.
164.
Gal and Elkin-Koren (2017), p. 331.
165.
See Footnote 165.
166.
Stucke and Ezrachi (2017b).
167.
UNCTAD (2016). See also Ioannidou (2018), p. 9.
168.
For a detailed analysis of the problem that algorithmic consumers may cause, see Ioannidou (2018).
169.
Petit (2017b).
170.
Petit (2017b), slide 15.
171.
Petit (2017b), slide 16.
172.
Petit (2017b), slide 17.
173.
Petit (2017b), slide 16. The appeal to soft law has also been made by Colombo. See Colombo (2018), p. 21.
174.
Petit (2017b), slide 18.
175.
Ezrachi and Stucke (2016), pp. 228–229.
176.
Ezrachi and Stucke (2016), pp. 230–231.
177.
Ezrachi and Stucke (2016), p. 230. See also Oxera (2017), p. 30.
178.
Ezrachi and Stucke (2016), p. 231. There seems to be discussion on the feasibility or desirability to engage in auditing. See Colombo (2018), p. 20.
179.
Ezrachi and Stucke (2016), p. 231.
180.
See above Sect. 4.2 The Need to Create a Rule of Reason .
181.
The information that the algorithm will take into consideration is most likely past data of the firm to analyze the relationship between price and profit. This data could be linked to current data on the market conditions. Another source of information could be Big Data , information gathered on from consumers, from sales, or even from rival’s firms.
182.
Defined as “An estimation optimization algorithm estimates the environment faced by a firm and then determines what conduct performs best for that estimated environment. It can deliver a forecast on performance (e.g., profit or revenue) for any action (e.g., price) or strategy (e.g., pricing algorithm ). An estimation-optimization algorithm learns over both the environment and the best action for an environment.”
183.
Defined as “reinforcement learning fuses these two learning processes by learning directly over actions (or strategies); it figures out what action (or strategy) is best based on how various actions (or strategies) have performed in the past. It does not explicitly estimate the firm’s environment (e.g., it does not estimate the firm’s demand function) and thus is seen as “model free” because it is not based on a particular model of the firm’s environment”.
184.
See Footnote 193.
185.
See Footnote 193.
186.
Schwalbe (2018), p. 15.
187.
See also Ezrachi and Stucke (2016), pp. 230–231.
188.
Schwalbe (2018), p. 23. See also Marty (2017), p. 15.
189.
Schwalbe (2018), p. 23.
190.
See Footnote 116.
191.
Ezrachi and Stucke (2016), pp. 226–228.
192.
Ezrachi and Stucke (2016), p. 227.
193.
Ezrachi and Stucke (2016), p. 227. See also Dolmans (2017), p. 20.
194.
Ezrachi and Stucke (2016), pp. 229–230.

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Graduate School of Law, Kyushu University, Fukuoka, Japan
Steven Van Uytsel

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Marcelo Corrales
Faculty of Law, Kyushu University, Fukuoka, Japan
Mark Fenwick
Department of Innovation and Digitalisation in Law, University of Vienna, Vienna, Austria
Nikolaus Forgó

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Van Uytsel, S. (2018). Artificial Intelligence and Collusion: A Literature Overview. In: Corrales, M., Fenwick, M., Forgó, N. (eds) Robotics, AI and the Future of Law. Perspectives in Law, Business and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-13-2874-9_7

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