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2018 | OriginalPaper | Buchkapitel

Artificial Intelligence and Collusion: A Literature Overview

verfasst von : Steven Van Uytsel

Erschienen in: Robotics, AI and the Future of Law

Verlag: Springer Singapore

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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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Fußnoten
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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Metadaten
Titel
Artificial Intelligence and Collusion: A Literature Overview
verfasst von
Steven Van Uytsel
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-2874-9_7