Results 11 to 20 of about 5,279 (162)

Algorithmic collusion: Genuine or spurious?

International Journal of Industrial Organization, 2023
Reinforcement-learning pricing algorithms sometimes converge to supra-competitive prices even in markets where collusion is impossible by design or cannot be an equilibrium outcome. We analyze when such spurious collusion may arise, and when instead the algorithms learn genuinely collusive strategies, focusing on the role of the rate and mode of ...
Calvano E., Calzolari G., Denicolò V., Pastorello S.   +3 more
openaire   +3 more sources

DIGITAL PLATFORMS AND ALGORITHMIC PRICING: INVESTIGATING MARKET EFFICIENCY AND CONSUMER WELFARE IN THE AGE OF BIG DATA [PDF]

Malaysian E Commerce Journal
The rise of digital platforms has profoundly transformed modern markets, particularly through the deployment of algorithmic pricing strategies powered by big data.
Israel Grace, Onum Friday Okoh
doaj   +1 more source

Supporting social innovation through visualisations of community interactions [PDF]

, 2019
Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members ...
Allahbakhsh Mohammad, Dixon Eleri, European Commission, European Commission DG CONNECT., Gasparini Gian-Luca, Mckenna S., Morelli Nicola, Moreno J.   +7 more
core   +2 more sources

Autonomous algorithmic collusion: economic research and policy implications [PDF]

Oxford Review of Economic Policy, 2021
Abstract Markets are being populated with new generations of pricing algorithms, powered with artificial intelligence (AI), that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude.
Stephanie Assad, Emilio Calvano, Giacomo Calzolari, Robert Clark, Vincenzo Denicolò, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes, Lei Xu, Matthijs Wildenbeest   +10 more
openaire   +7 more sources

Can Government Incentive and Penalty Mechanisms Effectively Mitigate Tacit Collusion in Platform Algorithmic Operations?

Systems
Algorithmic collusion essentially constitutes a form of monopolistic agreement that utilizes algorithms as tools for signaling collusion, making it particularly challenging for both consumers and antitrust enforcement agencies to detect.
Yanan Wang, Yaodong Zhou
doaj   +1 more source

Algorithmic collusion with imperfect monitoring

International Journal of Industrial Organization, 2021
Abstract We show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another.
Calvano E., Calzolari G., Denicolo V., Pastorello S.   +3 more
openaire   +3 more sources

Competition law in the age of AI: Confronting algorithmic collusion in the smart economy

Smart Cities and Regional Development Journal
AI-driven pricing has become vital in the smart economy, boosted by advancements in IoT, AI, and big data. While robots and AI systems enhance efficiency and drive innovation across digital commerce platforms, they also raise competition law concerns ...
Chen LI, Ina VIRTOSU
doaj   +1 more source

Deciphering algorithmic collusion: Insights from bandit algorithms and implications for antitrust enforcement

Journal of Economy and Technology
This paper explores algorithmic collusion from both legal and economic perspectives, underscoring the increasing influence of algorithms in firms’ market decisions and their potential to facilitate anti-competitive behaviour.
Frédéric Marty, Thierry Warin
doaj   +1 more source

A Hybrid Approach to Privacy-Preserving Federated Learning

, 2019
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees ...
Anwar, Ali, Baracaldo, Nathalie, Ludwig, Heiko, Steinke, Thomas, Truex, Stacey, Zhang, Rui, Zhou, Yi   +6 more
core   +1 more source

A New Lower Bound for Deterministic Truthful Scheduling

, 2020
We study the problem of truthfully scheduling $m$ tasks to $n$ selfish unrelated machines, under the objective of makespan minimization, as was introduced in the seminal work of Nisan and Ronen [STOC'99].
A Filos-Ratsikas, A Mu’alem, C Ventre, C Yu, E Koutsoupias, E Koutsoupias, G Christodoulou, G Christodoulou, G Christodoulou, I Ashlagi, JK Lenstra, N Nisan, O Kuryatnikova, P Dhangwatnotai, P Lu, P Lu, P Penna, R Lavi, V Auletta, VV Vazirani, X Chen, Y Giannakopoulos, Y Giannakopoulos   +22 more
core   +1 more source