46 Pages Posted: 10 Apr 2017 Last revised: 14 Apr 2017
Date Written: April 9, 2017
In the future, one may imagine a new breed on antitrust humor. Jokes might start along the following lines: “Two Artificial Neural Network and one Nash equilibrium meet in an online (pub) hub. After a few milliseconds, a unique silent friendship is formed…”
Back to the present; we are not sure how this joke might end. Nor can we estimate how funny future consumers would find it. We can, however, explain, at present, how technological advancements have changed, and will continue to change, the dynamics of competition and subsequently the distribution of wealth in society. How algorithms may be used in stealth mode to stabilize and dampen market competition while retaining the façade of a competitive environment. That tale is at the heart of this paper.
We first raised algorithmic tacit collusion in 2015. Our recent book, Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy, provides further context and analysis. We illustrate how online tacit collusion may emerge when products are generally homogeneous, sellers do not benefit from brand recognition or loyalty, and markets are transparent and concentrated.
Since our book elaborates on the four collusion scenarios, we begin here by outlining one model of tacit collusion and its manifestation online. Taking note of advancements in technology and emerging policies, we move the debate forward in reviewing the possible harm and means to address it. We illustrate with several case studies how the move to an online pricing environment, under certain market conditions, may harm the buyers’ welfare. We note how new technologies may undermine enforcers’ attempts to intervene - as stealth becomes a feature of future strategies.
That tale, of course, is not immune from disruptive strategies. We consider the testing of counter-measures in an “algorithmic collusion incubator” to better understand what effectively destabilizes algorithmic tacit collusion. Further, we consider the effects and likelihood of secret dealings. We note how, somewhat counter-intuitively, secrete deals in an online environment could reduce, at times, our welfare.
Keywords: collusion, competition, algorithms, behavioral discrimination
JEL Classification: K21, L40, L41
Suggested Citation: Suggested Citation
Ezrachi, Ariel and Stucke, Maurice E., Two Artificial Neural Networks Meet in an Online Hub and Change the Future (Of Competition, Market Dynamics and Society) (April 9, 2017). Available at SSRN: https://ssrn.com/abstract=2949434 or http://dx.doi.org/10.2139/ssrn.2949434