Articles by Anne Hobson

Anne HobsonAnne Hobson is a Program Manager for Academic & Student Programs at the Mercatus Center at George Mason University. She is an associate fellow at the R Street Institute. Anne is currently pursuing a PhD in economics from George Mason University. She received her M.A. in applied economics from George Mason University and her B.A. in International Studies from Johns Hopkins University.


-Coauthored with Mercatus MA Fellow Walter Stover

Imagine visiting Amazon’s website to buy a Kindle. The product description shows a price of $120. You purchase it, only for a co-worker to tell you he bought the same device for just $100. What happened? Amazon’s algorithm predicted that you would be more willing to pay for the same device. Amazon and other companies before it, such as Orbitz, have experimented with dynamic pricing models that feed personal data collected on users to machine learning algorithms to try and predict how much different individuals are willing to pay. Instead of a fixed price point, now users could see different prices according to the profile that the company has built up of them. This has led the U.S. Federal Trade Commission, among other researchers, to explore fears that AI, in combination with big datasets, will harm consumer welfare through company manipulation of consumers to increase their profits.

The promise of personalized shopping and the threat of consumer exploitation, however, first supposes that AI will be able to predict our future preferences. By gathering data on our past purchases, our almost-purchases, our search histories, and more, some fear that advanced AI will build a detailed profile that it can then use to estimate our future preference for a certain good under particular circumstances. This will escalate until companies are able to anticipate our preferences, and pressure us at exactly the right moments to ‘persuade’ us into buying something we ordinarily would not.

Such a scenario cannot come to pass. No matter how much data companies can gather from individuals, and no matter how sophisticated AI becomes, the data to predict our future choices do not exist in a complete or capturable way. Treating consumer preferences as discoverable through enough sophisticated search technology ignores a critical distinction between information and knowledge. Information is objective, searchable, and gatherable. When we talk about ‘data’, we are usually referring to information: particular observations of specific actions, conditions or choices that we can see in the world. An individual’s salary, geographic location, and purchases are data with an objective, concrete existence that a company can gather and include in their algorithms.

Continue reading →

“You don’t gank the noobs” my friend’s brother explained to me, growing angrier as he watched a high-level player repeatedly stalk and then cut down my feeble, low-level night elf cleric in the massively multiplayer online roleplaying game World of Warcraft. He logged on to the server to his “main,” a high-level gnome mage and went in search of my killer, carrying out two-dimensional justice. What he meant by his exclamation was that players have developed a social norm banning the “ganking” or killing of low-level “noobs” just starting out in the game. He reinforced that norm by punishing the overzealous player with premature annihilation.

Ganking noobs is an example of undesirable social behavior in a virtual space on par with cutting people off in traffic or budging people in line. Punishments for these behaviors take a variety of forms, from honking, to verbal confrontation, to virtual manslaughter. Virtual reality social spaces, defined as fully artificial digital environments, are the newest medium for social interaction. Increased agency and a sense of physical presence within a VR social world like VRChat allows users to more intensely experience both positive and negative situations, thus reopening the discussion for how best to govern these spaces.

Continue reading →