Forestry Economics: A Managerial Approach by John E. Wagner has a great explanation of the decision modes.  The Wikipedia article on Minimax includes pseudocode for using Minimax in games, such as Chess.  Of particular interest was the mention of this technique’s use by Deep Blue, the computer which beat Gary Kasparov in chess.

Management and the Technology Professional – B302 Risk analysis using maximin criterion, minimax regret criterion, expected value criterion, and decision trees is a good example of decision theory writing as well, and it includes a Dilbert cartoon.  Ultimately the regret table at Wikipedia was one of the most useful.

Real-World Decision Making: An Encyclopedia of Behavioral Economics edited by Morris Altman captured my interest when search related to Laplace decision criteria.  It’s more of an economics book on behavior.  I am mentioning here not because of decision theory content, but because it’s page on Google Books led me to IndieBound.org, which seems to be a federation of independent bookstores.

Parmy Olsen describes the filter bubble that exists on the internet.

“…as algorithms make predictions about people based on their web behavior, they can inadvertently deepen existing disparities on aspects like culture, race or gender. In a few years you could, for instance, be looking at a richer or poorer version of the Internet depending on how things work out with your credit score or where you live, and not even know it.” (1)

She further says “The Princeton researchers will compare search results, prices, ads, offers and emails that their fake profiles receive over the coming months, and look for patterns to measure what kind of discrimination is happening across different sites.” (1)

1. Olson, Parmy. “This Landmark Study Could Reveal How The Web Discriminates Against You – Forbes.” News. This Landmark Study Could Reveal How The Web Discriminates Against You, 2 Dec. 2013, http://www.forbes.com/sites/parmyolson/2013/12/02/this-landmark-study-could-reveal-how-the-web-discriminates-against-you/.

2. Englehardt, Steven, et al. Web Privacy Measurement: Scientific Principles, Engineering Platform, and New Results., Draft, June 1, 2014 https://www.cs.princeton.edu/~arvindn/publications/WebPrivacyMeasurement.pdf

3. Englehardt, Steven, et al. OpenWPM: An Automated Platform for Web Privacy Measurement. Zotero, https://senglehardt.com/papers/openwpm_03-2015.pdf.

Addendum: The study was published. They focused on news site personalization at the time, which was before Google entered the foray as a primary driver of news content via Google News. Steven Englehardt maintains a webpage at https://senglehardt.com/pages/publications.html. The published material is very interesting and reveals how tracking mechanisms work over time. I have been unable to find detailed analysis of the different filter bubbles encountered by their bots. They published a second paper on their privacy analysis tool, but it does not delve into the different potential experiences of the web user. The material relates to the technical aspects of what occurs. Those studies have been added with notes 2 and 3.

Last modified on March 7th, 2026 at 10:44 pm