« Power pose relapse | Main | Bursting of the social-media bubble »


Feed You can follow this conversation by subscribing to the comment feed for this post.

Michael Schettler

I think we might disagree on what is being optimized for.

My assumption as I was reading was that the algorithm would be optimized for cost/energy expenditures, therefore the fewest trips with no regard for passenger waiting times.


MS: Good point. It's more than likely that most businesses are optimizing for their own benefits first before customers'. There's probably some regularizing constraint or multiple objectives because the minimal energy setting would probably involve a higher occupancy rate, which means making passengers wait a lot longer.

James Kingsbery

This analysis ignores the people who pressed the "9" "12" and "14" buttons, who didn't have to share an elevator with you as a result.


JK: My point is that what is good for individuals may not be good for the average. If optimizing for the average, there will be individual "winners" and "losers." There was one clear winner in the example I gave.

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Your comment could not be posted. Error type:
Your comment has been posted. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.


Post a comment

Your Information

(Name is required. Email address will not be displayed with the comment.)


Link to Principal Analytics Prep

See our curriculum, instructors. Apply.
Business analytics and data visualization expert. Author and Speaker. Founder of Principal Analytics Prep, MS Applied Analytics at Columbia. See my full bio.

Next Events

Oct: 31 Webinar on Data Visualization, online at JMP

Nov: 1 NYU unCOMMON Salon Public Lecture, New York, NY

Nov: 8 Tufts Gordon Institute: A Conversation with Kaiser Fung, Facebook Live

Nov: 8 Tufts TGI Careers & Networking Night panel, Somerville, MA

Nov: 26 Data Visualization New York Meetup, New York, NY

Nov: 27 NYPL Data Analytics Resume Workshop, New York, NY

Nov: 30 Purdue School of Engineering Seminar, West Lafayette, IN

Dec: 1 Purdue Mathematics, Data Science, and Industry Conference, West Lafayette, IN

Past Events

See here

Future Courses (New York)

Summer: Statistical Reasoning & Numbersense, Principal Analytics Prep (4 weeks)

Summer: Applied Analytics Frameworks & Methods, Columbia (6 weeks)

Junk Charts Blog

Link to junkcharts

Graphics design by Amanda Lee


  • only in Big Data