Amazon marketplace pricing-algorithm war
A biology textbook on Amazon's marketplace was automatically priced at over $23 million by two competing third-party sellers' algorithms before anyone bought it, a widely publicized example of runaway algorithmic pricing.
The damage was public. The root cause was preventable.
A biology textbook on Amazon's marketplace was automatically priced at over $23 million by two competing third-party sellers' algorithms before anyone bought it, a widely publicized example of runaway algorithmic pricing.
What the software actually got wrong
Two sellers each used automated repricing software that set their price as a fixed multiple of the other's current price; because both algorithms reacted to each other with no upper bound, they repeatedly and automatically outbid one another into an absurd price spiral.
Two sellers each used automated repricing software that set...
Two sellers each used automated repricing software that set their price as a fixed multiple of the other's current price
Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.
Because both algorithms reacted to each other with no...
because both algorithms reacted to each other with no upper bound, they repeatedly and automatically outbid one another into an absurd price spiral
Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.
Beyond code coverage and "shall" testing
Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.
Surfaces this before code exists
Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.
Connects the failure mode to the hazard
Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.
Find the overlooked root causes before they ship.
Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.