Dynamic Difficulty Adjustment
You don’t want your video game play to be too hard (frustrating) or too easy (boring) it needs to be “just right” to keep you in the flow. I can control this by setting a difficulty level – easy, medium, hard, wipe me out – but that produces additional frustrations when I get stuck on a level. Enter dynamic difficulty adjustment (DDA) where the game, often using an AI algorithm to analyze my performance, will dynamically make play hard or easier depending on circumstances. So the cars I am racing against might slow down a bit or the monsters I am fighting might get a bit weaker if I am stuck. Some games even pop up hints or encouragement, or reduce the difficult of play if I keep repeating a level.
For a great overview of which games do this well and which don’t check out the post on DDA on DevBump.
DDA and videogames socio-psycho dynamics in general are a great source of insights for cognitive designers. DDA is functionality for the real-time adjustment of cognitive load/fit to avoid certain frames of minds (mental states) in customers. The question is how can you incorporate DDA into learning, working and service environments?
For example, one casino has used a variation of DDA to monitor the frustration levels (pain points) of customers, intervening with offers of free meals and shows when frustration with losing gets too high. The idea is to keep them in the game (at the tables) as long as possible.
If maximizing the length of customer interactions is essential to your business model and you have both historical data (to build a profile) and real-time data (to infer emotional state) then DDA could be used to support your customer’s cognition (and make money). Also, if you have the data and customers that are leaving during service delivery or otherwise defecting, it might be possible to use DDA to improve retention.
