How Do Behavioural Algorithms Actually Work

Jess and Eric explain how behavioral algorithms work by first defining human behavior in digital environments through context, timing, past experiences, incentives, and emotions, and outlining how current digital systems often fail by relying on static designs, majority-rule A/B testing, and system-derived cues rather than individual humans. They introduce behavioral design as a way to craft interventions using psychological triggers and to better understand personality in relation to an organization. Eric describes two complementary categories: personality-based algorithms that infer interaction “personalities” from data using behavioral constructs and indicators (illustrated with Spotify Wrapped–style constructs), and decision algorithms that use that context for continuous learning and action, including epsilon-greedy, Naive Bayes, Ecosystem rewards, and Q-learning. The discussion explores better alternatives like loss aversion vs risk aversion models and clarifies the focus is product recommendations and offers to customers. 01:20 Defining Human Behavior 01:52 Context Timing Experience Incentives 04:18 Emotions and Digital Behavior 05:35 Why Digital Algorithms Fail 09:30 Behavioral Design Approach 11:52 Personality and Engagement 13:18 Two Algorithm Categories 14:46 Building Personality Algorithms 16:37 Spotify Wrapped Example 19:00 Digital Interaction Personality Model 20:51 Ecosystem AI Personality Types 24:07 Decision Algorithms and Learning 25:13 Algorithm Examples and Wrap Up 27:24 Q and A Advanced Models 29:00 How Constructs Become Execution