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Modeling Behavior for Just-in-Time Interventions

Explore modeling behavior that harms long-term utility to enable future technology interventions. Challenges, bold steps, and project ideas are discussed for developing sensitive systems.

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Modeling Behavior for Just-in-Time Interventions

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  1. Breakout topic: (originally: At what detail must you model behavior to sufficiently model ‘irrationality’ and temporal decision making so that you can develop sensitive, real-time interventions? And how would you propose to do this using technology of the future) Revamped by group to: At what level of detail do we need to model behavior that hurts person in long run (doesn’t maximize utility) to be able to do just-in-time intervention with technology of future? Bonnie Spring, Donna Spruijt-Metz, Daniel E. Rivera, Niels Boye, Susan Michie, Pamela Kato, Ana Paiva, Scribe: Outi Kenttä

  2. Problem area #1 (Model Derivation) • Challenge/barrier: Knowing the terms in the model [processes (including wants, needs, emotions), sequences, context) • Bold step: Compare • A. Formal theoretical model to • B. individual participant’s model of what causes their “Irrational” behavior • C. Build population level model and drive entirely data-driven, infer processes, iterate *Compare the models to understand “irrationality”

  3. Problem area #2 (How to detect processes, sequences) • Challenge/barrier:create sensors to detect the main processes, • Bold step: *Identify early process signatures

  4. Problem area #3 (Where to start) • Challenge/barrier: Where do we start – with testing models based on groups or individuals? • Bold step: Do both and compare them

  5. Problem area #4 (Data interpretation) • Challenge/barrier: Interpret the data in context and for the individual • Bold step: • Actually test theories against individual change data • Hold theories to the standard of falsifiability!!

  6. Problem area #5 (Actuation) • Challenge/barrier: How can model respond to input and actuate a response action, visualization or decision? • Bold step: • Develop systems that can learn

  7. Detailed solution [Pick one of the challenges/barriers from the five previous slides. Incorporating expertise from those in your group, describe a specific project or set of steps you might collectively engage in to address the challenge and advance the field] How can you design a learning system to control FF consumption? Sensors for context (FF proximity), urge to eat FF, goals. Implicit theory, collect data, iterate. Bold theory testing w/ refutation if construct adds nothing.

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