Observation Contexts

Understanding and working with observation contexts

Observation Contexts

An Observation Context is a set of beliefs that are grouped together based on how the Self-Model perceives the world. For example, if a Self-Model is tracking their sleep and heart rate variability, they might create an Observation Context for “Sleep and Heart Rate Variability”.

Creating an Observation Context

observation_context = epistemic_me.ObservationContext.create(
    self_model_id="self_001",
    name="Sleep and Heart Rate Variability",
    possible_values=[
        {"sleep_min": 0},
        {"sleep_max": 16},
        {"hrv_min": 0},
        {"hrv_max": 150}
    ],
    context="sleep_and_hrv"
)

Working with Observation Contexts

Observation Contexts can be used to:

• Group related beliefs together
• Provide context for falsifiable and causal beliefs
• Define possible values for observations
• Track historical observations and evidence

Conceptualized Belief Systems

When working with Observation Contexts, you can create a Conceptualized Belief System that forms a graph data structure representing an ontology of a Self-Model:

belief_system = epistemic_me.SelfModel.retrieve_belief_system(
   id="self_001",
   conceptualize=True
)