Data formatting
Behavioral, perception, and biological streams are normalized into structured inputs each agent can read—explicitly carrying Belief Vector Direction and Magnitude through the pipeline.
BOTTOM-UP
SIMULATION.
Divera simulation is not a forecast sketched on top of a spreadsheet. It is a bottom-up environment: individual agents act autonomously, and market outcomes are aggregated from their decisions—not estimated, not interpolated, not averaged in.
01 // METHOD · BOTTOM-UP PIPELINE
Mind model training
One neural decision engine is trained per individual, using one-shot learning so the approach works even when sample size is small.
Validation
Models are tested against held-out behavior and real market response before any scenario is run. Explainable outputs (XAI) are required to pass—not optional.
Decision engine deployment
Validated agents are released into the simulation. Each takes autonomous actions under your propositions, producing individual decisions that compose into market-level results.
Market share is aggregated, not estimated.
Conventional models guess the top line and back-fill a story. Divera runs in the opposite direction: every market share, conversion curve, and trajectory in the sandbox is composed from individual purchase decisions that agents make autonomously under the scenario you defined.
That is what makes the output defensible. The number isn't a regression it hid from you—it's a count of actions, traceable to the individual agents that produced them.
02 // TRAJECTORY_MAPPING
[AXIS_Y]: MARKET_PENETRATION
[AXIS_X]: TIME_SEQUENCE
A simulation is iterative: each run is a different scenario on the same inputs, so you compare trials—not a single one-off result.
03 // INPUT_LAYER (BELIEF_VECTORS + BIOLOGY)
SOURCE_01 // DIRECTION
Belief Vector Direction
For every strategy-relevant attribute, the agent carries which way the belief leans—positive or negative—so the simulation knows not just that a signal exists, but which side of zero it's on.
SOURCE_02 // MAGNITUDE
Belief Vector Magnitude
Paired with direction: how strong the conviction is. Magnitude determines how hard an attribute pulls on the agent's decision under stress—and which beliefs are too soft to move the needle.
SOURCE_03 // BIOLOGICAL
Neuroscience-based signal
Subconscious response is recorded at signal level via neuroscience-based measurement. This is the input that lets direction and magnitude reflect real reaction, not self-reported narrative.
04 // ATTRIBUTE_SPACE
STRATEGY_ATTRIBUTE_CLUSTERS
Node size reflects how often an attribute appears in the agent population—larger points are more frequent. Schematic / illustrative
05 // FROM_SIGNAL_TO_RUN
01
Signals become structured vectors.
Biological response and behavioral traces are converted into Belief Vector Direction and Magnitude per attribute, so each person's beliefs are encoded—not summarized.
02
Individual mind models are trained.
Each agent gets a dedicated neural decision engine, not a shared archetype. Training uses one-shot learning so the pipeline runs on the data a strategy team actually has.
03
Agents decide; the market is counted.
In the scenario, agents act autonomously. Market shares, conversion, and trajectories are aggregated from those individual decisions—explainable per agent, not just at the top line.
06 // COMPLEX_INTERACTIONS
EFFECT_01
Competitive response
How rivals might react to your moves and trigger chain effects across segments.
EFFECT_02
Ripple effects
When one audience shift pulls another—positive or negative—inside the run.
EFFECT_03
Exposure spacing
Frequency of experience improvements to maximize memory without fatigue.