Empathy Map with Evidence Marks¶
Experiential mapping artifact — instantiates Other-Agent State Model Calibration
Captures what another agent seems to see, hear, think, feel, say, and do — with every cell tagged as observed evidence or actor assumption.
An empathy map organises another agent's experience into lived quadrants — what they see, hear, think and feel, say and do — rather than into analytical categories of belief and desire. Empathy Map with Evidence Marks adds the one discipline that turns that worksheet from a projection generator into a calibration tool: every entry is tagged as either observed evidence (with its source) or actor assumption. That single mark is the mechanism's whole point. A plain empathy map is a structured way to write down what you already imagine the user feels; the evidence marks make the ratio of knowledge to guesswork visible on the page, so the map records not just a picture of the other agent but how much of that picture is actually grounded.
Example¶
A payments startup builds an Empathy Map with Evidence Marks for a first-time user opening the app to send money abroad. Sees: a home screen dense with options (assumption); a large "Send" button (evidence — from five onboarding sessions). Hears: friends saying the app is "cheaper than the bank" (evidence — survey quotes). Thinks and feels: anxious that the transfer might vanish (evidence — three testers hovered before confirming); excited about the low fee (assumption). Says and does: re-checks the recipient's details twice (evidence — session recordings).
Laid out with marks, the map's shape jumps out: the "feels anxious about the money disappearing" cell is evidenced, while "excited about the low fee" — the story the team was building the launch around — is pure assumption. The assumption cluster becomes the research backlog, and the evidenced anxiety reshapes the confirmation screen. The map didn't just say what the user experiences; it said which of the team's beliefs about the user had earned the right to drive design.
How it works¶
- Fill the experiential quadrants. See, hear, think-and-feel, say-and-do — framing the model in the other agent's lived situation rather than in abstract traits.
- Mark every cell. Each entry is tagged evidence or assumption; evidenced cells carry their source (a session, a quote, an analytics signal).
- Read the assumption density. Clusters of unmarked or assumption cells reveal where the model is guessed — and those clusters become the questions to go answer.
- Refuse blanks. An unmarked cell is treated as an assumption by default, never as a quiet fact.
Tuning parameters¶
- Quadrant set — the classic see/hear/think-feel/say-do, or a variant adding pains and gains. More cells capture more experience but invite more filler.
- Evidence-mark strictness — what counts as a source (a direct quote, a behavioural signal, a second-hand report). Strict marks curb wishful evidence; loose ones let assumptions masquerade as data.
- Assumption-density trigger — how thick a cluster of guesses must get before it forces research rather than shipping.
- Persona scope — one specific user in one moment, or a segment. Narrow scope grounds the map; broad scope generalises and dilutes the evidence.
When it helps, and when it misleads¶
Its strength is that it makes projection visible: an empathy map without marks is a worksheet for imagining the user, while the marks convert it into an honest inventory of what is known versus supposed. It is especially good at catching the moment a team's favourite assumption gets treated as a user fact.
Its failure mode is fabricated marks — labelling a hopeful guess "evidence" so the map looks grounded, which is worse than an honest blank. Its recurring distortion is the false-consensus effect: the team fills the think-and-feel cells with what they would feel and marks them as obvious.[1] And a map assembled after the design is set can be run backwards, decorated with evidence to ratify choices already made. The discipline that guards against this is a hard rule that no cell ships unmarked, and that assumption clusters become the research plan rather than a footnote.
How it implements the components¶
evidence_signal_map— the evidence marks are this component: each experiential claim is mapped to the signal (or the absence of one) that supports it, recording ambiguity instead of erasing it.projection_and_stereotype_filter— the evidence-versus-assumption tagging is a passive projection filter: unmarked cells surface exactly where the actor's own experience has been projected onto the other agent.
It does not maintain the analytical belief/desire/knowledge model with confidence levels (that's the Belief-Desire-Knowledge Map), gather the evidence live (Active Listening Loop), or govern whether the model is legitimate to hold (Consent and Privacy Boundary Checklist). Its projection-flagging is passive; the active version — manufacturing rival readings — is the Counterparty Model Red Team.
Related¶
- Instantiates: Other-Agent State Model Calibration — the experiential, evidence-graded face of the archetype's modelling.
- Sibling mechanisms: Belief-Desire-Knowledge Map · Counterparty Model Red Team · Active Listening Loop · Consent and Privacy Boundary Checklist · False-Belief Check · Perspective-Taking Interview · Prediction and Surprise Log · Role-Reversal Simulation · Interaction After-Action Review · Stakeholder Hidden-Constraint Board
Editorial Notes¶
Form Classification¶
Form family: Representation, Specification & Plan
Rationale: Empathy Map with Evidence Marks operates as a non-executable information artifact that externalizes static or prospective structure because it captures what another agent seems to see, hear, think, feel, say, and do — with every cell tagged as observed evidence or actor assumption.
Independent corroboration: The frozen evidence defines Empathy Map with Evidence Marks as 'Captures what another agent seems to see, hear, think, feel, say, and do — with every cell tagged as observed evidence or actor assumption', so its operative form is Representation, Specification & Plan.
Review outcome: Independent reviewer agreement; high confidence.
Origin Attribution¶
Primary origin: Human-Computer Interaction
Origin pattern: Cross-disciplinary synthesis
Present-day reach: Multi-domain
Rationale: User-centered design cohered empathy maps that organize what a person sees, hears, thinks, feels, says, and does.
Related originating lineages:
- Engineering & Design — Design-research practice supplies the artifact's use in requirements and service design.
- Psychology — Perspective-taking research supplies the distinction between observed behavior and inferred internal state.
Review resolution: The current reviewers agree that human_computer_interaction is primary. For the reported differences (reported_ambiguity, alternate_origin_disagreement, encyclopedia_synthesis_disagreement), the evidence supports cross_disciplinary_synthesis, multi_domain, and engineering_design, psychology; these choices preserve materially formative origins without conflating later domain reach.
Attribution caveat: Evidence marks are a synthetic safeguard added to the standard empathy-map form.
Encyclopedia synthesis: The exact catalogued form synthesizes established practice rather than reproducing a single standard historical label.
Review outcome: Reconciled after independent review; high confidence.
References¶
[1] The false-consensus effect (Ross, Greene & House, 1977) is the tendency to overestimate how widely others share one's own beliefs and reactions. Evidence marks are the empathy map's defence against it: an unmarked cell is a candidate false consensus, not a fact about the other agent. withdrawn registry ↩