Micro-Experiment Sequence¶
Experimental sequence — instantiates Sense-Act Loop Coupling
Chains many small, cheap tests into a branching series, carrying each result forward so every next experiment is chosen from what the previous ones revealed.
A Micro-Experiment Sequence chains many small, cheap tests into a branching series in which each result is carried forward and reframes the next test — so the sequence as a whole learns something no single experiment could. Its unit is not one probe but the chain: the value lives in how results accumulate in a running trace and how each outcome forks which experiment runs next. It runs the loop at a deliberate cadence, updating its model of what is now possible after each result settles. Crucially, it does not design or bound the individual probe — that job belongs to the Active Probe Protocol it consumes for each step. Its own contribution is the sequencing, the memory across steps, and the branching.
Example¶
A two-person startup cannot tell why sign-ups stall on their landing page. Instead of one big redesign, they run a sequence of micro-experiments, each cheap and reversible. Experiment 1: rewrite the headline; the result — click-through is flat, but scroll depth rises. Logged, that result reframes the next question: the problem isn't the hook, it's the ask. Experiment 2, chosen because of experiment 1: move the sign-up form above the fold; the result — sign-ups climb 20%, but people drop off at the email field. Experiment 3, branched from that: make the email field optional; the result — completion jumps.
Each experiment was selected from the accumulated trace of the ones before it. Run in a different order, or with the results forgotten between steps, the sequence would have learned far less — it would have been flailing that happened to include some A/B tests. What makes it a mechanism is that the chain has memory and the branches are chosen from it: a path through a design space too large to map up front, bought one cheap result at a time.
How it works¶
- Delegate each step's design to a bounded probe. Every experiment is an Active Probe Protocol — the sequence assumes the single probe is already scoped and safe, and concerns itself with what comes after.
- Accumulate results in a running trace. The sequence has memory: each outcome is recorded so later choices can draw on all the earlier ones.
- Branch, don't batch. Each result reroutes which experiment runs next; the sequence is a tree grown from outcomes, not a fixed list run to completion.
- Update what's now possible, at a settled cadence. After each result the model of what is attractive, blocked, or newly reachable updates — paced so interpretation stabilizes before the next step fires.
Tuning parameters¶
- Branching policy — how aggressively each result reroutes the plan versus running a pre-set batch; adaptive branching learns faster but is harder to audit.
- Step cadence — how long each result is allowed to settle before the next experiment; too fast and interpretation never stabilizes (thrash), too slow and momentum dies.
- Memory horizon — how many past results inform the next choice; a longer horizon catches slow patterns but risks over-fitting to history.
- Reversibility budget — how cheap each step is to undo; keeping steps disposable is what lets the sequence branch freely.
When it helps, and when it misleads¶
Its strength is accumulating a path through a space too large to map in advance — cheaply, reversibly, with each result buying the next question. It shines exactly where a single large experiment would be too blunt and pure trial-and-error too aimless.
Its failure mode is subtle: an undisciplined branching sequence with a rich trace is a garden of forking paths — with enough experiments and enough freedom to reinterpret after the fact, you will eventually "find" a result that is noise dressed as signal.[1] The classic misuse is HARKing across the sequence — deciding what you were really testing only after you see which branch looked good. The guarding discipline is to pre-commit each step's hypothesis and success bar before it runs, and to keep the trace honest so the branching stays auditable rather than retrofitted.
How it implements the components¶
Micro-Experiment Sequence fills the chaining-and-memory side of the archetype's machinery — the part that turns isolated probes into cumulative learning:
state_memory_trace— its core: the running record of results that gives the sequence memory across steps.changed_observation_state— each experiment's result is a first-class observation that the next step consumes.affordance_update— after each result, the model of what is now possible, attractive, or blocked updates.loop_cadence— the deliberate rhythm at which experiments run and interpretations settle between them.
A Micro-Experiment Sequence chains and remembers; it does not implement epistemic_action_probe, safety_and_contamination_boundary, or perceptual_constraint_map — designing and bounding each single probe is its nearest twin, Active Probe Protocol, which this sequence consumes for every step.
Related¶
- Instantiates: Sense-Act Loop Coupling — it is the scientific / organizational version of the loop, learning a path through a space by cumulative small tests.
- Consumes: Active Probe Protocol supplies the scoped, bounded design for each individual experiment in the chain.
- Sibling mechanisms: Look-Move-Look Cycle · Interactive Task Walkthrough · Mobile or Embodied Sensor Array · Perceptual Calibration Drill · Action-Observation Log
Editorial Notes¶
Form Classification¶
Form family: Experiment, Test & Rehearsal
Rationale: Micro-Experiment Sequence operates as a bounded trial, probe, simulation, or rehearsal that generates evidence from performance because it chains many small, cheap tests into a branching series, carrying each result forward so every next experiment is chosen from what the previous ones revealed.
Independent corroboration: The frozen evidence defines Micro-Experiment Sequence as 'Chains many small, cheap tests into a branching series, carrying each result forward so every next experiment is chosen from what the previous ones revealed', so its operative form is Experiment, Test & Rehearsal.
Review outcome: Independent reviewer agreement; high confidence.
Origin Attribution¶
Primary origin: Statistics & Experimental Design
Origin pattern: Cross-disciplinary synthesis
Present-day reach: Multi-domain
Rationale: Choosing each small experiment from prior results is sequential or adaptive experimental design. Lean innovation and management practice made cheap branching tests an operating routine, but the evidence-acquisition logic is statistical.
Related originating lineages:
- Innovation & Entrepreneurship — Lean experimentation institutionalized cheap iterative tests under uncertainty.
- Organizational & Management Science — Organizational learning contributes the sense-act adaptation cycle.
Review resolution: NIST's sequential Bayesian experiment-design program explicitly selects new experiments using information from prior measurements. That is the mechanism's defining logic; innovation and management are retained for the low-cost learning cadence. The alternates are retained only as formative or independently established origins, not because the mechanism can be applied there. origin_mode=cross_disciplinary_synthesis states the provenance relationship; domain_reach=multi_domain separately records breadth because independent established uses occur in several fields. confidence=high reflects the strength and specificity of the evidence; encyclopedia_synthesis=false because the entry generalizes an established mechanism without inventing a new composite.
Review outcome: Researched adjudication after independent review; high confidence.
Sources consulted:
- https://www.nist.gov/programs-projects/sequential-bayesian-experiment-design — NIST research describes sequential selection of experiments based on earlier results.
References¶
[1] Gelman, A., & Loken, E. "The Garden of Forking Paths: Why Multiple Comparisons Can Be a Problem, Even When There Is No ‘Fishing Expedition’ or ‘p-Hacking’ and the Research Hypothesis Was Posited Ahead of Time". Unpublished manuscript, Columbia University (2013). Shows how data-contingent analytical choices can make pure noise appear statistically significant. registry ↩