Evidence grounds the tradeoffs — it does not predict your future
Forked Futures does not use evidence to forecast a personal outcome. It uses a small, curated base of public sources and decision frameworks to ground each route's assumptions and make its tradeoffs clearer. Every piece of evidence is labeled by where it came from, and occupation-level data is only attached when the decision is actually occupation-shaped.
These are evidence-grounded future scripts — plausible trajectories, not deterministic predictions. Forked Futures drafts the scripts; you choose what to test.
From your answers to a grounded route review
A directed flow. Your own answers drive it; evidence enters to ground the route assumptions, never to score you.
one messy situation + a short causal question chain
inferred values, constraints, fears — soft signals, never verdicts
10 distinct strategies scored against your signal
public sources + frameworks, gated to the decision
a transparent match score — not a probability
assumptions, tradeoffs, and a low-cost first test
Every piece of evidence is labeled by where it came from
So you can weigh it accordingly. An inferred assumption is never dressed up as a sourced citation.
Something you actually told us in the journey — your values, constraints, or fears.
A public source or decision framework, used at its true coverage level — never as a per-person forecast.
A reasonable assumption the system inferred from your answers. Flagged as such, and never shown as a citation.
The curated evidence base
Each card states what its source can and cannot support, at its true coverage level. No invented statistics; nothing here is an individual prediction.
Public data sources
career-shaped onlyOccupation, education, labor-market and outcome data — attached only when the decision is occupation-shaped.
- +Typical day-to-day duties of an occupation
- +The direction (not the magnitude for you) of projected demand for a role
- +The education and training a role typically expects
- –Whether you specifically will get hired or how much you will earn
- –Outcomes for a single person or a single employer
- +The specific skills, tasks, and work activities an occupation involves
- +Which of your strengths a role actually draws on day to day
- +Adjacent occupations that share a skill profile
- –Whether a specific role will suit you personally
- –Your individual performance or satisfaction in it
- +Field- and program-level earnings and cost patterns across cohorts
- +How outcomes vary by program rather than by school name alone
- +The typical debt a program's graduates have carried
- –What you specifically will earn after a program
- –Causation — earnings reflect who enrolls, not only the program
- +Where recent graduate cohorts land roughly six months out (employed / continuing study / seeking)
- +How first-destination patterns differ by field of study
- +A base rate for 'still figuring it out shortly after graduating'
- –Your individual destination or timeline
- –Long-run career outcomes (it is a first-destination snapshot)
- +Longer-run employment and further-study patterns of bachelor's cohorts over years
- +How early choices relate to later enrollment and debt across a cohort
- +A multi-year view that a six-month snapshot cannot give
- –Your individual long-run path
- –A causal effect of any one decision
- +Population-level distributions of income, field-to-occupation flows, and demographics
- +How spread out outcomes are within a field (the variance, not just an average)
- +A reality check on how often a field leads to a given occupation
- –Any individual's income or path
- –Forward-looking projections (it is a current cross-section)
Decision frameworks
Framework-level reasoning lenses that apply to any decision.
- +Surfacing failure modes by imagining the route has already failed
- +Turning a vague worry into named, checkable risks
- +Designing early-warning signs before committing
- –The probability that the route will fail
- –Any individual outcome
- +Classifying a route as reversible (two-way) or hard-to-undo (one-way)
- +Matching deliberation to how reversible a step really is
- +Justifying a fast, cheap try when the door swings both ways
- –Whether the route is right for you
- –A forecast of the outcome
- +Anchoring on how a reference class of similar situations usually goes
- +Counterweighting an over-optimistic inside view of your own plan
- +Framing 'how long / how hard' more realistically
- –A precise number for your case
- –Removing the genuine uncertainty in your specific situation
- +Projecting forward to which path you'd most regret not trying
- +Separating fear-of-loss from fear-of-missing-out
- +Weighing action vs inaction regret explicitly
- –Which choice is correct for you
- –How you will actually feel later
- +Designing the cheapest test that could actually change the decision
- +Pre-committing a pass/fail signal before running it
- +Spending effort where it most reduces uncertainty
- –The result of the test before you run it
- –Certainty — only a cheaper way to learn
- +Why focused, feedback-rich effort compounds a distinctive edge
- +Structuring a long build around the hardest sub-skill
- +Explaining why depth is hard to copy
- –How fast you specifically will improve
- –That effort guarantees any outcome
- +Pairing a protected floor with a bounded, asymmetric upside bet
- +Capping downside while keeping exposure to a large upside
- +Justifying a fallback floor underneath a riskier move
- –The size of the upside in your case
- –That the structure removes risk — it bounds it
Startup / customer-discovery frameworks
For routes that involve building or validating something new.
- +Testing whether a real demand exists before building heavily
- +Getting out of the building to talk to target users early
- +Separating a vision from evidence that anyone wants it
- –Whether your specific venture will succeed
- –Market size or revenue figures
- +Asking about users' past behavior, not their opinions of your idea
- +Avoiding false validation from polite encouragement
- +Designing interview questions that surface real demand signals
- –Whether the idea will work
- –A quantitative demand estimate
A match / support score — not a probability
The evidence-fit score expresses how strongly a route matches your answers and the reference support behind it. It is not a probability of success and not a forecast. It is computed transparently in code from six components:
how much of your stated direction the route reflects
how well it fits the constraints you named (higher when reversible)
how strongly the strategy matches your dominant values
how many curated reference cards apply to the route
lowers the score when the route is inherently uncertain
lowers it for every assumption the system had to infer
The result is clamped to a sane band and shown with a qualitative label (Strong / Moderate / Loose), so the number never reads as certainty. It is a way to compare routes — not a claim about your outcome.
What this evidence layer is — and is not
Stated plainly, because honesty about limits is the point.
What it will not do
- •Not a prediction of what will happen to you.
- •Not a success probability or any kind of odds.
- •Not a personal outcome forecast — sources are occupation-, cohort-, population-, or framework-level only.
- •No private data required; the app never identifies real people.
- •Mock-first in production — the full experience runs locally with no API key.
Optional providers, honestly stated
- •The production demo defaults to this curated local evidence — it does not depend on live web search.
- •An optional live research / search provider can be connected when keys are present; the dispatcher upgrades transparently and is provider-ready.
- •If no provider is configured, the fallback stays deterministic — the same curated cards and local mock, so the demo never breaks.
No required environment variables. Nothing here sends your answers to a third party in the keyless demo.