Robert Helewka
a967f73d09
Add CLAUDE.md defining the Palladium master notebook conventions and Red Panda Approval criteria, plus a review-notebook slash command for LLM-driven notebook review. Expand .gitignore to block client/engagement documents and generated exports, keeping masters client-clean while allowing text/image sources. Normalize slider widget numeric values from floats to integers in notebook JSON.
229 lines
10 KiB
Python
229 lines
10 KiB
Python
"""Value-at-stake math: driver values, capability capping, unlock sequence.
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The pipeline (build spec §6):
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1. Each configured value driver yields a theoretical annual value range
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from the operational baseline (dispatch on ``driver.kind``).
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2. Theoretical annual value = sum of drivers.
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3. The **weakest foundational competency score** selects a realization
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band from the capping heuristic.
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4. ``realizable_18mo = theoretical × realization_factor × 1.5``
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(18 months of annual run-rate).
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5. Trapped value (annual) = theoretical − realizable run-rate. Range
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pairing is conservative-consistent: the low trapped estimate assumes
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the low theoretical *and* the high realization factor, and vice versa.
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6. Binding constraints = every foundational competency sitting at the
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weakest score.
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7. Unlock sequence = up to three **tier lifts**: raise the whole binding
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set one level, recompute the band, attribute the delta. When several
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competencies tie at the weakest level a single-competency lift would
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honestly unlock nothing — the set is the move (see UnlockMove docs).
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Guard rails: every output is a range; 🔴-unknown inputs raise warnings on
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the result; money *display* is capped at two significant figures
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(:func:`money`) while raw floats stay exact in exports.
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"""
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from __future__ import annotations
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from .models import (
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CompetencyScore,
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DiagnosticConfig,
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DriverValue,
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OperationalBaseline,
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UnlockMove,
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ValueAtStake,
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ValueDriver,
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)
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#: 18 months expressed in years of annual run-rate.
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MONTHS_18 = 1.5
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#: How many unlock moves the sequence proposes.
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MAX_UNLOCK_MOVES = 3
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# ── Money display (guard rail: ≤ 2 significant figures) ──────────────
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def _round_2sf(v: float) -> float:
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if v == 0:
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return 0.0
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from math import floor, log10
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exp = floor(log10(abs(v)))
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return round(v, -exp + 1)
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def money(v: float) -> str:
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"""House money format, capped at two significant figures: $2.5M, $950K."""
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sign, a = ("-" if v < 0 else ""), _round_2sf(abs(v))
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if a >= 1e6:
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m = a / 1e6
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return f"{sign}${m:,.1f}M" if m < 10 else f"{sign}${m:,.0f}M"
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if a >= 1e3:
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return f"{sign}${a / 1e3:,.0f}K"
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return f"{sign}${a:,.0f}"
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def html_money(v: float) -> str:
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"""Plotly text with two or more bare ``$`` triggers MathJax math mode —
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annotations holding several amounts must use the HTML entity instead."""
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return money(v).replace("$", "$")
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# ── Driver math (dispatch on kind) ───────────────────────────────────
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def driver_value(driver: ValueDriver, baseline: OperationalBaseline) -> DriverValue:
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"""Theoretical annual value range for one configured driver."""
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if driver.kind == "containment_lift":
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if driver.lift_range_pts_low is None or driver.lift_range_pts_high is None:
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raise ValueError(f"driver {driver.id}: containment_lift needs lift_range_pts_low/high")
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low = baseline.annual_contact_volume * driver.lift_range_pts_low \
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* baseline.blended_cost_per_contact
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high = baseline.annual_contact_volume * driver.lift_range_pts_high \
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* baseline.blended_cost_per_contact
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elif driver.kind == "aht_reduction":
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# volume × (AHT × pct) seconds saved × ($/contact ÷ AHT) per second
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# — the baseline AHT cancels: volume × $/contact × pct.
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if driver.reduction_pct_low is None or driver.reduction_pct_high is None:
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raise ValueError(f"driver {driver.id}: aht_reduction needs reduction_pct_low/high")
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low = baseline.annual_contact_volume * baseline.blended_cost_per_contact \
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* driver.reduction_pct_low
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high = baseline.annual_contact_volume * baseline.blended_cost_per_contact \
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* driver.reduction_pct_high
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elif driver.kind == "attrition_reduction":
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if driver.reduction_pct_low is None or driver.reduction_pct_high is None:
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raise ValueError(f"driver {driver.id}: attrition_reduction needs reduction_pct_low/high")
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cost_per_replacement = driver.cost_per_replacement_default or 0.0
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low = baseline.agent_headcount * baseline.annual_attrition_rate \
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* driver.reduction_pct_low * cost_per_replacement
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high = baseline.agent_headcount * baseline.annual_attrition_rate \
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* driver.reduction_pct_high * cost_per_replacement
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else: # pragma: no cover — Literal already restricts kinds
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raise ValueError(f"driver {driver.id}: unknown kind {driver.kind}")
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return DriverValue(driver_id=driver.id, name=driver.name,
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theoretical_low=low, theoretical_high=high)
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# ── Capping ──────────────────────────────────────────────────────────
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def weakest_foundational_score(config: DiagnosticConfig,
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scores: list[CompetencyScore]) -> int:
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by_id = {s.competency_id: s.score for s in scores}
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missing = [c for c in config.foundational_competencies if c not in by_id]
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if missing:
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raise ValueError(f"foundational competencies unscored: {missing}")
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return min(by_id[c] for c in config.foundational_competencies)
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def binding_constraints(config: DiagnosticConfig,
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scores: list[CompetencyScore]) -> list[str]:
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"""Foundational competencies sitting at the weakest score, config order."""
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weakest = weakest_foundational_score(config, scores)
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by_id = {s.competency_id: s.score for s in scores}
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return [c for c in config.foundational_competencies if by_id[c] == weakest]
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# ── Unlock sequence (tier lifts of the binding set) ──────────────────
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def _tier_move(config: DiagnosticConfig, level: int, members: list[str],
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th_low: float, th_high: float) -> UnlockMove:
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band_now = config.capping_heuristic[level]
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band_next = config.capping_heuristic[level + 1]
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costs = {m: config.lift_cost(m, level) for m in members}
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missing = [m for m, c in costs.items() if c is None]
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note = ""
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if len(members) > 1:
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note = "joint lift — the tied competencies must move together to shift the cap"
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if missing:
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note = (note + "; " if note else "") + \
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f"cost not configured for: {', '.join(missing)}"
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have_all = not missing
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return UnlockMove(
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competency_ids=members,
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current_level=level,
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target_level=level + 1,
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est_cost_low=sum(c.cost_low for c in costs.values() if c) if have_all else None,
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est_cost_high=sum(c.cost_high for c in costs.values() if c) if have_all else None,
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est_weeks=max((c.weeks for c in costs.values() if c), default=None) if have_all else None,
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value_unlocked_low=th_low * (band_next.realized_low - band_now.realized_low),
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value_unlocked_high=th_high * (band_next.realized_high - band_now.realized_high),
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note=note,
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)
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def unlock_sequence(config: DiagnosticConfig, scores: list[CompetencyScore],
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th_low: float, th_high: float,
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max_moves: int = MAX_UNLOCK_MOVES) -> list[UnlockMove]:
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"""Up to ``max_moves`` sequential tier lifts of the binding set.
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Each move lifts every foundational competency at the current weakest
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level by one level (weeks = the longest workstream, run in parallel;
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costs summed). Value unlocked is the annual realizable delta from the
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capping-band shift. Moves stay in sequence order — each one is the
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prerequisite of the next, so ranking them against each other would be
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meaningless; the ratio walk (value/cost declining) is the story.
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"""
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if th_low == 0 and th_high == 0:
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return []
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current = {s.competency_id: s.score for s in scores
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if s.competency_id in config.foundational_competencies}
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moves: list[UnlockMove] = []
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for _ in range(max_moves):
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level = min(current.values())
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if level >= 5:
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break
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members = [c for c in config.foundational_competencies
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if current[c] == level]
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moves.append(_tier_move(config, level, members, th_low, th_high))
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for m in members:
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current[m] = level + 1
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return moves
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# ── The full computation ─────────────────────────────────────────────
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def value_at_stake(config: DiagnosticConfig, baseline: OperationalBaseline,
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scores: list[CompetencyScore]) -> ValueAtStake:
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"""Steps 1–8 of the build spec, as one call. See module docstring."""
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drivers = [driver_value(d, baseline) for d in config.value_drivers]
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th_low = sum(d.theoretical_low for d in drivers)
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th_high = sum(d.theoretical_high for d in drivers)
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weakest = weakest_foundational_score(config, scores)
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band = config.capping_heuristic[weakest]
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warnings: list[str] = []
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if not config.value_drivers:
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warnings.append(
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f"config '{config.industry}' has no value drivers — "
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"value-at-stake is zero (stub config)")
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used_fields = sorted({d.baseline_field for d in config.value_drivers}
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| ({"annual_contact_volume", "blended_cost_per_contact"}
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if config.value_drivers else set()))
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for f in used_fields:
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if baseline.confidence_for(f) == "unknown":
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warnings.append(
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f"baseline input '{f}' is flagged 🔴 unknown — "
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"the ranges below inherit that uncertainty")
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return ValueAtStake(
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theoretical_annual_value_low=th_low,
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theoretical_annual_value_high=th_high,
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realizable_18mo_low=th_low * band.realized_low * MONTHS_18,
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realizable_18mo_high=th_high * band.realized_high * MONTHS_18,
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trapped_value_low=th_low * (1 - band.realized_high),
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trapped_value_high=th_high * (1 - band.realized_low),
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binding_constraints=binding_constraints(config, scores),
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unlock_sequence=unlock_sequence(config, scores, th_low, th_high),
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weakest_foundational_score=weakest,
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realization_factor_low=band.realized_low,
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realization_factor_high=band.realized_high,
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driver_values=drivers,
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warnings=warnings,
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)
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