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Every action the bot takes on your behalf either earns trust or spends it. A well-drafted reply that lands cleanly earns a small amount. A weird auto-send that confuses a friend spends a large amount. A wrongly deleted email spends a catastrophic amount. Trust is not a linear meter — it's asymmetric, path-dependent, and easily zeroed out. This means the design goal is not 'maximize helpful actions' but 'never take an action whose worst-case trust cost exceeds its expected value'. In practice: aggressive automations on low-stakes, high-frequency tasks (delete newsletter spam, categorize routine mail) earn small amounts of trust steadily; automations on high-stakes tasks (auto-reply to your investor) must be conservative or gated because a single misfire zeroes months of earned trust. The rest of the course is about building the second kind while never letting it destroy the first.
A trust-ledger primitive: every bot action gets a signed trust-delta based on outcome. Watch how a single high-stakes misfire wipes out weeks of steady wins.
from datetime import datetime, timedelta
from dataclasses import dataclass, field
@dataclass
class TrustEvent:
when: datetime
action: str
stakes: str # "low" | "medium" | "high" | "catastrophic"
outcome: str # "clean" | "minor_fix" | "user_override" | "burned"
delta: float = 0
def score(event: TrustEvent) -> float:
matrix = {
("low", "clean"): +0.5,
("low", "minor_fix"): +0.1,
("low", "user_override"): -0.5,
("medium", "clean"): +2.0,
("medium", "minor_fix"): +0.5,
("medium", "user_override"): -3.0,
("high", "clean"): +5.0,
("high", "minor_fix"): -1.0,
("high", "user_override"): -10.0,
("high", "burned"): -50.0,
("catastrophic", "burned"): -200.0,
}
return matrix.get((event.stakes, event.outcome), 0)
# Simulate a month of a good bot
events = []
for _ in range(120): # 120 low-stakes wins
events.append(TrustEvent(datetime.now(), "delete_newsletter", "low", "clean"))
for _ in range(40): # 40 medium wins
events.append(TrustEvent(datetime.now(), "draft_reply", "medium", "clean"))
for _ in range(5): # a couple minor edits
events.append(TrustEvent(datetime.now(), "draft_reply", "medium", "minor_fix"))
trust = sum(score(e) for e in events)
print(f"After 165 clean/minor-fix events: trust = {trust:+.1f}")
# One high-stakes misfire
events.append(TrustEvent(datetime.now(), "auto_reply_investor", "high", "burned"))
trust = sum(score(e) for e in events)
print(f"After one high-stakes misfire: trust = {trust:+.1f}")
print("\nDesign implication: high-stakes actions MUST be gated by grace window + escalation.")
python3 main.py