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name: forage-resources locale: caveman-ultra source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-24" description: > Apply ant colony optimization and foraging theory to resource search, exploration-exploitation tradeoffs, and distributed discovery. Covers scout deployment, trail reinforcement, diminishing returns detection, and adaptive foraging strategy selection. Use when searching a large solution space where brute-force enumeration is impractical, balancing investment between exploring new approaches and deepening known good ones, optimizing resource allocation across uncertain opportunities, or diagnosing premature convergence on local optima. license: MIT allowed-tools: Read metadata: author: Philipp Thoss version: "1.0" domain: swarm complexity: intermediate language: natural tags: swarm, foraging, ant-colony-optimization, exploration-exploitation

Forage Resources

Apply foraging theory + ant colony opt → systematically search, evaluate, exploit distributed resources — balance exploration unknown vs exploitation known yields.

Use When

• Search large solution space, brute-force impractical
• Balance invest between explore new vs deepen known good
• Optimize resource alloc across uncertain opportunities
• Design search strategies distributed teams/automated agents
• Diagnose premature convergence (stuck local optima) or perpetual wandering (never commit)
• Complement coordinate-swarm w/ specific resource-discovery patterns

In

• Required: Resource being sought (info, compute, talent, solutions, opportunities)
• Required: Search space (size, structure, known features)
• Optional: Current strategy + failure mode
• Optional: N available scouts/searchers
• Optional: Cost exploration vs cost exploitation failure
• Optional: Time horizon (short-term exploitation vs long-term exploration)

Do

Step 1: Map Landscape

Characterize resource env → select strategy.

1. Resource type + distribution:
   • Concentrated: cluster rich patches (talent in specific communities)
   • Distributed: spread evenly (bugs across codebase)
   • Ephemeral: appear + disappear (market opportunities)
   • Nested: rich patches contain sub-patches diff scales
2. Information landscape:
   • How much known about locations before foraging?
   • Scouts share info w/ foragers? (see coordinate-swarm for signal design)
   • Static or changing while foraging?
3. Cost structure:
   • Cost per scout deployed (time, compute, money)
   • Cost exploiting low-quality (opportunity cost)
   • Cost missing high-quality (regret)

→ Characterized landscape w/ distribution, info, cost. Determines foraging model.

If err: completely unknown → max exploration (all scouts, no exploit) for fixed budget → build initial map. Switch to model once character clear.

Step 2: Deploy Scouts w/ Trail Marking

Exploratory agents into search space + instructions mark what find.

1. Allocate scout % (start 20-30% of available)
2. Scout behavior:
   • Move through space randomized/systematic
   • Evaluate each location (quick not deep)
   • Mark discoveries w/ signal strength proportional to quality:
     • High quality → strong trail
     • Medium → moderate
     • Low → weak or no signal
   • Return info to collective (signal deposit, report, broadcast)
3. Scout pattern:
   • Random walk: unknown, uniform landscapes
   • Levy flight: long jumps + local clustering — patchy
   • Systematic sweep: grid/spiral — bounded, well-defined
   • Biased random: lean toward similar previous finds — clustered

→ Scouts deployed, depositing signals proportional to quality. Initial map emerges from reports.

If err: nothing initial sweep → (a) scout % too low (increase 50%), (b) wrong pattern (random walk → Levy flight for patchy), (c) quality miscalibrated (lower detection threshold).

Step 3: Trail Reinforcement

Positive feedback loops amplify successful paths, let unsuccessful fade.

1. Forager follows trail + finds good:
   • Reinforce signal (increase strength)
   • Reinforced → more foragers → more reinforcement → exploitation
2. Forager follows trail + finds nothing:
   • No reinforce (trail decays naturally)
   • Weakening → fewer foragers → fades → exploration resumes
3. Reinforcement params:
   • Deposit: proportional to quality
   • Decay rate: trails lose X%/time
   • Saturation cap: max strength (prevents runaway single path)

Trail Reinforcement Dynamics:
┌─────────────────────────────────────────────────────────────────────┐
│                                                                     │
│  Strong trail ──→ More foragers ──→ If good: reinforce ──→ EXPLOIT │
│       ↑                                                      │      │
│       │                              If bad: no reinforce    │      │
│       │                                     │                │      │
│       │                                     ↓                │      │
│  Decay ←── Weak trail ←── Fewer foragers ←── Trail fades    │      │
│       │                                                      │      │
│       ↓                                                      │      │
│  No trail ──→ Scouts explore ──→ New discovery ──→ New trail ↗      │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘

→ Self-regulating loop: good attract, poor abandoned. Balance via trail dynamics.

If err: all converge single trail (premature convergence) → decay too slow or cap too high. Increase decay, lower cap, or random exploration mandates (10% ignore trails). Fade too fast → reduce decay.

Step 4: Diminishing Returns

Monitor yields → know when shift exploit back to explore.

1. Track yield/effort each active site:
   • Increasing → healthy, continue
   • Flat → approach saturation, begin scouting alts
   • Decreasing → diminishing, reduce foragers, increase scouts
2. Marginal value theorem:
   • Compare current yield vs avg across known sites
   • Current drops below avg → time to leave
   • Factor travel cost (switching to new)
3. Trigger scouting waves:
   • Overall yield across all drops below threshold
   • Best-performing exploited longer than expected lifetime
   • Env change detected (new signals from unexplored areas)

→ Swarm shifts between exploit (known-good) + exploration (scouts dispersed), driven by monitoring not arbitrary schedules.

If err: stays depleted too long → marginal threshold too low or travel cost too high. Recalibrate via actual rates. Abandons good too early → threshold too sensitive, add smoothing window.

Step 5: Adapt Strategy

Select + switch strategies based on env feedback.

1. Match to landscape:
   • Rich, clustered: commit heavy discovered patches (high exploit)
   • Sparse, scattered: high scout ratio (high explore)
   • Volatile, changing: short decay, frequent scouting waves (adaptive)
   • Competitive: faster reinforcement, pre-emptive marking (territorial)
2. Monitor strategy-env mismatch:
   • High effort, low yield → too exploitative
   • High discovery, low follow-through → too exploratory
   • Oscillating yield → switching too aggressively
3. Adaptive switching:
   • Rolling avg explore-to-exploit ratio
   • Ratio drifts too far from optimal (by landscape type) → nudge back
   • Gradual transitions — abrupt cause coordination chaos

→ System adapts balance to env, maintains effectiveness as conditions change.

If err: adaptation unstable (oscillating) → damping: require mismatch persist N time units before shift. No strategy works → reassess Step 1 landscape, distribution may be more complex than assumed.

Check

• Landscape characterized (distribution, info, cost)
• Scout % + pattern defined + deployed
• Trail reinforcement loop functional (deposit, decay, saturation)
• Diminishing returns triggers rebalance exploit → explore
• Strategy-env match monitored + adaptive switching
• System recovers landscape changes (new/depleted)

Traps

• Premature convergence: All pile on first good find, ignore better. Cure: mandatory exploration %, trail saturation caps, decay.
• Perpetual exploration: Scouts find new but swarm never commits. Cure: lower quality threshold for reinforcement, reduce scout %.
• Ignore travel costs: Switching has cost. Constantly jumping similar-quality → waste travel > gain. Factor travel into marginal value.
• Static strategy dynamic landscape: Optimized for yesterday fails tomorrow. Build adaptation into loop not afterthought.
• Conflate scout + forager quality: Good scouts (broad, quick) + good foragers (deep, thorough) require diff skills. Don't force both roles.

→

• coordinate-swarm — foundational coordination underpinning signal design
• build-consensus — swarm must collectively agree which patches prioritize
• scale-colony — scaling operations as landscape/swarm grows
• assess-form — morphic for system current state, complementary to landscape
• configure-alerting-rules — alerting applicable to diminishing returns
• plan-capacity — capacity planning shares explore-exploit framing
• forage-solutions — AI self-application variant; maps ant colony to single-agent solution exploration w/ scout hypotheses + trail reinforcement