smc

name	smc
description	An exhaustive inventory of search methods found in nature, science, technology, and human systems.
output	{"format":"prose"}

Comprehensive Search Methods Catalog

Input: $ARGUMENTS

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Overview

An exhaustive inventory of search methods found in nature, science, technology, and human systems. Understanding these methods enables borrowing strategies across domains.

Meta-question: Is intelligence just "how well you can search"?

Steps

Step 1: Identify the Search Problem

1. What are you searching FOR? (solution, information, option, path, configuration)
2. What is the search SPACE? (finite/infinite, discrete/continuous, structured/unstructured)
3. What CONSTRAINTS exist? (time, resources, access, knowledge)
4. What does SUCCESS look like? (exact match, good enough, optimal, novel)

Step 2: Catalog of Search Methods by Domain

Exhaustive / Systematic:

• Brute force enumeration — try everything
• Grid search — sample at regular intervals
• Branch and bound — eliminate provably bad regions
• Backtracking — try, fail, undo, try different path
• Dynamic programming — break into overlapping subproblems

Heuristic / Guided:

• Hill climbing — always move toward improvement
• Simulated annealing — accept worse moves early, fewer later
• Tabu search — remember where you've been, don't repeat
• Beam search — keep top-N candidates, expand all
• A* / best-first — use estimated distance to guide exploration

Evolutionary / Population:

• Genetic algorithms — mutate, crossover, select
• Particle swarm — each agent influenced by personal and group best
• Ant colony — pheromone trails reinforce good paths
• Differential evolution — perturb using differences between candidates

Probabilistic / Statistical:

• Monte Carlo — random sampling
• Bayesian optimization — model the objective, sample where uncertain
• MCMC (Markov Chain Monte Carlo) — random walk biased toward good regions
• Thompson sampling — sample from posterior, act on sample
• Random restart — run local search many times from random starts

Adversarial / Game-Theoretic:

• Minimax — assume opponent plays optimally
• Alpha-beta pruning — skip branches that can't affect outcome
• Nash equilibrium search — find stable strategy profiles
• Regret minimization — minimize worst-case regret over time

Learning / Adaptive:

• Reinforcement learning — reward good paths, punish bad ones
• Active learning — query the most informative examples
• Curriculum learning — start easy, increase difficulty
• Transfer learning — apply learned search strategy to new domain

Biological / Natural:

• Chemotaxis — follow gradient (bacteria)
• Lévy flights — short local searches + occasional long jumps (foraging)
• Stigmergy — modify environment to guide future search (ants, termites)
• Immune system — generate random antibodies, amplify matches
• Neural search — pattern completion from partial cues (brain)

Social / Human:

• Ask an expert — skip search, use someone else's result
• Crowdsourcing — distribute search across many agents
• Market mechanisms — price signals aggregate distributed information
• Gossip protocols — spread information through local exchanges
• Institutional memory — search organizational records

Constraint-Based:

• SAT solvers — systematic logical constraint satisfaction
• Linear programming — optimize within linear constraints
• Constraint propagation — eliminate impossible values before searching

Step 3: Match Methods to Problem Characteristics

Problem Characteristic	Best Methods
Small search space	Exhaustive, branch and bound
Large smooth space	Hill climbing, gradient descent
Large rugged space	Simulated annealing, genetic algorithms
Unknown landscape	Bayesian optimization, random restart
Adversarial	Minimax, regret minimization
Time-constrained	Heuristic, beam search, ask expert
Need diversity	Population-based, random restart
Need optimality proof	Branch and bound, LP, exhaustive
Sequential decisions	RL, MCTS, bandit methods
Social/organizational	Crowdsource, expert, market

Step 4: Apply to Input

1. Classify the input's search problem (from Step 1)
2. Match to appropriate methods (from Step 3)
3. For each recommended method:
   • How would it apply concretely?
   • What resources does it require?
   • What are its failure modes?
4. Recommend primary method + backup

Step 5: Cross-Domain Transfer

1. Is the input's domain using only its "native" search methods?
2. What methods from OTHER domains might apply?
3. Which cross-domain transfer is most promising?
4. → INVOKE: /cda (cross-domain analogy) if transfer looks productive

Step 6: Report

SEARCH METHOD ANALYSIS:
Problem type: [classification]
Search space: [size, structure, smoothness]
Constraints: [time, resources, knowledge]

Recommended methods:
1. [Primary] — why it fits, how to apply
2. [Backup] — when to switch to this
3. [Cross-domain] — borrowed from [domain], novel application

Methods to AVOID: [which and why]

When to Use

• Stuck on a problem and need a new approach
• Want to systematically consider all search strategies
• Looking for cross-domain inspiration
• Designing an algorithm or process

Verification

• Search problem clearly characterized
• Multiple method categories considered
• Methods matched to problem characteristics
• Cross-domain transfer explored
• Failure modes of recommended methods noted