outcome-determinism

name	outcome-determinism
description	Protocol Type Trigger outcome_determinism - detected when EITHER of these code patterns are present - - Selection from finite depletable pool with fallback behavior (while(full)...

Injectable Skill: Outcome Determinism - Selection Fairness & Strategic Timing

Protocol Type Trigger: outcome_determinism - detected when EITHER of these code patterns are present:

• Selection from finite depletable pool with fallback behavior (while(full) next, modulo with shrinking domain, category/slot/validator selection)
• Time-gated actions with observable default/fallback outcomes (deadline, claimPeriod, defaultSelection, expiry + fallback path that computes from predictable state) Inject Into: Breadth agents, depth-edge-case Language: All (rational actor patterns are chain-agnostic) Finding prefix: [OD-N] Added in: v1.1

NOTE: Callback selective revert (Section 1a of the original skill) and RNG consumption enumeration (Section 1b) are now ALWAYS-ON checks in the depth agent template - they do not require this injectable to trigger.

What This Injectable Adds (beyond always-on checks)

The always-on depth template already covers:

• Callback selective revert: Full taxonomy of external execution transfers (ERC-721/1155/777/1363, flash loans, hooks, low-level calls, ETH transfers) - see depth-templates.md "CALLBACK SELECTIVE REVERT ANALYSIS"
• Tainted source consumption enumeration: RNG/oracle consumed by multiple functions → rate severity by worst consumer - see depth-templates.md PART 1 item 6

This injectable adds analysis that only applies when SPECIFIC structural patterns exist:

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1. Selection Fairness Under Constraint Changes

For every selection algorithm operating on a set that can shrink, grow, or be modified between selections:

1a. Probability Redistribution on Depletion

Selection Function	Pool Size	Depletion Mechanism	Fallback Behavior	Redistribution Fair?

Fairness test: When element X is removed from a pool of N:

• Fair: Each remaining element gets probability 1/(N-1) - uniform redistribution
• Biased: Sequential fallback (index++, next slot) - adjacent element gets doubled probability
• Broken: Infinite loop (no exit when all depleted) or unreachable elements

Common biased patterns across protocol types:

• while (slot_full) { index++ } - next-in-line gets 2/N probability (NFT categories, validator slots, LP positions)
• if (depleted) skip - skipped element's share goes entirely to next check, not distributed
• modulo(N) where N decreases - specific indices get boosted as N shrinks

Quantify: At each depletion level (1-of-N, N/2-of-N, N-1-of-N), what is the maximum probability deviation from uniform? Is it exploitable (can the actor influence which elements deplete first)?

Tag: [VARIATION:pool N→N-K → element X probability 1/N→{new_prob} → bias={factor}x]

1b. Admin Parameter Regression on Selection Algorithms

For bounded parameters (caps, limits, quotas) where the bound can be LOWERED below already-accumulated state and the bound is used by a selection/iteration algorithm:

Parameter	Setter	Current Accumulated	Can Set Below Accumulated?	Selection Algorithm Affected	What Breaks?

This extends Rule 14 (Setter Regression) specifically for selection/allocation algorithms: when a cap is lowered below the current count, does the selection algorithm's termination condition still hold? (e.g., while (count == max) becomes unreachable when count > max)

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2. Strategic Timing - Delay, Front-Run, Sequence

Core question: For each user-facing action, is the TIMING of the action exploitable by a rational self-interested actor?

2a. Delay-vs-Act Rationality

For every time-gated action with a default or fallback:

Action	Active Path	Default/Fallback Path	Default Predictable?	Default Sometimes Better?	Delay Cost

A rational actor will delay when ALL THREE hold:

1. The default outcome is predictable (computed from observable on-chain state, not future unknowns)
2. The default is sometimes better than the active choice (different formula, different prices, different parameters)
3. The cost of waiting (opportunity cost, gas, penalty) is lower than the expected value gain

Cross-protocol examples:

• DeFi: Defer unstaking claim when penalty decreases over time and rate may improve
• NFT: Defer coin/trait claiming when default allocation uses more favorable prices than current
• Governance: Defer vote to observe other votes and vote strategically
• Staking: Defer withdrawal when accrued rewards during delay exceed the withdrawal amount
• Auctions: Defer bid to last block to prevent counter-bidding (sniping)

Methodology:

1. Identify the active path (user calls function before deadline)
2. Identify the default path (what happens if user doesn't act - system assigns default, claim expires, fallback triggers)
3. Is the default outcome computable from CURRENT on-chain state? (If it depends on future block hash or unresolved oracle → not predictable → no strategic edge)
4. Compare: under what market/state conditions is default > active? How often do those conditions occur?
5. What does delay cost? (forfeiture, penalty, opportunity cost, gas savings)

Tag: [TRACE:action={fn} deadline={T} → default={fallback_fn} → computed_from={state_vars} → predictable={YES/NO} → default_better_when={condition}]

2b. Admin Action Information Asymmetry

For every admin-settable parameter that affects pending or future user operations:

Parameter	Setter	Timelock?	Event Emitted?	Affects Pending Operations?	Asymmetry Window

Applies universally - any onlyOwner/onlyAdmin setter that changes a parameter used in user-facing calculations creates a window where the admin knows the new value but users don't.

Severity factors:

• Instant setter (no timelock): 1-block asymmetry - exploitable via same-block front-running
• No event emission: window extends until users discover change via failed transaction or manual inspection
• Retroactive effect on pending state: admin changes rules for operations already in progress
• Combined (instant + no event + retroactive): maximum exploitation window

2c. Sequence-Dependent Outcomes

For actions where the ORDER of multiple actors matters:

Action	Same-Block Ordering Matters?	Cross-Block Ordering Matters?	First-Mover Advantage?	Last-Mover Advantage?

Check: Can an actor observe others' pending actions (mempool) and position before/after them for profit?

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Key Questions (must answer all)

1. For each selection from a shrinking pool: does probability redistribute uniformly? Quantify the bias.
2. For each time-gated action: is the default/fallback predictable and sometimes better than active choice?
3. For each admin setter: what is the information asymmetry window? Does it affect pending operations?
4. For each multi-actor sequence: can ordering be exploited?

Common False Positives

• Truly unpredictable defaults: If fallback depends on future block hash or external oracle not yet published, strategic delay has no edge
• Equal-value outcomes in selection: If all pool elements have identical economic value, selection bias has no incentive
• Timelock-protected setters: If admin changes require N-block delay with public announcement, asymmetry is mitigated (but verify ALL setters have the timelock, not just some)
• Uniform delay cost: If delaying always costs more than the potential gain, rational delay is not profitable

Step Execution Checklist (MANDATORY)

Section	Required	Completed?	Notes
1a. Depletion Redistribution	IF selection from finite/depletable pool		Quantify bias at each depletion level
1b. Parameter Regression on Selection	IF admin can lower cap below accumulated AND cap used by selection algo		Extends Rule 14
2a. Delay-vs-Act Rationality	IF time-gated action with default/fallback		Check all three conditions
2b. Admin Information Asymmetry	IF admin setter affects pending user operations		Check timelock AND event emission
2c. Sequence-Dependent Outcomes	IF multiple actors' ordering affects results		Check same-block and cross-block