yogsoth-ai Temporal Sequencing
Purpose
Determine the optimal order, timing, and phasing of portfolio elements when dependencies, learning effects, and option value make sequencing matter as much as selection.
When to use
- Candidates have dependencies (A must precede B)
- Early investments create options for later ones
- Information gained from early bets informs later decisions
- Budget is released in phases over time
- Timing affects value (first-mover advantage, market windows)
Budget
| Dimension | Target |
|---|---|
| Candidates sequenced | 8-20 |
| Time periods modeled | 3-6 phases |
| Dependencies mapped | all critical |
| Decision points identified | >=2 stage-gates |
State Ledger
| Field | Type | Description |
|---|---|---|
| candidates | list | Candidates with timing attributes |
| dependencies | graph | Precedence relationships between candidates |
| phases | list | Time periods with budget allocations |
| option_values | list | Value of information/flexibility from early bets |
| sequence | list | Ordered plan with stage-gates |
Available Tactics
| Tactic | When |
|---|---|
| pareto-frontier-construction | Trading off speed vs cost vs risk in sequencing |
| scenario-stress-testing | Testing sequence robustness under timeline uncertainty |
Available SOPs
| SOP | Purpose |
|---|---|
| objective-definition | Define sequencing objectives and constraints |
| optimization-run | Find optimal sequences |
| scenario-construction | Model timeline uncertainties |
| portfolio-evaluation-per-scenario | Test sequence under delays/accelerations |
| portfolio-synthesis | Synthesize robust sequence recommendation |
Execution Guidance
- Map dependencies and precedence constraints
- Identify option value — which early investments create future flexibility
- Define phase budgets and stage-gate criteria
- Optimize sequence considering dependencies, option value, and constraints
- Stress-test sequence against timeline uncertainties
- Build staged investment plan with decision points
Output Format
strategy: temporal-sequencing
sequence:
- phase: 1
candidates: [<name1>, <name2>]
budget: <amount>
stage_gate: <criteria for proceeding>
- phase: 2
candidates: [<name3>]
budget: <amount>
depends_on: [<phase 1 outcomes>]
critical_path: [<ordered candidates>]
option_values:
- candidate: <name>
options_created: [<future possibilities>]
method_used: <real-options|critical-path|staged-investment>
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| pareto-frontier-construction | Build the Pareto frontier from multi-objective optimization, visualize trade-offs, and select a portfolio from non-dominated solutions. |
| scenario-stress-testing | Construct distinct future scenarios, evaluate portfolio performance under each, and identify vulnerabilities and robustness characteristics. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| objective-definition | Define optimization objectives, constraints, and trade-off preferences from context and candidate information. |
| optimization-run | Execute multi-objective optimization on candidates to produce a Pareto front of non-dominated solutions. |
| portfolio-evaluation-per-scenario | Evaluate a specific portfolio's performance metrics and vulnerabilities under a given scenario. |
| portfolio-synthesis | Synthesize all per-scenario evaluations into a final portfolio recommendation with robustness score and actionable guidance. |
| scenario-construction | Construct distinct future scenarios spanning key uncertainties for portfolio stress testing. |