value-maximization

name	value-maximization
description	Maximize total portfolio value within constraints using Knapsack, Linear programming, Cost-benefit analysis, and NPV ranking methods.
dependencies	{"tactics":["pareto-frontier-construction"],"sops":["objective-definition","optimization-run","pareto-visualization","selection-from-frontier"]}

Value Maximization

Purpose

Select the portfolio subset that maximizes aggregate value (ROI, impact, utility) subject to resource constraints. Applies when the primary goal is getting the most out of a limited budget.

When to use

• Fixed budget with many candidate investments
• Clear value metrics exist for each candidate
• Constraints are well-defined (cost, time, capacity)
• Goal is maximum total return, not diversity or risk management

Budget

Dimension	Target
Candidates evaluated	8-20
Constraints modeled	1-5
Value metrics	1-3 per candidate
Solutions compared	>=5

State Ledger

Field	Type	Description
candidates	list	All candidate items with value and cost attributes
constraints	list	Budget, capacity, or other binding constraints
objective_function	string	How value is aggregated (sum, weighted sum, etc.)
optimal_solution	list	Selected portfolio maximizing value
value_achieved	number	Total value of selected portfolio

Available Tactics

Tactic	When
pareto-frontier-construction	Multiple value dimensions to trade off

Available SOPs

SOP	Purpose
objective-definition	Define what "value" means and what constraints bind
optimization-run	Run the optimization to find best portfolios
pareto-visualization	Visualize value trade-offs if multi-objective
selection-from-frontier	Pick final portfolio from candidates

Execution Guidance

1. Define value metric(s) and constraints via objective-definition
2. If single objective: solve as knapsack/LP directly
3. If multiple objectives: use pareto-frontier-construction tactic
4. Select from frontier based on stakeholder preferences
5. Validate selected portfolio against all constraints

Output Format

strategy: value-maximization
selected_portfolio:
  - candidate: <name>
    value: <score>
    cost: <cost>
total_value: <aggregate>
total_cost: <aggregate>
constraint_slack: <remaining budget>
method_used: <knapsack|LP|NPV>
confidence: <high|medium|low>

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.

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.
pareto-visualization	Create visual representation of the Pareto frontier showing trade-offs between objectives with narrative explanation.
selection-from-frontier	Select the final portfolio from the Pareto front by applying stakeholder preferences and decision criteria.