Multi-objective Pareto Genetic Algorithms + EVPI

1. Multi-Objective Nature
- We have at least two major objectives, possibly more:
- Maximize dietary diversity and traditional knowledge for local communities (human well-being goal)
- Maximize conservation of plants, animals, and ecosystems (ecological goal)
- Minimize costs for park management
- Balance species dynamics (e.g., control invasive species or overabundant species via sustainable gathering)

These objectives can conflict, align, or both

2. Decision Options (Management Techniques)
We have different levels of access and gathering as management levers:
- High access, moderate gathering
- Low access, low gathering
- Selective gathering (invasives allowed, vulnerable natives protected)
- Temporal restrictions (seasonal gathering only)
- Spatial restrictions (zones where gathering is allowed)

Each of these would lead to different outcomes across our objectives.

3. Uncertainty
Our Monte Carlo (MC) model captures uncertainties in:
- Plant population dynamics
- Human behavior and gathering rates
- Dietary intake changes
- Cost estimates
- Ecological responses (e.g., resilience, regeneration)

4. Using Pareto GA + EVPI

We can structure the whole decision support like this:

- Run the MC simulation for different management strategies
- Use Pareto GA to find non-dominated management strategies (best trade-offs) across conservation, dietary diversity, costs, etc.
- For each Pareto solution, calculate EVPI: What would be the value of knowing uncertain parameters perfectly? (e.g., plant regrowth rates, real dietary benefits, exact cost of enforcement)
- Interpret EVPI: High EVPI points to where better research or monitoring would help decisions the most. Low EVPI means we can decide confidently even without more information.
- Decision support: Give decision-makers a Pareto front of strategies, along with EVPI indicators for each, helping them choose wisely based on uncertainty and trade-offs.

In our case, there’s also an interesting "win-win" potential—gathering might help conservation in some cases (invasive species control, balancing ecosystem dynamics). This nonlinear, non-obvious benefit is exactly the kind of thing that Pareto exploration + Monte Carlo uncertainty analysis + EVPI can reveal.