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fix: resolve remaining Axiology test failures (Pareto regression + value/fairness bugs) #17

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Merged
hyperpolymath merged 1 commit into from
Jun 13, 2026
Merged

fix: resolve remaining Axiology test failures (Pareto regression + value/fairness bugs) #17

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13 changes: 5 additions & 8 deletions src/optimization.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -57,16 +57,14 @@ function value_score(value::Value, state::Dict)::Float64
end
end

# Normalize welfare value to [0,1] using max_welfare from state if provided,
# otherwise clamp the raw value to [0,1].
# Normalize welfare value to [0,1] using max_welfare from state if provided.
# Without max_welfare, return the raw welfare value so callers (e.g. pareto_frontier)
# can compare solutions meaningfully rather than collapsing all positive values to 1.0.
max_welfare = get(state, :max_welfare, nothing)
if !isnothing(max_welfare) && max_welfare > 0.0
return min(1.0, max(0.0, welfare_val / max_welfare))
else
# For egalitarian (negative values mean inequality), map to [0,1]:
# 0.0 = perfect equality, negative = inequality → score = max(0, 1 + welfare_val)
# For utilitarian/rawlsian without max_welfare, clamp to [0,1].
return min(1.0, max(0.0, welfare_val))
return welfare_val
end

elseif value isa Profit
Expand Down Expand Up @@ -115,7 +113,7 @@ function weighted_score(values::Vector{<:Value}, state::Dict)::Float64
return weighted_sum / total_weight
end

function normalize_scores(scores::AbstractVector{<:Real})::Vector{Float64}
function normalize_scores(scores::AbstractVector)::Vector{Float64}
if isempty(scores)
throw(ArgumentError("Cannot normalize an empty vector of scores."))
end
Expand Down Expand Up @@ -190,4 +188,3 @@ function pareto_frontier(system::Dict, values::AbstractVector{<:Value})::Vector{
# Call the primary pareto_frontier method
return pareto_frontier(solutions, values)
end

5 changes: 2 additions & 3 deletions src/welfare.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,12 @@

using Statistics

function utilitarian_welfare(utilities::AbstractVector{<:Real})::Float64
function utilitarian_welfare(utilities::AbstractVector)::Float64
isempty(utilities) && return 0.0 # Return 0.0 for empty utility vector
return sum(utilities)
end

function rawlsian_welfare(utilities::AbstractVector{<:Real})::Float64
function rawlsian_welfare(utilities::AbstractVector)::Float64
isempty(utilities) && error("Cannot compute Rawlsian welfare for an empty utility vector.")
return minimum(utilities)
end
Expand Down Expand Up @@ -190,4 +190,3 @@ function verify_value(value::Safety, proof::Dict)::Bool

return verified
end

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