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Geometric economics: multi-dimensional decision manifolds, A* pathfinding, and Bond Geodesic Equilibrium

Project description

eris-econ

CI PyPI Python License: MIT

Geometric economics: multi-dimensional decision manifolds, A* pathfinding, and Bond Geodesic Equilibrium.

Overview

Classical economics reduces human decisions to scalar utility maximization. This library implements the geometric economics framework from Bond (2026), which models economic decisions as pathfinding on a 9-dimensional decision manifold.

The core insight: Homo economicus is not wrong because humans are irrational — it's incomplete because it computes on a projected subspace of the actual decision manifold. Projecting from 9 dimensions to 1 (monetary utility) destroys information that is mathematically irrecoverable.

The Nine Dimensions

Every economic state is a point in R^9:

Dim Name Examples
d₁ Consequences monetary cost, material outcome, expected value
d₂ Rights property rights, contractual obligations
d₃ Fairness distributional justice, reciprocity
d₄ Autonomy freedom of choice, voluntariness
d₅ Privacy/Trust information asymmetry, fiduciary duty
d₆ Social Impact externalities, reputation
d₇ Virtue/Identity self-image, moral identity
d₈ Legitimacy institutional trust, rule compliance
d₉ Epistemic information quality, confidence

Dimensions d₁–d₄ are transferable (conserved in bilateral exchange). Dimensions d₅–d₉ are evaluative (not conserved, enabling mutual gains from trade).

Key Concepts

Bond Geodesic

The optimal path on the decision manifold from current state to goal, minimizing Mahalanobis distance + boundary penalties. This is computed via A* search, where:

  • g(n) = accumulated cost (System 2: deliberate calculation)
  • h(n) = heuristic estimate (System 1: moral intuition)

Bond Geodesic Equilibrium (BGE)

Generalization of Nash equilibrium to multi-dimensional manifolds. Each agent minimizes behavioral friction on their own decision complex. Reduces to Nash when all non-monetary dimensions vanish.

Emergent Behavioral Phenomena

These are not ad-hoc biases — they emerge geometrically:

  • Loss aversion (λ ≈ 2.25): losses traverse more dimensions than gains
  • Reference dependence: distance measured from current state on manifold
  • Endowment effect: ownership activates rights + identity dimensions
  • Framing effects: gauge transformations on the description basis

Installation

pip install eris-econ

Or for development:

git clone https://github.com/ahb-sjsu/eris-econ.git
cd eris-econ
pip install -e ".[dev]"

Quick Start

from eris_econ.games import ultimatum_game
from eris_econ.pathfinding import astar

# Build the proposer's decision complex
E = ultimatum_game(stake=10.0)

# Find the optimal offer (Bond Geodesic)
goals = {f"offer_{p}" for p in [0, 10, 20, 30, 40, 50]}
result = astar(E, "start", goals)

print(f"Optimal choice: {result.path[-1]}")  # ~offer_40 (not offer_0!)
print(f"Total behavioral friction: {result.total_cost:.3f}")
from eris_econ.behavioral import compute_loss_aversion
import numpy as np

# Compute emergent loss aversion from metric structure
sigma = np.eye(9)
sigma[0, 6] = 0.3  # consequences-identity coupling
sigma[6, 0] = 0.3
lambda_ratio = compute_loss_aversion(sigma)
print(f"Loss aversion λ = {lambda_ratio:.2f}")  # > 1.0

Modules

Module Description
dimensions.py The 9 economic dimensions, transferable vs evaluative classification
manifold.py Economic Decision Complex — weighted directed graph on R^9
metrics.py Mahalanobis distance, boundary penalties, edge weights
pathfinding.py A* search for Bond Geodesic computation
equilibrium.py Bond Geodesic Equilibrium via iterated best response
games.py Standard games: ultimatum, dictator, prisoner's dilemma, public goods
behavioral.py Loss aversion, reference dependence, endowment effect, framing
calibration.py Parameter estimation for Σ and β from behavioral data
welfare.py Multi-dimensional Pareto optimality and social welfare

Citation

If you use this library in academic work, please cite:

@article{bond2026geometric,
  title={Geometric Economics: Multi-Dimensional Decision Manifolds and Bond Geodesic Equilibrium},
  author={Bond, Andrew H.},
  journal={Journal of Economic Theory},
  year={2026}
}

License

MIT

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