Computational Modeling of Post-Scarcity Economic Equilibria

As automation and artificial intelligence approach the potential for material post-scarcity, existing economic theory provides limited guidance for understanding the resulting equilibrium conditions and transition dynamics. This proposal outlines a comprehensive computational modeling framework to analyze post-scarcity economic systems, focusing on persistent constraints, emergent equilibria, and the evolutionary dynamics of institutional arrangements. Through agent-based modeling, game-theoretic analysis, and evolutionary algorithms, we aim to identify stable configurations for post-material-scarcity societies and the conditions under which they emerge.

1. Introduction and Motivation

The prospect of post-scarcity economics—where material goods become so abundant that traditional economic models based on resource limitations break down—is transitioning from science fiction to policy consideration. Current discussions of automation, universal basic income, and technological unemployment reflect growing recognition that fundamental economic assumptions may soon require revision. Recent advances in physics research, particularly in areas like quantum-coherent nuclear fusion ( see superfluid_fusion_proposal.md), suggest that material abundance through novel energy and matter synthesis may arrive sooner than anticipated.

However, existing analyses of post-scarcity scenarios suffer from three critical limitations: (1) insufficient attention to persistent constraints that remain binding even under material abundance, (2) lack of systematic analysis of possible equilibrium configurations and their stability properties, and (3) inadequate consideration of transition dynamics and path-dependent outcomes.

This research addresses these gaps through computational modeling that explicitly incorporates multiple constraint types, allows for complex agent interactions, and simulates evolutionary dynamics across institutional arrangements.

2. Theoretical Framework

2.1 Persistent Constraints in Post-Scarcity Systems

Even under conditions of material abundance, several categories of constraints remain binding:

Thermodynamic Constraints: Energy requirements and entropy increases create ultimate limits on production and consumption, establishing boundary conditions for any post-scarcity equilibrium.

Spatial and Temporal Constraints: Location-specific advantages, proximity effects, and finite human lifespans create inherent scarcities that resist technological solutions.

Cognitive and Attention Constraints: Human information processing limitations, decision-making capacity, and attention spans create bottlenecks in complex systems.

Coordination and Information Constraints: The computational and social overhead of coordinating large-scale systems creates scaling limits independent of material resources.

Social and Preference Constraints: Status competition, cultural heterogeneity, and preference evolution create new forms of scarcity and conflict even under material abundance. Information and Reality Constraints: As explored in [managedmanaged_reality_paper.mdnagement of information environments becomes a critical constraint in post-scarcity societies where traditional economic pressures no longer organize human behavior.

2.2 Potential Equilibrium Configurations

Our framework considers multiple possible equilibrium states:

• Labor-leisure equilibria where creative activity and leisure optimization become primary
• Status competition equilibria focused on positional goods and social hierarchy
• Innovation-driven equilibria organized around research and novel experience creation
• Curated reality equilibria where information management systems shape distinct economic experiences ( see [managed_realitymanaged_reality_paper.mdlogy

3.1 Agent-Based Modeling Framework

Core Architecture: Heterogeneous agents with differentiated utility functions, cognitive constraints, and resource endowments operating within multi-layer networks representing economic exchange, social influence, and information flows. Technical Implementation:

• Multi-threaded simulation engine using Julia for computational efficiency
• Graph-based network representation using NetworkX and GraphBLAS algorithms
• Distributed computing architecture for parameter sweeps across 10^6+ simulation runs
• CUDA acceleration for utility optimization and market clearing operations

Agent Types:

Agent Decision Models:

• Bounded rationality implemented via Monte Carlo Tree Search (MCTS) with depth limitations
• Reinforcement learning using Proximal Policy Optimization (PPO) for strategy adaptation
• Bayesian belief updating with sparse priors for information processing
• Attention allocation modeled as constrained optimization with diminishing returns

Environmental Dynamics:

Simulation Parameters:

• Time resolution: 1 month per tick, with 100-year simulation horizons
• Population scale: 10^4 to 10^6 agents with hierarchical abstraction for larger scales
• Network density: Small-world networks with configurable clustering coefficients (0.1-0.5)
• Technology advancement: Logistic and exponential curves with stochastic breakthrough events

3.2 Game-Theoretic Submodels

Status Competition Games: Positional goods markets with relative utility functions, tournament models for attention allocation, and costly signaling equilibria in social hierarchies. Mathematical Formulation:

• Utility functions: U(x_i, x_j) = α·v(x_i) - β·r(x_i, x_j) where v() is absolute value and r() is relative position
• Tournament payoffs modeled as Tullock contests with effort cost functions
• Signaling games with Bayesian Nash equilibria and costly verification mechanisms

Coordination Games: Public goods provision mechanisms, commons management with monitoring and sanctioning, and coalition formation for infrastructure investment. Implementation Details:

• Ostrom-inspired institutional analysis and development (IAD) framework
• Multi-level governance with nested Markov Decision Processes
• Reputation systems using distributed ledger simulation with Byzantine fault tolerance
• Coalition formation via hedonic games with transferable utility

Innovation Races: R&D competition with spillover effects, intellectual property strategy choices, and network effects in platform adoption. Algorithmic Approach:

• Patent race modeling using stochastic differential equations
• Knowledge diffusion via epidemic models on complex networks
• Platform competition using Bass diffusion with network externalities
• Open source dynamics using public goods games with reputation benefits

3.3 Evolutionary Dynamics

Cultural Evolution Module: Group selection on institutional arrangements, individual learning and social transmission of economic strategies, and mutation operators introducing novel organizational forms. Technical Implementation:

• Replicator dynamics with mutation rates calibrated to historical institutional change
• Cultural transmission modeled via Price equations with selection coefficients
• Meme diffusion using agent-based epidemiological models with exposure thresholds
• Institutional innovation via genetic programming with fitness based on multiple welfare metrics

Economic System Evolution: Fitness landscapes for different economic arrangements, migration between communities with different systems, and analysis of hybrid system emergence and stability. Computational Methods:

• NK fitness landscapes with tunable ruggedness (K) for institutional complementarity
• Migration dynamics using gravity models with preference heterogeneity
• Hybrid stability analysis using Lyapunov functions and basin of attraction mapping
• Path dependence quantification via information-theoretic entropy measures

3.4 Multi-Scale Implementation

Micro-level: Individual decision-making with bounded rationality and preference updating Meso-level: Market clearing mechanisms, network formation, and institutional emergence Macro-level: System-wide resource flows, technological progress, and equilibrium dynamics Integration Architecture:

• Hierarchical temporal abstraction with event-driven updates across scales
• Meso-level market clearing using double auction mechanisms with O(log n) matching algorithms
• Macro-level flows modeled via system dynamics with adaptive time-stepping
• Cross-scale feedback implemented through message-passing interfaces with priority queues Data Structures and Algorithms:
• Agent state representation: Sparse feature vectors with dimensional reduction via autoencoders
• Network operations: Optimized graph algorithms with O(n log n) complexity for large-scale simulations
• Resource allocation: Constraint satisfaction problems solved with approximate algorithms (simulated annealing)
• Institutional rule representation: Formal grammar with production rules and constraint logic programming

4. Research Questions

1. Constraint Analysis: Which categories of constraints remain most binding under different levels of material abundance, and how do constraint interactions shape equilibrium possibilities?

2. Equilibrium Stability: Under what conditions do different post-scarcity equilibria emerge and remain stable? What factors drive transitions between equilibrium states?

3. Transition Dynamics: How do path-dependent factors and existing institutional arrangements influence trajectories toward post-scarcity conditions?

4. Fragmentation vs. Convergence: Do competitive pressures force convergence toward single economic systems, or can multiple arrangements coexist stably?

5. Inequality Evolution: How do different forms of inequality (material, status, attention, spatial) evolve under post-scarcity conditions?

6. Innovation Incentives: What mechanisms maintain innovation incentives in the absence of material scarcity and traditional profit motives?

5. Expected Outcomes

5.1 Theoretical Contributions

• Comprehensive taxonomy of constraints that persist beyond material scarcity
• Formal characterization of post-scarcity equilibrium conditions and stability properties
• Analysis of evolutionary pathways between different economic system configurations

5.2 Policy Implications

• Identification of institutional designs that promote stable, equitable post-scarcity transitions
• Analysis of policy interventions that can influence trajectory toward preferred equilibria
• Assessment of risks associated with different transition strategies

5.3 Methodological Advances

• Novel computational framework for analyzing complex economic transitions
• Integration of agent-based modeling with evolutionary game theory for institutional analysis
• Validation approaches for speculative economic modeling

6. Validation and Robustness

Historical Benchmarking: Validation against known economic transitions (agricultural to industrial, planned to market economies) Data Sources and Methods:

• Historical time series from Maddison Project Database and Clio Infra
• Synthetic control methods for counterfactual analysis of past transitions
• Pattern-oriented modeling with stylized facts from economic history
• Calibration using Approximate Bayesian Computation with summary statistics

Experimental Validation: Cross-validation with laboratory experiments on resource allocation behavior and existing UBI pilot program data Experimental Design:

• Online experiments (n=1000) using oTree platform for strategic interaction
• Field experiments in collaboration with existing UBI pilots (GiveDirectly, Stockton SEED)
• Virtual laboratory economies with real monetary incentives
• Prediction markets for model outcome validation

Sensitivity Analysis: Systematic parameter sweeps across automation rates, resource abundance levels, initial inequality distributions, and network topologies Technical Approach:

• Latin Hypercube Sampling for efficient parameter space exploration
• Sobol sequence generation for quasi-random parameter combinations
• Variance decomposition using ANOVA and global sensitivity indices
• Machine learning metamodels (Gaussian Processes) for response surface analysis

Robustness Testing: Analysis of model behavior under extreme parameter values and structural assumptions Methodological Details:

• Monte Carlo filtering to identify critical parameter thresholds
• Structural breaks analysis using changepoint detection algorithms
• Adversarial validation with intentionally misspecified models
• Cross-model validation with alternative implementations (agent-based vs. system dynamics) Computational Infrastructure:
• High-performance computing cluster with 1000+ cores for parallel simulations
• Containerized environments using Docker for reproducibility
• Version control and experiment tracking using Git and MLflow
• Automated sensitivity analysis pipeline with distributed task queue (Celery)

7. Timeline and Milestones

Year 1: Framework development, basic agent-based model implementation, initial constraint analysis Year 2: Game-theoretic submodel integration, evolutionary dynamics implementation, preliminary results Year 3: Full model validation, policy scenario analysis, manuscript preparation

8. Broader Impacts

This research addresses fundamental questions about the future of human economic organization as technological capabilities approach post-scarcity conditions. The computational framework will provide policymakers, technologists, and social planners with tools to anticipate and influence transitions toward more equitable and stable economic arrangements.

Beyond immediate policy applications, this work contributes to our understanding of complex adaptive systems, institutional evolution, and the relationship between technological capability and social organization. The methodological innovations may prove applicable to other domains involving large-scale social and economic transitions.

As societies worldwide grapple with automation, inequality, and technological displacement, systematic analysis of post-scarcity possibilities becomes increasingly crucial for informed decision-making about our economic future.