- The paper introduces the SNG model, an agent‐based framework that bridges econophysics with institutional dynamics to simulate abrupt macroeconomic regime shifts.
- It employs network-based simulations calibrated with empirical data to reveal how policy sequencing and network topology influence capital flows and systemic resilience.
- The study demonstrates that avoiding combinatorial dependency traps and mismatched reforms is crucial for sustaining long-term economic stability.
Stochastic Networked Governance: Synthesis of Econophysics and Institutional Dynamics in Agent-Based Macroeconomic Modeling
Introduction
The "Stochastic Networked Governance: Bridging Econophysics and Institutional Dynamics in a Positive-Sum Agent-Based Model" (2604.19968) presents a formalism for agent-based macroeconomic modeling that transcends traditional equilibrium paradigms in economics. It critiques the neoclassical and endogenous growth models for ignoring abrupt economic regime changes, nonlinearities, and the empirically validated importance of institutional path-dependence. The work integrates concepts from econophysics, complex networks, and institutional economics to capture the emergence of macroeconomic phenomena such as catastrophic regime collapse, topological spillovers, and resilience mechanisms within a formal agent-based positive-sum framework.
The SNG Model: Structure and Mechanics
Central to the paper is the Stochastic Networked Governance (SNG) model, which operationalizes macroeconomic regimes as discrete-time, agent-based entities embedded in a network. Each jurisdiction, or node, is governed by a binary "institutional genome" where five key levers—property rights, price mechanisms, labor market flexibility, capital account convertibility, and financial sector independence—encode policy configurations.
Institutional Complementarity and Genomic Penalties
Unlike equilibrium models that treat institutions as frictionless and interchangeable, the SNG model constrains institutional configurations through combinatorial dependency traps. Specific contradictory macropolicies incur severe penalties (e.g., the "Gorbachev Trap" and arbitrage-inducing configurations), encoded as deterministic efficiency reductions in node-level fitness. This formalization allows the model to reproduce the empirically observed phenomenon whereby partial or missequenced reforms provoke systemic MACs, pronounced capital flight, and prolonged output losses—features omitted in continuous, smooth macroeconomic models.
Endogenous Growth, J-Curve, and Path Dependency
Each node’s Total Factor Productivity (TFP) is adaptively linked to its institutional genome, with mutations penalized proportionally to the magnitude and speed of policy shifts, implementing a formal analog to the "J-Curve" of posteconomic reform chaos. The SNG model incorporates positive-sum endogenous growth dynamics: aggregate wealth compounds multiplicatively as a function of TFP, modulated by depreciation and networked spillovers. However, non-equilibrium transitions, induced by structural reform or shocks, can cause sequence-dependent regime collapses or rapid convergence, governed by the combinatorial efficiency landscape.
Networked Capital Flows and Topological Frictions
The SNG framework discretizes global economic topology as a weighted network, empirically calibrated using the CEPII Gravity Database for the 100 largest economies. Bilateral link weights encode economic mass and geospatial proximity, providing a more robust mechanism for spatial and network diffusion of shocks than random or mean-field topologies.
Interjurisdictional capital flows are made topology-aware and friction-limited. The friction coefficient scales with institutional dissimilarity (Hamming distance on the policy genome), reflecting the empirical barriers to capital movement across ideologically heterogeneous regimes. Tiebout sorting is modeled via zero-sum capital transfer mechanics, driving global resource reallocation toward nodes with higher institutional efficiency.
Regime Shift Dynamics and Empirical Shocks
Nodes that fall below a minimum wealth threshold enter a "panic" state, triggering endogenous regime change. Novelty is introduced by simulating exogenous "black swan" shocks, and further realism is imparted by replacing stochastic generic shocks with empirical data from the Laeven-Valencia IMF systemic crises catalog. This ensures that the timing and distribution of macro-shocks in the simulation reflect real-world economic events (e.g., the Latin American Debt Crisis, Asian Financial Crisis, and Global Financial Crisis).
Results: Network Topology, Phase Transitions, and “Spatial Firewall”
Phase Transitions and Contagion
The simulation ensemble demonstrates that, on mean-field and Erdős-Rényi networks, shocks diffuse rapidly and the network self-organizes toward efficient equilibria, with capital flight and demographic sorting eliminating inefficient regimes. In contrast, scale-free network architectures exhibit high systemic fragility: shocks at high-degree hubs can disproportionately propagate inefficiency, sustaining "zombie" economic blocs and suppressing global TFP. Geometric and gravity network topologies, by contrast, engender "spatial firewall" effects: physical and economic-geographical constraints localize shocks, prevent global contagion, and preserve growth trajectories in unaffected regions.
Empirical Replay: Collapse and Divergence
The empirical historical simulation rigorously recapitulates the 1989-1991 Soviet collapse without chronological hardcoding: states initialized with fatal policy contradictions (e.g., deregulated prices without property rights) suffered compounded decay, capital flight, and regime collapse as an intrinsic model prediction. The model endogenously distinguishes the “China Exception”: partial and properly sequenced reform programs enable sustained TFP growth, thereby avoiding the Gorbachev penalty and capturing China's divergence from the USSR.
Notably, major global financial crises are reflected in J-curve inflections, variance expansions, and temporary setbacks in the model’s free-market trajectory—yet, due to the empirical gravity network, these shocks are spatially quarantined and global exponential growth resumes.
Implications and Future Directions
The SNG model formalizes institutional path dependence, complementarity, and sequencing effects within a positive-sum, agent-based framework. It mathematically demonstrates that only those policy configurations avoiding combinatorial dependency traps and sequencing mismatches are macroscopically viable over time. The empirical replay strengthens the thesis that network topology—especially geospatial constraints—provides resilience against systemic contagion, a result with clear practical implications for policy design in globalized economies.
Methodologically, the introduction of a multidimensional genomic representation for institutions provides a flexible template for future extension. The authors propose integrating heterogeneous resource endowments ("resource curse" dynamics) and parametrically exploring ideological friction coefficients to simulate bloc formation and embargo effects, pointing toward richer, more granular modeling of multipolar macroeconomic architectures.
Conclusion
The SNG model offers an analytically tractable, empirically grounded agent-based methodology for studying macroeconomic dynamics under institutional, spatial, and topological constraints. Its capacity to predict catastrophic phase transitions, topologically mediated contagion, and endogenous hegemonic emergence provides significant advances over equilibrium-centric models. The formalism bridges the mathematical rigor of econophysics with the institutional detail required in economic history, opening the path for further interdisciplinary research into global economic resilience and regime transitions.