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This study adapts the representative agent optimal growth problem for virtual worlds. It introduces heterogeneous agents, local interactions, and bounded rationality, shifting from equilibrium to emergent outcomes in economic modeling.

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Area of Science:

  • Economics
  • Computational Economics
  • Agent-Based Modeling

Background:

  • The representative agent optimal growth problem is a cornerstone of economic orthodoxy.
  • This model assumes rational agents maximizing intertemporal utility.
  • Limitations exist in reflecting complex, real-world economic dynamics.

Purpose of the Study:

  • To adapt the optimal growth problem for virtual worlds with interacting agents.
  • To incorporate heterogeneity, local interactions, and bounded rationality.
  • To transition from equilibrium-based models to complexity frameworks.

Main Methods:

  • Replaced the representative agent with heterogeneous households.
  • Introduced local interaction and boundedly rational decision rules (heuristics).
  • Analyzed the interplay of heterogeneity, local interaction, and non-optimality.

Main Results:

  • Heterogeneous productivity influences technology adoption and innovation diffusion.
  • Consumption heuristics allow for diverse saving-consumption behaviors.
  • Local interactions facilitate the unpredictable spread of sentiments (optimism/pessimism).

Conclusions:

  • Simultaneous consideration of heterogeneity, local interaction, and non-optimality is crucial for complexity frameworks.
  • These factors reinforce each other, leading to emergent results.
  • The new framework offers a richer analysis of aggregate economic dynamics.