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An Adaptive Multichain Blockchain: A Multiobjective Optimization Approach

Nimrod Talmon, Haim Zysberg

TL;DR

This work casts blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price.

Abstract

Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price. The objective maximizes a governance-weighted combination of normalized utilities for applications, operators, and the system. The model is modular -- accommodating capability compatibility, application-type diversity, and epoch-to-epoch stability -- and can be solved off-chain with outcomes verifiable on-chain. We analyze fairness and incentive issues and present simulations that highlight trade-offs among throughput, decentralization, operator yield, and service stability.

An Adaptive Multichain Blockchain: A Multiobjective Optimization Approach

TL;DR

This work casts blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price.

Abstract

Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price. The objective maximizes a governance-weighted combination of normalized utilities for applications, operators, and the system. The model is modular -- accommodating capability compatibility, application-type diversity, and epoch-to-epoch stability -- and can be solved off-chain with outcomes verifiable on-chain. We analyze fairness and incentive issues and present simulations that highlight trade-offs among throughput, decentralization, operator yield, and service stability.
Paper Structure (69 sections, 1 theorem, 27 equations, 1 figure)

This paper contains 69 sections, 1 theorem, 27 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Scope and contribution
  3. Related Work
  4. Preliminaries
  5. Blockchain Setting
  6. System Dynamics
  7. Practical Assumptions
  8. Core Model of Ephemeral Chains
  9. Notation
  10. Decision Variables
  11. Agent Inputs
  12. Derived Chain Properties
  13. Constraints
  14. Assignment.
  15. Stake feasibility.
  16. ...and 54 more sections

Key Result

Proposition 1

Even in the core model, the following decision problem is NP-hard: given an instance and a threshold $T$, decide whether there exists a feasible assignment with $U^{\text{sys}}\ge T$ (for $\lambda_{\text{sys}}=1$).

Figures (1)

  • Figure 1: Governance control over utilities. Each triangular panel is a ternary plot over the governance-controlled parameters of multiagent preferences $(\lambda_{\text{app}},\lambda_{\text{op}},\lambda_{\text{sys}})$. From left to right: (i) application utility, (ii) operator utility, (iii) system utility. Color indicates normalized utility (colder $\to$ lower, warmer $\to$ higher). Utilities peak near the vertex where the corresponding weight dominates (e.g., app utility near $(1,0,0)$), and trade off smoothly along the edges and interior.

Theorems & Definitions (7)

  • Proposition 1: NP-hardness
  • proof
  • Remark 1
  • Remark 2: Balancing fairness, efficiency, and dynamics
  • Remark 3
  • Remark 4: Instance geometry
  • Remark 5: Misalignment dynamics