Axiomatic Characterization of Allocation Mechanisms in Decentralized Exchange Markets with Frictional Costs

Core Concepts

The core message of this paper is to provide an axiomatic characterization of allocation mechanisms in decentralized exchange markets with frictional costs, where certain transfers between agents result in costs to the economy. The authors introduce the notion of "frictional participation" to capture these frictions and show how it leads to a representation of allocation mechanisms as robust linear mechanisms or robust conditional mean allocation mechanisms.

Abstract

The paper examines allocation mechanisms in a decentralized pure-exchange economy under uncertainty, where agents have initial state-contingent endowments. The authors argue that certain types of transfers between agents, namely initial transfers of state-contingent endowments to any agent with zero initial endowment, result in costs to the economy, which they call "frictional costs".
The authors introduce the notion of an "allocation mechanism" that transforms feasible allocations of the aggregate endowment into other feasible allocations, and they propose an axiomatic study of such mechanisms in the presence of frictional costs. They introduce several axioms, including Internal Fairness, Agent Anonymity, Operational Anonymity, Frictional Participation, and Scale Invariance.
The authors show that the combination of these axioms characterizes two classes of allocation mechanisms: robust allocation mechanisms and robust conditional mean allocation mechanisms. Robust allocation mechanisms are represented as worst-case linear allocation mechanisms, while robust conditional mean allocation mechanisms are represented as worst-case conditional expectations.
The authors also discuss the relationship between their results and the literature on decentralized risk sharing within a pool of agents, providing an axiomatization of conditional mean allocation mechanisms and a subjective version of the Conditional Mean Risk Sharing (CMRS) rule.
Finally, the authors present two examples of robust allocation mechanisms: the Mean-Deviation allocation mechanism and the Expected-Shortfall allocation mechanism, and analyze the impact of parameters such as correlation and risk attitudes on the global frictional costs and the incentives for agents to participate in the risk-sharing pool.

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Allocation Mechanisms in Decentralized Exchange Markets with Frictions

by Mario Ghosso... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.10900.pdf

Deeper Inquiries

What are some potential real-world applications of the robust allocation mechanisms characterized in this paper

The robust allocation mechanisms characterized in the paper have various potential real-world applications in the field of finance and economics.

Risk Sharing and Insurance: These mechanisms can be applied in the context of risk-sharing pools or insurance markets. By using robust allocation mechanisms, participants can redistribute risks among themselves efficiently, taking into account the costs associated with transfers and ensuring fair and optimal allocations.

Investment Portfolios: In the realm of investment management, these mechanisms can be utilized to allocate assets among different portfolios or investors. By considering the frictional costs involved in transfers, the allocation can be optimized to maximize returns while minimizing costs.

Peer-to-Peer Lending: Robust allocation mechanisms can also be applied in peer-to-peer lending platforms where individuals lend and borrow money from each other. By incorporating transfer costs and frictional participation, the mechanisms can ensure fair and efficient lending practices.

Resource Allocation in Supply Chains: In supply chain management, these mechanisms can help in allocating resources among different nodes or partners in the supply chain. By considering the costs associated with transfers, the allocation can be optimized to improve overall efficiency and reduce wastage.

How could the authors' framework be extended to account for heterogeneous preferences or information asymmetries among the agents

To account for heterogeneous preferences or information asymmetries among the agents, the authors' framework could be extended in the following ways:

Preference Heterogeneity: Introduce a parameter in the allocation mechanisms that captures individual risk attitudes or preferences. This parameter can vary among agents and influence the allocation decisions based on their risk tolerance levels.

Information Asymmetry: Incorporate asymmetric information by allowing agents to have different levels of knowledge or access to information. This can be reflected in the allocation mechanisms by adjusting the allocation based on the available information to each agent.

Subjective Conditional Allocations: Extend the framework to include subjective conditional allocations where agents have subjective beliefs or assessments of the risks involved. This can lead to personalized allocations based on individual perceptions of risk.

Game-Theoretic Approaches: Utilize game theory to model interactions between agents with different preferences or information sets. This can lead to strategic allocations that account for the strategic behavior of agents in the allocation process.

Are there other types of frictions or market imperfections that could be incorporated into the axiomatic study of allocation mechanisms

The axiomatic study of allocation mechanisms can be expanded to incorporate various other types of frictions or market imperfections, such as:

Transaction Costs: Including transaction costs in the allocation mechanisms can provide a more realistic representation of the actual costs involved in transferring assets or resources between agents. This can lead to more accurate and practical allocation decisions.

Liquidity Constraints: Considering liquidity constraints in the allocation process can account for situations where agents have limited access to funds or assets, impacting their ability to participate in certain transactions or allocations.

Regulatory Constraints: Incorporating regulatory constraints or limitations in the allocation mechanisms can ensure compliance with legal requirements and restrictions that may affect the transfer of assets or resources among agents.

Market Frictions: Addressing market frictions such as asymmetric information, market power, or externalities can provide a more comprehensive understanding of the challenges and complexities present in real-world markets. By incorporating these frictions, the allocation mechanisms can be designed to mitigate their effects and optimize resource allocations.