Multi-Party Data Pricing for Complex Data Trading Markets: A Rubinstein Bargaining Approach

Bing Mi; Zhengwang Han; Kongyang Chen

Home/Archive/arXiv

arXiv · 2502.16363

Multi-Party Data Pricing for Complex Data Trading Markets: A Rubinstein Bargaining Approach

Bing Mi·Zhengwang Han·Kongyang Chen

View original source ↗Read source document ↗Export metadata ↓

Abstract

With the rapid development of Internet of Things (IoT) and artificial intelligence technologies, data has become an important strategic resource in the new era. However, the growing demand for data has exacerbated the issue of \textit{data silos}. Existing data pricing models primarily focus on single factors such as data quality or market demand, failing to adequately address issues such as data seller monopolies and the diverse needs of buyers, resulting in biased pricing that cannot meet the complexities of evolving transaction scenarios. To address these problems, this paper proposes a multi-party data pricing model based on the Rubinstein bargaining model. The model introduces buyer data utility indicators and data quality assessments, comprehensively considering factors such as the utility, accuracy, and timeliness of data sets, to more accurately evaluate their value to buyers. To overcome the limitations of single-factor models, this paper innovatively introduces the buyer data set satisfaction indicator, which reflects the overall satisfaction of buyers with data sets by integrating both data utility and quality assessments. Based on this, the model uses the Rubinstein bargaining model to simulate the pricing process between multiple sellers and multiple buyers, yielding pricing results that better align with market demands. Experimental results show that the proposed model effectively addresses the pricing imbalance caused by data monopolies and demonstrates good applicability and accuracy in multi-seller, multi-buyer transaction environments. This research provides an effective pricing mechanism for complex data trading markets and has significant theoretical and practical value in solving pricing issues in actual data transactions.

Explore related subjects

cs.GT

Keep this discovery

BibTeX RIS

Bing Mi, Zhengwang Han, Kongyang Chen. 2025-02-22. Multi-Party Data Pricing for Complex Data Trading Markets: A Rubinstein Bargaining Approach. https://arxiv.org/abs/2502.16363

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MMS Allocation for Chores with Online Agent Arrivals

We study the fair allocation of $m$ indivisible chores to $n$ agents with subadditive cost functions arriving online in an arbitrary order. Upon an agent's arrival, we are informed of her cost function and must irrevocably assign her a set of chores. We focus on the Maximin Share (MMS) fairness notion and aim to compute an allocation in which all items are assigned, and no agent incurs a cost more than $\alpha$ times her MMS. Without any prior information about the instance (other than $n$ and $m$), we design an algorithm with a competitive ratio of $O(\min\{n, k\log^{1+\epsilon}k, \log m\})$ for any constant $\epsilon > 0$, where $k$ denotes the number of cost function types. Our bound matches the best known offline approximation guarantees for MMS under subadditive costs and is nearly optimal with respect to all three parameters: we show that even for binary additive cost functions, no online algorithm can achieve a competitive ratio of $o(\min\{n, k\log k, \log m\})$. We then consider the setting in which the $k$ cost function types are known in advance (though the realized types of arriving agents are not). For additive cost functions, we provide an algorithm with a competitive ratio of $O(\min\{\log k, \log(kn)/\log\log(kn)\})$, and show that constant-competitive algorithms do not exist for general $k$, even for the binary additive setting. For binary additive functions when $k \le n$, we propose a $3$-competitive algorithm and establish a lower bound of $2$.

cs.GT↗

arXiv2026

Truncated Noisy Best-Response Algorithms: Toward Game Theoretic Learning with Safety Guarantees

We consider a game theoretic approach to solve multi-agent coordination problems with submodular maximization objectives. It is known for such problems that the Nash equilibria for the corresponding game are always within 50% of the optimal, but that the equilibria which achieve this worst-case bound are not stable. To exploit this instability, we propose a family of algorithms which we call Truncated Noisy Best-Response (TNBR) Algorithms. These algorithms are flexibly characterized by agents asynchronously and stochastically selecting actions from a neighbourhood of their best response payoffs. We compute bounds on the recurrent classes of TNBR algorithms' associated Markov chains. Our bounds fall into two categories: first, "Performance" bounds ensure that TNBR algorithms always have a high-value recurrent state; second, "Safety" bounds ensure that TNBR algorithms never have arbitrarily-bad recurrent states. Furthermore, these two types of bounds are linked by a waterbed-like effect: every game with a poor Safety guarantee necessarily has a favorable Performance guarantee.

cs.GT↗

arXiv2026

Existence of the Core in Approval-Based Committee Elections

We settle the main open question in the theory of approval-based multi-winner elections: we show that there always exists a committee in the core. The core is a stability and group fairness concept. The proof introduces a new voting rule that optimizes an entropy-like objective function over committees and payment systems. All local optima of this objective function lie in the core, which implies that a core committee can be found in polynomial time.

cs.GT↗