arXiv research

The paper analyzes auction theory and its impact on buyer demographics.

A new pricing strategy minimizes regret by controlling strategic buyer behavior.

Study identifies 1,012 persistent wallet cohorts on Solana pump.fun, showing coordinated buying behavior.

We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution $D$ in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisio…

UNMIX identifies hidden buyers in darknet markets by clustering anonymized IDs.

We consider an American contingent claim on a financial market where the buyer has additional information. Both agents (seller and buyer) observe the same prices, while the information available to them may differ due to some extra exogenous knowledge the buyer has. The buyer's information flow is modeled by an initial…

In Part III of this study, we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing prices of the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange. The basic idea is to estimate the strength parameters of the big buyers and the…

Study strategic dynamic pricing for buyers with unknown manipulation costs.

We propose a continuum model for the description of buyer and seller dynamics in an Internet market. The relevant variables are the research effort of buyers and the sellers' reputation building process. We show that, if a commercial web-site gives consumers the possibility to rate credibly sellers they bargained with,…

The paper addresses fairness in dynamic pricing for strategic buyers.

In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitable contract for the buyer in the offered set, i.e., a contract with payoff higher than the payoff of not accepting any c…

Ad exchanges use CORP to set reserve prices against strategic buyers.

In market modeling, one often treats buyers as a homogeneous group. In this paper we consider buyers with heterogeneous preferences and products available in many variants. Such a framework allows us to successfully model various market phenomena. In particular, we investigate how is the vendor's behavior influenced by…

The author suggests using non-Euclidean geometry for psychometric models.

NFT art market shows strong preferential ties among sellers and buyers.

AI models assess psychological risks in currency trading.

The book explores essential stats and psychology for quantitative trading.

The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on th…

We consider a simple market where a vendor offers multiple variants of a certain product and preferences of both the vendor and potential buyers are heterogeneous and possibly even antagonistic. Optimization of the joint benefit of the vendor and the buyers turns the toy market into a combinatorial matching problem. We…

The cognitive framework of conceptual spaces bridges the gap between symbolic and subsymbolic AI by proposing an intermediate conceptual layer where knowledge is represented geometrically. There are two main approaches for obtaining the dimensions of this conceptual similarity space: using similarity ratings from psych…

We propose a new efficient online algorithm to learn the parameters governing the purchasing behavior of a utility maximizing buyer, who responds to prices, in a repeated interaction setting. The key feature of our algorithm is that it can learn even non-linear buyer utility while working with arbitrary price constrain…

New model explains price dynamics of Bitcoin with psychological factors.

Task-agnostic data valuation without validation requirements.

Study shows house buyers in Christchurch value earthquake risk differently based on time since 2011 quake.

Model studies money exchange stability in social networks.

The paper interprets financial markets as crowds during booms and busts.

The paper analyzes regret in bilateral trade mechanisms without prior valuations.

New framework for forecasting psychological processes from ILD.

Study uses AI to simulate stock market behavior, revealing how trader psychology affects market stability.

Buyer--seller relationships among firms can be regarded as a longitudinal network in which the connectivity pattern evolves as each firm receives productivity shocks. Based on a data set describing the evolution of buyer--seller links among 55,608 firms over a decade and structural equation modeling, we find some evide…

We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that h…

Model shows how Ethereum can capture MEV from block construction, but centralization remains a concern.

We show, under weaker assumptions than in the previous literature, that a perpetual optimal stopping game always has a value. We also show that there exists an optimal stopping time for the seller, but not necessarily for the buyer. Moreover, conditions are provided under which the existence of an optimal stopping time…

Optimal buying and selling times for homes in fluctuating interest rates.

Paper reviews intrinsic motivations and their role in open-ended learning.

Study proves duality in exotic option pricing under uncertain model and delayed information.

IPGP framework improves psychological assessment by integrating shared and unique traits.

We study pricing and (super)hedging for American options in an imperfect market model with default, where the imperfections are taken into account via the nonlinearity of the wealth dynamics. The payoff is given by an RCLL adapted process $(ξ_t)$. We define the {\em seller's superhedging price} of the American option a…

Dual Variable Learning Rates improve neural network training efficiency.

In the recent paper \cite{DESZ}, the notion of $\mathscr{Y}^{g,ξ}$-submartingale processes has been introduced. Within a jump-diffusion model, we prove here that a process $X$ which satisfies the simultaneous $\mathscr{Y}^{\mathbb{Q},g,ξ}$ -submartingale property under a suitable family of equivalent probability measur…

AI framework predicts invoice dilution in supply chain finance.

PsychFM predicts individual gambling choices using psychological and machine learning models.

This paper develops a pricing model for data assets from the buyer's perspective.

The credit crisis of 2007 and 2008 has thrown much focus on the models used to price mortgage backed securities. Many institutions have relied heavily on the credit ratings provided by credit agency. The relationships between management of credit agencies and debt issuers may have resulted in conflict of interest when …

Deep neural networks (DNNs) have achieved unprecedented performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions. This has caused a recent surge of interest in methods for rendering modern neural systems more interpretable. In this work, we propose to address th…

Method learns behavioral states from wearable sensor data.

Study breaks down graphs into structural and featural components for task-agnostic data valuation.

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. For this setting, first, we propose a novel algorithm that never decreases …