A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Algorithm learns optimal coordination for strategic agents in uncertain settings.
COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.
Study allocates resources to strategic agents while balancing cost and incentives.
New algorithm learns optimal policies in strategic MDPs with private types.
Study optimal treatment assignment policies under strategic agent responses.
New algorithm prevents strategic replication in multi-armed bandit problems.
We introduce a strategic behavior in reinsurance bilateral transactions, where agents choose the risk preferences they will appear to have in the transaction. Within a wide class of risk measures, we identify agents' strategic choices to a range of risk aversion coefficients. It is shown that at the strictly beneficial…
Modified Perceptron handles strategic agents with limited position changes.
The paper tackles performative policy learning with strategic agents, improving scalability and generalizability.
Algorithms are often used to produce decision-making rules that classify or evaluate individuals. When these individuals have incentives to be classified a certain way, they may behave strategically to influence their outcomes. We develop a model for how strategic agents can invest effort in order to change the outcome…
New approach incentivizes strategic agents to explore, making exploration almost free.
In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously…
The dynamics of many socioeconomic systems is determined by the decision making process of agents. The decision process depends on agent's characteristics, such as preferences, risk aversion, behavioral biases, etc.. In addition, in some systems the size of agents can be highly heterogeneous leading to very different i…
We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack. In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of…
New framework for robust uncertainty quantification in strategic settings.
New findings show strategic interactions can undermine model expressiveness in machine learning.
This work explains crises in markets without external news using bounded rational agents.
Study compares employers with and without anticipating strategic labor force responses.
The paper develops an economic foundation for multi-agent learning in markets.
LLMs can collude in market divisions, maximizing profits.
This paper tackles online strategic decision making with asymmetry and knowledge transportability.
In this paper we extend the series of our studies on the properties of an interacting particle model for market microstructure. In our earlier work we defined a Markov process on the majority opinion of the agents, obtained the transition probabilities and analyzed the martingale properties of the ensuing wealth proces…
Study identifies a Strategic Gap in market efficiency due to AI-driven timing and complexity in disclosure.
Strategic behaviour is one of the main explanations for cost overruns. It can theoretically be supported by agency theory, in which strategic behaviour is the result of asymmetric information between the principal and agent. This paper gives a formal account of this relation by a signalling game. This is a game with in…
The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…
Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…
The game theory techniques are used to find the equilibrium of a market. Game theory refers to the ways in which strategic interactions among economic agents produce outcomes with respect to the preferences (or utilities) of those agents, where the outcomes in question might have been intended by none of the agents. Th…
We consider the problem of stopping a diffusion process with a payoff functional that renders the problem time-inconsistent. We study stopping decisions of naive agents who reoptimize continuously in time, as well as equilibrium strategies of sophisticated agents who anticipate but lack control over their future selves…
Calibrated models can lead to miscalibrated aggregations in strategic interactions.
When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent responsiveness, termed "strategic manipulation," analyze the interaction between a learner and agents in a world where all agents are equally able …
We study a game-theoretic variant of the maximum circulation problem. In a flow allocation game, we are given a directed flow network. Each node is a rational agent and can strategically allocate any incoming flow to the outgoing edges. Given the strategy choices of all agents, a maximal circulation that adheres to the…
AI agents manage portfolios, improving on human oversight.
Randomised classifiers outperform deterministic ones in strategic classification.
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
In this short paper we define the wealth process in a spin model for market microstructure, for individual agents and in aggregate. The agents in our model try to balance their desire to belong to the local majority (herding behavior), defined over random network neighborhoods, and the occasional advantage of belonging…
We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward and can choose an amount to pass on to the principal (keeping for itself). All non-pulle…
We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them. Such feature manipulation has been obser…
The goal of this study is to determine which strategic model, either IO or RBV, allows firms to generate the highest performance on a competitive market. Contrasting with classical studies that mobilize analyses as VARCOMP, we deploy a multi-agent system simulating the behavior of firms adopting RBV or IO strategic mod…
We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed …
The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…
PEAR dynamically reconfigures agent roles to prevent persistent biases in multi-agent debates.
ABIDES-MARL uses MARL to study market behavior in a realistic financial simulation.
A monopolist sells goods with possibly a characteristic consumers dislike (for instance, he sells random goods to risk averse agents), which does not affect the production costs. We investigate the question whether using undesirable goods is profitable to the seller. We prove that in general this may be the case, depen…
The paper analyzes strategic interactions in a multi-agent reinsurance chain using game theory.
Abstract MDPs enable strategic exploration and fast reward transfer in complex environments.
Cross-dimensional neural networks improve AI in Catan game.
Adobe research tackles strategic recommendations using reinforcement learning.
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…