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arXiv research

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.

168,657 papers · 148 categories

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48 results for strategic behavior

Study strategic dynamic pricing for buyers with unknown manipulation costs.

problem Strategic buyers manipulate their features to get lower prices, hindering profit maximization.
method Proposes a strategic dynamic pricing policy that incorporates strategic behavior and binary response data.
result Achieves sublinear regret bound of O(T)O(\sqrt{T}) compared to linear Ω(T)Ω(T) regret of non-strategic policies.

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to th…

2018-08-25abs ↗pdf ↗

Look-ahead reasoning helps predict strategic user behavior on learning platforms.

problem Optimization criteria on learning platforms do not reflect users' priorities.
method Formalized level-k thinking and contrasted collective and selfish behavior.
result Coordination benefits users but does not offer higher-level reasoning advantages in the long run.

User strategization undermines algorithmic trustworthiness.

problem User strategic behavior corrupts algorithmic data and trust.
method Modeling user-platform interactions as a game, analyzing strategic behavior's short-term benefits and long-term harms.
result User strategization can initially benefit platforms but ultimately harms their ability to make accurate decisions.

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

problem Ensuring truthful reporting from strategic agents in online platforms.
method Proposes COBRA, an algorithm for contextual bandits involving strategic agents that disincentivizes strategic behavior.
result COBRA achieves sub-linear regret guarantee and incentive compatibility without monetary incentives.

Adobe research tackles strategic recommendations using reinforcement learning.

problem Optimizing user interactions for long-term objectives in various use-cases.
method Reinforcement learning applied to modeling user behavior and decision-making.
result Practical solutions implemented for various use-cases, addressing fundamental challenges.

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…

2019-09-04abs ↗pdf ↗

Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals…

2019-10-23abs ↗pdf ↗

The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.

problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.

The paper addresses fairness in dynamic pricing for strategic buyers.

problem Price disparities among specific groups can lead to unfair perceptions and legal violations.
method Proposes a dynamic pricing policy that achieves fairness and discourages strategic behavior.
result Achieves an upper bound of O(T+H(T))O(\sqrt{T}+H(T)) regret over TT time horizons, reducing regret by 35.06% compared to a benchmark policy.

Study allocates resources to strategic agents while balancing cost and incentives.

problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves ildeO(T) ilde{\mathcal{O}}(\sqrt{T}) social welfare regret, satisfies all cost constraints, and ensures incentive alignment.

We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies. At the same time, individuals may use knowledge, gained by transparency, …

2019-05-22abs ↗pdf ↗

Algorithm learns optimal coordination for strategic agents in uncertain settings.

problem Optimizing rewards for strategic agents with private types and actions.
method Combines delaying mechanism, reward angle estimation, and LinUCB algorithm.
result Near optimal regret bound of O~(T)\tilde{O}(\sqrt{T}) for learning optimal policy.

This paper tackles online strategic decision making with asymmetry and knowledge transportability.

problem Strategic decision making with information asymmetry and knowledge transportability challenges.
method Developed a sample-efficient algorithm for online learning under these conditions.
result Proved sample complexity of O(1/ε2)O(1/ε^2) for learning an εε-optimal policy.

New algorithm prevents strategic replication in multi-armed bandit problems.

problem Strategic replication by agents can exploit bandit algorithms' balance.
method Designs Hierarchical UCB (H-UCB) and Robust Hierarchical UCB (RH-UCB) algorithms.
result Achieves O(lnT)O(\ln T)-regret and sublinear regret in realistic scenarios.

The paper analyzes strategic irreversible investments with novel dynamic strategies.

problem Tradeoff between preemption incentives and option value of waiting in oligopolistic markets.
method Developed novel Markov perfect equilibrium to handle singular control of optimal investment.
result Simpler strategies lead to a 'preemption trap' with zero net present values.

The paper tackles performative policy learning with strategic agents, improving scalability and generalizability.

problem Strategic agents adjust their features in response to a released policy, causing endogenous distribution shifts.
method Relaxing parametric assumptions, the paper uncovers a low-dimensional structure in distribution shifts and proposes a gradient-based policy optimization algorithm.
result The proposed algorithm achieves high sample efficiency and provides theoretical guarantees for convergence.

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…

2020-02-17abs ↗pdf ↗

Study optimal treatment assignment policies under strategic agent responses.

problem Learning optimal treatment policies with strategic agents complicates estimation.
method Dynamic model with threshold convergence to mean-field equilibrium, consistent estimator for policy gradient.
result Threshold for treatment assignment converges to mean-field equilibrium threshold under large but finite number of agents.

Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.

problem Effect of traders' strategic behavior on market efficiency and informational asymmetry.
method Examines a market with boundedly rational, asymmetrically informed traders using multiarmed bandit algorithms.
result Strategically acting traders can lead to more efficient markets than purely competitive ones under certain conditions.

New findings link causal models to strategic classification, improving robustness and alignment.

problem Strategic adaptation by users in classification tasks.
method Causal models to bound worst-case out-of-distribution risk.
result Causal classification optimizes classification error after adaptation under certain noise conditions.

The paper develops classifiers that encourage positive adaptation in machine learning settings.

problem Strategic behavior by decision subjects leads to performance loss in machine learning models.
method Formulates a two-stage game to characterize optimal strategies for model designers and decision subjects.
result Trained classifiers maintain accuracy while inducing higher improvement and less manipulation.

LLMs can collude in market divisions, maximizing profits.

problem Strategic collusion of LLM agents in multi-commodity markets.
method Examined LLMs in Cournot competition frameworks, analyzing pricing and resource allocation strategies.
result LLMs can monopolize specific commodities without direct human input or explicit collusion commands.

Study examines pricing strategies in competitive supply chains with discrete prices.

problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.

ABIDES-MARL uses MARL to study market behavior in a realistic financial simulation.

problem Understanding equilibrium behavior in complex financial market games.
method Combines MARL with a realistic LOB simulation to study market behavior.
result Validated approach by solving an extended Kyle model and showing how execution strategies shape market dynamics.

New approach incentivizes strategic agents to explore, making exploration almost free.

problem Incentivized exploration in multi-armed bandits with long-term strategic agents.
method Simple incentive-provision strategy, best arm identification algorithm, and UCB lower bound.
result Exploration can be (almost) free when there are many learning agents.

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…

2006-05-16abs ↗pdf ↗

Model predicts option movements using residual transactions for better market timing.

problem Predicting option movements using standard metrics like open interest and trading volume.
method Analyzes residual transactions, integrates machine learning and regression techniques.
result Identifies early indicators of market trends for better option price forecasting.

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…

2005-02-07abs ↗pdf ↗

Analyzes gaming in federated learning systems and provides design principles.

problem Gaming and cooperation in federated learning systems under partial observability.
method Developed an analytical framework to separate welfare-improving behavior from metric gaming.
result Derived threshold conditions for deterring harmful gaming and preserving cooperation.

Improved εε-greedy handles strategic bidding in PPC auctions.

problem Strategic bidding in PPC auctions with personalization and corruptions.
method Extended εε-greedy to handle strategic arms in contextual multi-arm bandit.
result εε-greedy is robust to adversarial corruptions and degrades linearly with corruption.

The paper develops an economic foundation for multi-agent learning in markets.

problem Learning dynamics in markets with strategic externalities.
method A two-phase incentive mechanism that estimates and uses implementable transfers to steer long-run dynamics.
result The mechanism achieves sublinear social-welfare regret and asymptotically optimal welfare under mild rationality and exploration conditions.

Game theory models storage investment to balance market competition and profits.

problem Strategic storage investment impacts electricity market prices and revenues.
method Formulated a non-cooperative game between investors to model strategic storage decisions.
result Increasing storage capacity reduces individual profits but increases total investment.

Collectives can manipulate learning platforms by coordinated data submission, requiring strategic assessments and algorithms.

problem Collectives can influence learning platforms by altering data, posing risks and requiring strategic planning.
method Developed a theoretical and algorithmic framework to understand and mitigate collective manipulation of learning platforms.
result Demonstrated the need for strategic assessments and implementable coordination algorithms to prevent collective manipulation.