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.
First ABAW 2020 Competition analyzes affective behavior tasks.
Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.
We present examples of agent-based and stochastic models of competition and business processes in economics and finance. We start from as simple as possible models, which have microscopic, agent-based, versions and macroscopic treatment in behavior. Microscopic and macroscopic versions of herding model proposed by Kirm…
Agents learn to outperform in trading by using past and current prices.
Proposes CoPO, a new policy optimization method for competitive games.
Study examines pricing strategies in competitive supply chains with discrete prices.
Challenge encourages reproducible deep learning methods.
We propose a simple dynamical model of the formation of production networks among monopolistically competitive firms. The model subsumes the standard general equilibrium approach à la Arrow-Debreu but displays a wide set of potential dynamic behaviors. It robustly reproduces key stylized facts of firms' demographics. O…
The paper analyzes reinsurance strategies in a competitive multi-agent system.
New method ranks competitors from multiple types of comparisons.
Study minimax off-policy evaluation in multi-armed bandits with known and unknown behavior policies.
LLMs can collude in market divisions, maximizing profits.
We propose a novel approach to train a multi-modal policy from mixed demonstrations without their behavior labels. We develop a method to discover the latent factors of variation in the demonstrations. Specifically, our method is based on the variational autoencoder with a categorical latent variable. The encoder infer…
Neoclassical economics has two theories of competition between profit-maximizing firms (Marshallian and Cournot-Nash) that start from different premises about the degree of strategic interaction between firms, yet reach the same result, that market price falls as the number of firms in an industry increases. The Marsha…
The origin of economic crises is a key problem for economics. We present a model of long-run competitive markets to show that the multiplicity of behaviors in an economic system, over a long time scale, emerge as statistical regularities (perfectly competitive markets obey Bose-Einstein statistics and purely monopolist…
AI beats 95% of humans in Rock-Paper-Scissors.
Two-stream model recognizes affect from audio and video.
Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These tech…
Algorithm for decentralized competition among adaptive agents.
The paper revisits classical competition theory to explain speculative asset price dynamics.
Models of spatial firm competition assume that customers are distributed in space and transportation costs are associated with their purchases of products from a small number of firms that are also placed at definite locations. It has been long known that the competition equilibrium is not guaranteed to exist if the mo…
Gradient-descent-ascent dynamics can exhibit various behaviors in non-convex non-concave games.
We show that the dynamical equations describing the collective behavior of the model introduced by Cavagna et al. i) are not their Eqs. (5,6) but rather ii) are the same as those of the minority game (MG). As a consequence the analytic solution of the MG presented in [PRL, 84, 1824 (2000)] holds also for this model. Fi…
FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.
This paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To p…
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…
Improved model accuracy can reduce overall user accuracy in competitive markets.
NeurIPS 2020 competition seeks to predict deep learning generalization.
When society maintains a competitive system to promote an abstract goal, competition by necessity relies on imperfect proxy measures. For instance profit is used to measure value to consumers, patient volumes to measure hospital performance, or the Journal Impact Factor to measure scientific value. Here we note that \t…
A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with near neighbor interactions. The evolution of each agent results from the competition between two factors: the agent's own tendency to grow and the environmen…
New framework recovers reward and rationality parameters from game behavior.
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions …
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-leve…
Study shows market volatility affects optimal communication design for trading strategies.
Modeling trading behavior with information signals and limit order books, showing market impact and equilibrium properties.
We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care prov…
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
RL controls small soccer robots in a real league, beating human-designed policies.
We develop a mean-field theory for multi-component ICA in high dimensions.
Paper models market dynamics using bull and bear forces.
Reshuffling splits improves hyperparameter optimization's generalization performance.
New RL method learns value function for many policies using few key states.
We study optimal behavior of energy producers under a CO_2 emission abatement program. We focus on a two-player discrete-time model where each producer is sequentially optimizing her emission and production schedules. The game-theoretic aspect is captured through a reduced-form price-impact model for the CO_2 allowance…
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent …
Paper proposes a method to improve off-policy reinforcement learning in batch settings.
Competition has been introduced in the electricity markets with the goal of reducing prices and improving efficiency. The basic idea which stays behind this choice is that, in competitive markets, a greater quantity of the good is exchanged at a lower and a lower price, leading to higher market efficiency. Electricity …
We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace. For non-trivial dynamical systems, MBRL typically suffers from accumulating errors. Several recent studies have addressed thi…