arXiv research

Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.

Develops methods for finding counterfactual explanations in sequential decision making.

New algorithm improves knowledge transfer in dynamic decision-making.

Batch Thompson Sampling reduces exploration-exploitation trade-off in online decision making.

Chronological Causal Bandits (CCB) tackles dynamic causal decision-making.

New bounds show complexity of adversarial decision making.

Modeling decision-making dynamics in MDD patients using RL-HMM.

Unified framework models neural decision-making, improving accuracy.

The study examines robust decision-making in volatile financial markets, finding action robustness is more impactful than uncertainty tolerance.

We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose differ…

Study links cognitive effort to thermodynamic principles, optimizing decision-making.

We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, sync…

In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environments, making individual observations incomplete and unreliable. Moreover, in many situations it is preferable to delay a decision rather than run the risk of making …

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the s…

A new MDP with Bandits approach for sequential decision making in linear-flow scenarios.

Framework infers Langevin dynamics from stochastic observations of latent systems.

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant infor…

New online algorithms tackle dynamic decision-focused learning.

Framework for deferring decisions to experts in sequential medical settings.

The paper proposes Tier Balancing for dynamic fairness in decision-making.

It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness. We propose to address this challenge by modeling feedback effects as Markov decision processes (MDPs). First, we prop…

Spectral method speeds fitting of binary time series models.

Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical a…

Interpole learns transparent decision-making policies from data.

Study on information evolution in interactive decision making using multi-armed bandits.

Model shows how diversity on corporate boards influences decision-making and innovation.

Optimistic pricing algorithm handles online dynamic pricing with censored demand.

IDS algorithm optimizes sequential decisions in various monitoring settings.

Bayesian method estimates dynamics from near-optimal trajectories.

This paper analyzes the multi-armed bandit model using path-integral methods.

Enhanced decision-making through Dreamer's anticipatory trajectories and Online Decision Transformer.

A new classifier improves one-class predictions on unevenly sampled data.

New methods improve robust decision-making under uncertainty in off-policy evaluation.

Deep learning complements OR/MS for decision-making under uncertainty.

New method selects features for sequential decision making.

Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…

Proposes efficient algorithm for system-level I&M decisions under uncertainty.

FinHEAR combines LLMs with human expertise for better financial decision-making.

Prospective learning improves AI performance in changing conditions.

DCBO optimizes interventions in evolving causal systems.

New framework optimizes forecasting and decision-making in dynamic systems.

DiffLOB models future market conditions for better decision-making.

The paper addresses private and Byzantine-proof cooperative decision-making in multi-agent systems.

Existing approaches to online convex optimization (OCO) make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best ye…

Algorithm reduces decision-making errors in multi-agent bandit problems.

Unified framework for constrained online decision-making.

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an e…

New algorithm optimizes online decision-making with dynamically generated actions.