V. Zhukovin, Z. Alimbarashvili
In the item there are presented Interval Structure allow to extend the class of multicriteria decision making problems including incomplete Initial Information. this step will facilitate dada gathering in pairs matching for decision making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
继续探索
This paper investigates natural conditions for the existence of optimal policies for a Markov decision process with incomplete information (MDPII) and with expected total costs. The MDPII is the classic model of a controlled stochastic process with incomplete state observations which is more general than Partially Observable Markov Decision Processes (POMDPs). For MDPIIs we introduce the notion of a semi-uniform Feller transition probability, which is stronger than the notion of a weakly continuous transition probability. We show that an MDPII has a semi-uniform Feller transition probability i…
This paper deals with control of partially observable discrete-time stochastic systems. It introduces and studies Markov Decision Processes with Incomplete Information and with semi-uniform Feller transition probabilities. The important feature of these models is that their classic reduction to Completely Observable Markov Decision Processes with belief states preserves semi-uniform Feller continuity of transition probabilities. Under mild assumptions on cost functions, optimal policies exist, optimality equations hold, and value iterations converge to optimal values for these models. In parti…
Empathic network learning for multi-expert emergency decision-making under incomplete and inconsistent information
针对应急决策中不完备不一致信息，提出基于鲁棒序数回归的共情网络学习方法，补全模糊判断矩阵并构建模型推导最具代表性的共情网络。
Generalized Ordinal Priority Approach for Multi-Attribute Decision-Making under Incomplete Preference Information
针对不完全偏好信息下的多属性决策问题，提出基于“先估计后优化”框架的广义序优先方法（GOPA），通过两阶段优化同时确定专家、属性和备选方案权重，并验证了其泛化性、解析可解性和风险偏好独立性。
Repair Crew Routing for Infrastructure Network Restoration under Incomplete Information
本文针对不完全信息下基础设施网络修复问题，提出基于强化学习和状态聚合的有限时域马尔可夫决策过程求解方法，并通过计算实验验证其性能。
Adding Decision Problem Makes Information More Valuable
本文研究了在未知自然状态信息不完整时的决策问题，证明了在信息结构中加入一个独立的并行决策问题能提高信息价值。
It is introduced the using of generation lexicographical procedure for multicriteria decision-making problems.
We propose a (seemingly) new computationally tractable model for multi-stage decision making under stochastic uncertainty.
Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process (POMDP) is known. By incorporating uncertainty into our model, we show that navigation policies can be constructed that maintain safety, outperforming the baseline performance of traditional dynamic programming for Markov decision processes (MDPs). Adding in controlled sensing methods, we show that these policies can also lower measurement costs at the same time.
We define decision-making functions which arise from studying the multidimensional generalization of the weighted Bajraktarević means. It allows a nonlinear approach to optimization problems. These functions admit several interesting (from the point of view of decision-making) properties, for example, delegativity (which states that each subgroup of decision-makers can aggregate their decisions and efforts), casuativity (each decision affects the final outcome except two trivial cases) and convexity-type properties. Beyond establishing the most important properties of such means, we solve thei…
In a zero-sum stochastic game with signals, at each stage, two adversary players take decisions and receive a stage payoff determined by these decisions and a variable called state. The state follows a Markov chain, that is controlled by both players. Actions and states are imperfectly observed by players, who receive a private signal at each stage. Mertens (ICM 1986) conjectured two properties regarding games with long duration: first, that limit value always exists, second, that when Player 1 is more informed than Player 2, she can guarantee uniformly the limit value. These conjectures were …
From this set of procedures for given clause we shall choose only interrogation of experts on pairs decisions. It is widely widespread method. It makes the whole chapter in the theory of the decision-making, well investigated with the formal point of view. In the modern theory of decision-making at gathering the initial information for mathematical model it practically does not have alternative.
It is introduced the concept of Superiority Degree one competitive decision over another. On the basis of this concept the mathematics theoretic structure is developed, which is part of pairs comparisons branch in modern decision making theory. It will be useful for practice and interesting for scientific research.
When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological mode…
There is given the program realization system "lexicon" by the using lexicographical procedure.
The collective decision-making exhibited by animal groups provides enormous inspiration for multi-agent control system design as it embodies several features that are desirable in engineered networks, including robustness and adaptability, low computational effort, and an intrinsically decentralized architecture. However, many of the mechanistic models for collective decision-making are described at the population-level abstraction and are challenging to implement in an engineered system. We develop simple and easy-to-implement models of opinion dynamics that realize the empirically observed c…
The weighted OWA (WOWA) is a function that aggregates a set of values with weights assigned based on the rank and relative importance of each value. The weighted OWA of uncertain objective functions can generalize many of the criteria that is used in decision making under uncertainty. In this paper, we apply the WOWA criterion to two-stage decision making problems, and present decomposition algorithms to solve them. The algorithms are applied to location-transportation problem with uncertain demands and computational results are presented.
We consider the optimal duration allocation in a decision making queue. Decision making tasks arrive at a given rate to a human operator. The correctness of the decision made by human evolves as a sigmoidal function of the duration allocated to the task. Each task in the queue loses its value continuously. We elucidate on this trade-off and determine optimal policies for the human operator. We show the optimal policy requires the human to drop some tasks. We present a receding horizon optimization strategy, and compare it with the greedy policy.
Fairness is a major concern in contemporary decision problems. In these situations, the objective is to maximize fairness while preserving the efficacy of the underlying decision-making problem. This paper examines repeated decisions on problems involving multiple stakeholders and a central decision maker. Repetition of the decision-making provides additional opportunities to promote fairness while increasing the complexity from symmetry to finding solutions. This paper presents a general mathematical programming framework for the proposed fairness-over-time (FOT) decision-making problem. The …
Determination of inspection and maintenance policies for minimizing long-term risks and costs in deteriorating engineering environments constitutes a complex optimization problem. Major computational challenges include the (i) curse of dimensionality, due to exponential scaling of state/action set cardinalities with the number of components; (ii) curse of history, related to exponentially growing decision-trees with the number of decision-steps; (iii) presence of state uncertainties, induced by inherent environment stochasticity and variability of inspection/monitoring measurements; (iv) prese…
We use decision theory to confront uncertainty that is sufficiently broad to incorporate "models as approximations." We presume the existence of a featured collection of what we call "structured models" that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max-min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide a…
Decision-Making in Digital Twins: Optimization, Control, and Learning
本文综述数字孪生驱动的决策方法进展，聚焦仿真优化、序列决策、风险感知决策三领域，探讨相关技术并指出研究挑战与机遇。
Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we propose an information-theoretic formalization of bounded rational decision-making where decision-makers trade off expected utility and information processing costs. Such bounded rational decision-makers can be thought of as thermodynamic machines that undergo physical state changes when they compute. Their behavior is governed by a free energy functional that trades off changes in internal energy-as a proxy for utility-and entropic changes re…
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for softmax decision-making models with linear objective functions. We present conditions under which the likelihood function is convex. These allow us to provide sufficient conditions for convergence of the resulting maximum likelihood estimator and to construct its asymptotic distribu…
Discrete Route/Trajectory Decision Making Problems
本文研究了复合多阶段决策问题，旨在设计从初始决策状态到目标决策状态的路径或轨迹。通过汽车路线问题作为基本物理隐喻，探讨了离散操作/状态设计空间（如有向图）中的多种决策问题，并在教育、医学和经济等领域应用。
Collective Decision-Making in Ideal Networks: The Speed-Accuracy Tradeoff
本文研究人类群体在具有网络交互的模型中进行两种选择任务的集体决策，探讨个体在速度与准确性之间的权衡，通过耦合漂移扩散模型分析证据聚合过程，并探讨网络位置对决策性能的影响。
Double Fuzzy Probabilistic Interval Linguistic Term Set and a Dynamic Fuzzy Decision Making Model based on Markov Process with tts Application in Multiple Criteria Group Decision Making
针对概率语言术语缺陷及动态属性权重确定难问题，提出双模糊概率区间语言术语集概念，定义相关内容，开发模糊语言马尔可夫矩阵等，给出权重确定方法，并用金融风险投资案例说明其在多准则决策中的应用。
On Risk-Sensitive Decision Making Under Uncertainty
本文提出在不确定性下风险敏感决策的最优性条件，并通过最佳投注和库存管理示例验证了理论。
A Hierarchical Bayesian Dynamic Game for Competitive Inventory and Pricing under Incomplete Information: Learning, Credible Risk, and Equilibrium
本文提出了一种层次结构的贝叶斯动态博弈模型，用于在不完全信息下的竞争库存和定价问题，通过贝叶斯学习和可信风险标准实现稳健决策。
扫码添加微信好友，提出您的宝贵建议 👇
💡 备注请填写：网站反馈
发表机构由 AI 从论文文本抽取，如发现错误，欢迎反馈修正建议。