Dynamic decisions

2012 ◽  
pp. 380-383
Author(s):  
Peter P. Wakker
Keyword(s):  
Dynamic Decisions

Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models

2021 ◽  
Author(s):  
Xi Chen ◽  
Yining Wang ◽  
Yuan Zhou
Keyword(s):  
Choice Model ◽  
Current Knowledge ◽  
Search Algorithm ◽  
Multinomial Logit ◽  
Planning Problem ◽  
Assortment Planning ◽  
Substitutable Products ◽  
Dynamic Decisions ◽  
Mnl Model ◽  
Regret Bound

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and the customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Because all the utility parameters of the MNL model are unknown, the seller needs to simultaneously learn customers’ choice behavior and make dynamic decisions on assortments based on the current knowledge. The goal of the seller is to maximize the expected revenue, or, equivalently, to minimize the expected regret. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing policies require the estimation of mean utility for each product and the final regret usually involves the number of products [Formula: see text]. The optimal regret of the dynamic assortment planning problem under the most basic and popular choice model—the MNL model—is still open. By carefully analyzing a revenue potential function, we develop a trisection-based policy combined with adaptive confidence bound construction, which achieves an item-independent regret bound of [Formula: see text], where [Formula: see text] is the length of selling horizon. We further establish the matching lower bound result to show the optimality of our policy. There are two major advantages of the proposed policy. First, the regret of all our policies has no dependence on [Formula: see text]. Second, our policies are almost assumption-free: there is no assumption on mean utility nor any “separability” condition on the expected revenues for different assortments. We also extend our trisection search algorithm to capacitated MNL models and obtain the optimal regret [Formula: see text] (up to logrithmic factors) without any assumption on the mean utility parameters of items.

scholarly journals A geometric framework for modeling dynamic decisions among arbitrarily many alternatives

2018 ◽  
Author(s):  
Peter D. Kvam
Keyword(s):  
Context Effects ◽  
Geometric Approach ◽  
Stopping Rules ◽  
Decision Space ◽  
Geometric Framework ◽  
Continuous Response ◽  
Similarity Relations ◽  
Preferential Choice ◽  
Dynamic Decisions ◽  
Choice Set

Multiple-choice and continuous-response tasks pose a number of challenges for models of the decision process, from empirical challenges like context effects to computational demands imposed by choice sets with a large number of outcomes. This paper develops a general framework for constructing models of the cognitive processes underlying both inferential and preferential choice among any arbitrarily large number of alternatives. This geometric approach represents the alternatives in a choice set along with a decision maker's beliefs or preferences in a ``decision space,'' simultaneously capturing the support for different alternatives along with the similarity relations between the alternatives in the choice set. Support for the alternatives (represented as vectors) shifts over time according to the dynamics of the belief / preference state (represented as a point) until a stopping rule is met (state crosses a hyperplane) and the corresponding selection is made. I review stopping rules that guarantee optimality in multi-alternative inferential choice, minimizing response time for a desired level of accuracy, as well as methods for constructing the decision space, which can result in context effects when applied to preferential choice.

Marriage and Career: The Dynamic Decisions of Young Men

2008 ◽  
Vol 2 (4) ◽  
pp. 337-378 ◽  
Author(s):  
Eric D. Gould
Keyword(s):  
Young Men ◽  
Dynamic Decisions

Lupin’s Foray into Japan

2012 ◽  
pp. 1-19
Author(s):  
Joshy Jacob ◽  
Sobhesh Kumar Agarwalla ◽  
Prem Chander
Keyword(s):  
Mergers And Acquisitions ◽  
Emerging Market ◽  
Pharmaceutical Firm ◽  
Cross Border ◽  
Trade Offs ◽  
Dynamic Decisions ◽  
Cost Competitiveness ◽  
Strong Motivation

The case described the issues faced by a mid-sized Indian generic pharmaceutical firm, in its attempt to acquire a small unlisted Japanese generics manufacturer. It showcases the strong motivation of a successful emerging market pharmaceutical firm to expand into the developed market, buoyed by its cost competitiveness. The case presents an opportunity to discuss the trade-offs involved with most of the dynamic decisions in a cross-border acquisition, such as estimation of synergies and value, bidding, and financing the acquisition. The case may be used in programmes on valuation, and mergers and acquisitions.

Operational and Strategic Decisions in Engineering Design Games

2018 ◽  
Author(s):  
Paul T. Grogan ◽  
Alparslan Emrah Bayrak
Keyword(s):  
Cognitive Load ◽  
Engineering Design ◽  
Situation Awareness ◽  
Strategic Decisions ◽  
Design Decisions ◽  
Operational Decisions ◽  
Operational Design ◽  
Load Limit ◽  
Dynamic Decisions ◽  
Human Participants

Engineering design games model decision-making activities by incorporating human participants in an entertaining platform. This article distinguishes between design decisions at operational and strategic timescales as important features of engineering design games. Operational decisions consider static and short-term dynamic decisions to establish a player’s situation awareness and initial entertainment. Strategic decisions consider longer-term dynamic decisions subject to large uncertainties to retain player engagement. However, constraints on cognitive load limit the ability to simultaneously address both lower-level operational design decisions and higher-level strategic decisions such as collaboration or sustainability. Partial automation can be introduced to reduce cognitive load for operational decisions and focus additional effort on strategic decisions. To illustrate tradeoffs between operational and strategic decisions, this paper discusses example cases for two existing games: Orbital Federates and EcoRacer. Discussion highlights the role of automation and entertainment in engaging human participants in engineering design games and makes recommendations for design of future engineering design games.

Supervisory Decision-Making

2002 ◽  
Vol 46 (3) ◽  
pp. 318-322
Author(s):  
Dwight P. Miller ◽  
Jack Schryver ◽  
Daniel R. Tufano
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Decision Aids ◽  
Organization Design ◽  
Transportation Systems ◽  
Design Guidelines ◽  
Intelligent Software ◽  
Trust In Automation ◽  
Dynamic Decisions

Supervisory Decision-Making (SDM) refers to human supervision of several semi-autonomous (nonhuman) systems in a collaborative manner to accomplish a goal. This study defined SDM and distinguished it from traditional supervisory control and decision-making. An examination of diverse literature in organization design, biology, robotics, innovation diffusion, and trust in automation, yielded no directly applicable or comprehensive models. Field observations were made of large-scale war-games, where operators interacted with semi/autonomous sensors and defense-management systems. Four cognitive models were subsequently developed describing 1) adaptive partnering with automation, 2) technology adoption, 3) trust in automation, and 4) dealing with advice from decision aids. The latter quantitatively models individual, dynamic decisions to accept or reject recommendations made by automated battlespace advisors. The anticipated benefits of this work include more effective human-robot coordination, communication, the identification of experiments, and ultimately design guidelines for robotics, intelligent software agents, intelligent transportation systems, and space exploration.

scholarly journals Operational Competitiveness Development and Its Risk Evaluation

2011 ◽  
Vol 2 (3) ◽  
pp. 2 ◽  
Author(s):  
Yang Liu ◽  
Josu Takala
Keyword(s):  
Global Financial Crisis ◽  
Empirical Studies ◽  
Analytical Models ◽  
Operational Performance ◽  
Manufacturing Strategy ◽  
Manufacturing Companies ◽  
Global Competitiveness ◽  
Dynamic Decisions ◽  
Technology Level ◽  
The Impact

This paper connects previous research in global competitiveness analysis, taking the impact of global financial crisis into account, to evaluate how manufacturing companies are able to manage crisis by adjusting their manufacturing strategy and transformational leadership together with technology level, and develop their operational competitiveness through Sense & Respond (S&R) for dynamic decisions to optimize resource allocations and adjust strategies. It develops a theoretical approach of integrating the core factors which influence operational performance into conceptual analytical models to evaluate overall competitiveness and the risks arisen from adjustments. The empirical studies are focused to compare manufacturing companies in Finland with benchmarking to China, Slovakia, Iceland, and Spain to conclude the development of operational competitiveness.

Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds

2020 ◽  
Vol 68 (6) ◽  
pp. 1678-1697
Author(s):  
Daniel R. Jiang ◽  
Lina Al-Kanj ◽  
Warren B. Powell
Keyword(s):  
Monte Carlo ◽  
Decision Tree ◽  
Upper Bounds ◽  
Tree Search ◽  
Expansion Process ◽  
Monte Carlo Tree Search ◽  
Tree Expansion ◽  
Ride Sharing ◽  
Dynamic Decisions ◽  
Dual Bounds

In the paper, “Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds,” the authors propose an extension to Monte Carlo tree search that uses the idea of “sampling the future” to produce noisy upper bounds on nodes in the decision tree. These upper bounds can help guide the tree expansion process and produce decision trees that are deeper rather than wider, in effect concentrating computation toward more useful parts of the state space. The algorithm’s effectiveness is illustrated in a ride-sharing setting, where a driver/vehicle needs to make dynamic decisions regarding trip acceptance and relocations.

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