scholarly journals MNL-Bandit: A Dynamic Learning Approach to Assortment Selection

2019 ◽  
Vol 67 (5) ◽  
pp. 1453-1485 ◽  
Author(s):  
Shipra Agrawal ◽  
Vashist Avadhanula ◽  
Vineet Goyal ◽  
Assaf Zeevi
Keyword(s):  
Choice Model ◽  
A Priori ◽  
A Priori Knowledge ◽  
Model Parameters ◽  
Dynamic Learning ◽  
Substitutable Products ◽  
General Parameter ◽  
Exploration Period ◽  
Parameter Values ◽  
Priori Knowledge

We consider a dynamic assortment selection problem where in every round the retailer offers a subset (assortment) of N substitutable products to a consumer, who selects one of these products according to a multinomial logit (MNL) choice model. The retailer observes this choice, and the objective is to dynamically learn the model parameters while optimizing cumulative revenues over a selling horizon of length T. We refer to this exploration–exploitation formulation as the MNL-Bandit problem. Existing methods for this problem follow an explore-then-exploit approach, which estimates parameters to a desired accuracy and then, treating these estimates as if they are the correct parameter values, offers the optimal assortment based on these estimates. These approaches require certain a priori knowledge of “separability,” determined by the true parameters of the underlying MNL model, and this in turn is critical in determining the length of the exploration period. (Separability refers to the distinguishability of the true optimal assortment from the other suboptimal alternatives.) In this paper, we give an efficient algorithm that simultaneously explores and exploits, without a priori knowledge of any problem parameters. Furthermore, the algorithm is adaptive in the sense that its performance is near optimal in the “well-separated” case as well as the general parameter setting where this separation need not hold.

scholarly journals Pseudo-Elastic Response of Gas Bearing Clastic Formations: An Italian Case Study

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2488 ◽  
Author(s):  
Christian Coti ◽  
Vera Rocca ◽  
Quinto Sacchi
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Fluid Pressure ◽  
Back Analysis ◽  
Elastic Response ◽  
A Priori Knowledge ◽  
Model Parameters ◽  
Elastic Model ◽  
Priori Knowledge

The research presented in this paper focuses on the analysis of land movements induced by underground gas storage operations in a depleted reservoir in Northern Italy with the aim of increasing the understanding of the deformation response of deep formations via a real case study. The a priori knowledge of the pseudo-elastic parameters showed a substantial discrepancy between static values from triaxial lab tests and dynamic values obtained via the interpretation of sonic data at wellbore scale. The discrepancy is not surprising for the formations under investigation: A thousand meters of a silty to shaly sequence intercalated with arenaceous banks above a reservoir formation, which is basically made up of sandstone intercalated with shale intervals and conglomerates. Information collected for over more than ten years of seasonal production/injection cycles (i.e., time and space evolution of the reservoir fluid pressure and of the induced land surface movements) was then combined in a 3D numerical geomechanical model to constrain and update the a priori knowledge on the pseudo elastic model parameters via a back analysis approach. The obtained calibrated model will then be used for reliable prediction of system safety analyses, for example in terms of induced ground movements.

Seeing, Knowing, and Doing

2020 ◽  
Author(s):  
Robert Audi
Keyword(s):  
Practical Reasoning ◽  
Practical Knowledge ◽  
A Priori ◽  
Human Action ◽  
A Priori Knowledge ◽  
Rational Action ◽  
Reasons For Action ◽  
Discriminative Response ◽  
Rich Information ◽  
Priori Knowledge

This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.

How Reality is Reasonable

2018 ◽  
Author(s):  
Donald C. Williams
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Dimensional Manifold ◽  
The World ◽  
Being There ◽  
Ways Of Being ◽  
Fundamental Entity ◽  
Spatiotemporal Relations ◽  
Priori Knowledge

This chapter begins with a systematic presentation of the doctrine of actualism. According to actualism, all that exists is actual, determinate, and of one way of being. There are no possible objects, nor is there any indeterminacy in the world. In addition, there are no ways of being. It is proposed that actual entities stand in three fundamental relations: mereological, spatiotemporal, and resemblance relations. These relations govern the fundamental entities. Each fundamental entity stands in parthood relations, spatiotemporal relations, and resemblance relations to other entities. The resulting picture is one that represents the world as a four-dimensional manifold of actual ‘qualitied contents’—upon which all else supervenes. It is then explained how actualism accounts for classes, quantity, number, causation, laws, a priori knowledge, necessity, and induction.

How Do We Know that We’re Not Brains in Vats?

2018 ◽  
Author(s):  
Keith DeRose
Keyword(s):  
Epistemic Justification ◽  
A Priori ◽  
A Priori Knowledge ◽  
Conservative Approach ◽  
Contingent Fact ◽  
Brains In Vats ◽  
Priori Knowledge

In this chapter the contextualist Moorean account of how we know by ordinary standards that we are not brains in vats (BIVs) utilized in Chapter 1 is developed and defended, and the picture of knowledge and justification that emerges is explained. The account (a) is based on a double-safety picture of knowledge; (b) has it that our knowledge that we’re not BIVs is in an important way a priori; and (c) is knowledge that is easily obtained, without any need for fancy philosophical arguments to the effect that we’re not BIVs; and the account is one that (d) utilizes a conservative approach to epistemic justification. Special attention is devoted to defending the claim that we have a priori knowledge of the deeply contingent fact that we’re not BIVs, and to distinguishing this a prioritist account of this knowledge from the kind of “dogmatist” account prominently championed by James Pryor.

Incorporating a priori knowledge into neural networks

1995 ◽  
Vol 31 (22) ◽  
pp. 1930-1931 ◽  
Author(s):  
D. Anguita ◽  
S. Rovetta ◽  
S. Ridella ◽  
R. Zunino
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
A Priori Knowledge ◽  
Priori Knowledge

A priori knowledge based particle filter for estimating 3-D pose position of implanted knee

2010 ◽  
Author(s):  
Yusuke Nakajima ◽  
Syoji Kobashi ◽  
Yohei Tsumori ◽  
Nao Shibanuma ◽  
Fumiaki Imamura ◽  
...  
Keyword(s):  
Particle Filter ◽  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge
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