Bradley Conditionals and Dynamic Choice

One of the main contributions of Richard Bradley’s book is an elegant extension of Jeffrey’s Logic of Decision that countenances the evaluation of conditional prospects. This extension offers a promising new setting in which to model dynamic choice. In Bradley’s framework, plans can be understood as conditionals of an appropriate sort, while dynamic consistency can be viewed as providing a constraint on the evaluation of conditionals across time. In this paper, we study connections between planning conditionals and dynamic consistency.

CMS Full Simulation for Run 3

We report the status of the CMS full simulation for Run 3. During the long shutdown of the LHC a significant update has been introduced to the CMS code for simulation. The CMS geometry description is reviewed. Several important modifications were needed. CMS detector description software is migrated to the DD4Hep community developed tool. We will report on our experience obtained during the process of this migration. Geant4 10.7 is the CMS choice for Run 3 simulation productions. We will discuss arguments for this choice, the strategy of adaptation of a new Geant4 version, and will report on the physics performance of the CMS simulation. A special Geant4 Physics List configuration FTFP_BERT_EMM will be described, which provides a compromise between simulation accuracy and CPU performance. A significant fraction of time for simulation of CMS events is spent on tracking of charged particles in a magnetic field. In the CMS simulation a dynamic choice of Geant4 parameters for tracking in field is implemented. A new method is introduced into simulation of electromagnetic components of hadronic showers in the electromagnetic calorimeter of CMS. For low-energy electrons and positrons a parametrization of GFlash type is applied. Results of tests of this method will be discussed. In summary, we expect about 25% speedup of the CMS simulation production for Run 3 compared to the Run 2 simulations.

A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data

Problem solving has been recognized as a central skill that today’s students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student’s problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students’ problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012.

A Bayesian Level-k Model in n-Person Games

In standard models of iterative thinking, players choose a fixed rule level from a fixed rule hierarchy. Nonequilibrium behavior emerges when players do not perform enough thinking steps. Existing approaches, however, are inherently static. This paper introduces a Bayesian level-k model, in which level-0 players adjust their actions in response to historical game play, whereas higher-level thinkers update their beliefs on opponents’ rule levels and best respond with different rule levels over time. As a consequence, players choose a dynamic rule level (i.e., sophisticated learning) from a varying rule hierarchy (i.e., adaptive learning). We apply our model to existing experimental data on three distinct games: the p-beauty contest, Cournot oligopoly, and private-value auction. We find that both types of learning are significant in p-beauty contest games, but only adaptive learning is significant in the Cournot oligopoly, and only sophisticated learning is significant in the private-value auction. We conclude that it is useful to have a unified framework that incorporates both types of learning to explain dynamic choice behavior across different settings. This paper was accepted by Manel Baucells, decision analysis.

Selling Passes to Strategic Customers

Many service providers offer a prepaid package of credits that can be redeemed for future use, often called passes, in conjunction with regular individual sales. In dynamic pricing situations, customers can strategize on the purchase, redemption, and renewal of the pass by optimizing the timing and choices between passes and individual items based on future prices and their own changing needs. In “Selling Passes to Strategic Customers,” Wang, Levin, and Nediak integrate dynamic choice modeling and optimal control theory to study how to jointly price the passes and individual items in a dynamic setting. They endogenize an individual customer’s purchase/redemption decisions in their model and find that the seemingly complex problem has a simple (approximate) solution. The optimal prices remain nearly constant most of the time, except near the beginning and end of the sales horizon, exhibiting so-called turnpike properties. The pass, as a form of advance purchase, allows the seller to capitalize on the customer’s forward-looking behavior by exploiting the uncertainty of customer valuations.

Dynamic choice

This chapter investigates the issue of dynamic choice. Many important choices made in economic contexts are made through time. The consumer takes some action today, knowing that subsequent choices will be required tomorrow and the day following and so on. And today's choice has impact on either how the consumer views later choices or what choices will later be available or both. This can be referred to as a situation of dynamic choice. The chapter discusses how economists model the process of dynamic choice. In microeconomics, the issue of dynamic choice is usually dealt with by reducing dynamic choice to the static choice of an optimal dynamic strategy which is then carried out. The chapter examines this standard approach before considering a pair of alternatives.

Forecast of Corona Losses using Grey Wolves on Variant Load Condition

Depending upon material science law and guidelines, the factors of overhead lines in network, electric and meteorological data recorded the essential relationships with the corona losses and the states of climate are normally spotted. Corona losses occurred to be characterized just by weighting components for particular states of climate of average twelve Bulgarian territories showing components of complete transmission lattice. A determining unit imply on a measurable procedure worried on an hourly premise corona shortfall is inspected and proposed to have the option to decrease the lopsided characteristics costs. This casing work includes a proposed model of learning for limiting the corona losses. The deficiencies of suggested approach lessen in each situation since it speaks to an effective learning adaptation and at the hour of testing it productively figure heat/temperature giving an extraordinary effect to fundamentally losses decrease. The fundamental purpose for the improvement of learning approach is that the learning approach sums up the information however expectation approach utilizes dynamic choice and disregards past information execution.