Discretetimemarkovdecisionprocesses: Total Reward

2007 ◽  
pp. 11-38
Keyword(s):  
Total Reward

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

scholarly journals Total rewards strategy for a multi-generational workforce in a financial institution

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Mark Bussin ◽  
Dirk J. Van Rooy
Keyword(s):  
Generational Differences ◽  
Financial Incentives ◽  
Financial Institution ◽  
Survey Design ◽  
Staff Retention ◽  
Financial Industry ◽  
Total Reward ◽  
Managerial Implications ◽  
The Impact ◽  
Skilled Staff

Orientation: Different generations may value and perceive employee rewards differently. This impacts on reward strategies in the workplace which have been specifically developed to attract, retain and motivate staff. A one-size-fits-all approach to reward strategy may not achieve the objectives intended, leading to direct and indirect financial implications for businesses.Research purpose: This study investigated whether perceptions of reward strategy differed across generations in a large financial institution in South Africa. This context was specifically chosen due to the significant competition to attract and retain staff that exists in the financial sector. To contribute to the practical challenges of reward implementation, the study investigated whether specific reward preferences associated with generation exist, and whether offering rewards based on these preferences would successfully attract and retain staff.Motivation for study: South African businesses are competing for skilled staff and rely heavily on a total reward strategy to compensate all generations of employees. Given the financial incentives to retain and attract the most effective staff, it is essential that reward strategies meet their objectives. All factors impacting the efficacy of reward strategies should be considered, including the impact of generational differences in preference. This is of relevance not only to the financial industry, but to all companies that employ staff across a variety of generations.Research design, approach and method: A quantitative survey design was used. A total of 6316 employees from a financial firm completed a survey investigating their experiences and perceptions of reward strategies. Statistically significant differences across different generations and reward preferences were considered.Main findings: Significant differences in reward preferences were found across generational cohorts. This supports international literature.Practical/managerial implications: The results indicate that there is an opportunity for businesses and managers to link components of the total reward strategy to specific generations in the workforce by offering a wider variety of reward options to employees. Employee perceptions indicate a willingness to have reward strategies tailored to their needs and to have a greater say in their reward strategies. The challenge is in presenting the options in a fair and transparent manner, in providing choice and in tracking long-term retention and motivation based on the reward strategy.Contribution: The study found that generations value rewards differently, which will enable management to develop more strategic approaches to reward. This research extends international evidence to include workplaces in emerging economies, which have the additional challenges of high rates of unemployment, but also scarce skills and competition for skilled staff. The findings of this research go some way to support the need to develop more dynamic, flexible and generation-specific reward strategies to support staff retention and attraction.

scholarly journals Scheduling jobs with stochastically ordered processing times on parallel machines to minimize expected flowtime

1986 ◽  
Vol 23 (03) ◽  
pp. 841-847 ◽  
Author(s):  
R. R. Weber ◽  
P. Varaiya ◽  
J. Walrand
Keyword(s):  
Processing Time ◽  
Parallel Machines ◽  
Preemptive Scheduling ◽  
Total Reward ◽  
Processing Times ◽  
Identical Machines ◽  
Scheduling Strategies

A number of jobs are to be processed using a number of identical machines which operate in parallel. The processing times of the jobs are stochastic, but have known distributions which are stochastically ordered. A reward r(t) is acquired when a job is completed at time t. The function r(t) is assumed to be convex and decreasing in t. It is shown that within the class of non-preemptive scheduling strategies the strategy SEPT maximizes the expected total reward. This strategy is one which whenever a machine becomes available starts processing the remaining job with the shortest expected processing time. In particular, for r(t) = – t, this strategy minimizes the expected flowtime.

Nash equilibria in a class of Markov stopping games with total reward criterion

2021 ◽  
Author(s):  
Rolando Cavazos-Cadena ◽  
Mario Cantú-Sifuentes ◽  
Imelda Cerda-Delgado
Keyword(s):  
Nash Equilibria ◽  
Total Reward ◽  
Stopping Games ◽  
Reward Criterion
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