Search results for "Decision process"
showing 10 items of 52 documents
Social media evaluation metrics
2016
Background. There are many methods how specialists can evaluate return of online marketing activities. Most of the methods out there are designed for versatile use. But each online marketing tool has its own unique specific metrics that should be taken into account when measuring the return of marketing activities. Authors believe that the methods that are designed to evaluate online marketing activities should also be more specific. Hence authors believe that more specific online marketing revenue determination methods should be proposed. Objectives. The aim of this paper is to propose a formula that can be used to evaluate the return of social media activities depending on consumer purcha…
Editorial Judgments
2009
Based on participant observation of editors’ decisions for a sociology journal, the paper investigates the peer review process. It shows a hidden interactivity in peer review, which is overlooked both by authors who impute social causes to unwelcome decisions, and by the preoccupation with ‘reliability’ prevalent in peer review research. This study shows that editorial judgments are: (1) attitudes taken by editorial readers toward various kinds of text, as a result of their membership in an intellectual milieu; (2) impressions gained through the reading process (through a ‘virtual interaction’ with the author); and (3) rationalizing statements about manuscripts made by editors and addressed…
Towards Model-Based Reinforcement Learning for Industry-Near Environments
2019
Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. Although these algorithms are fundamentally different, both suffer from high variance, low sample efficiency, and hyperparameter sensitivity that, in practice, make these algorithms a no-go for critical operations in the industry.
A Stigmergic Guiding System to Facilitate the Group Decision Process
2012
The paper presents a stigmergic approach to engineer a guiding system to facilitate the complex problem of designing the group decision processes. The system aims to provide contextual, actionable recommendations based on the knowledge and past experience of its users as recorded in a collaborative working environment implemented around the concept of stigmergic systems. Through an agent-based socio-simulation experiment we have demonstrated already the feasibility of this approach. The paper illustrates how the simulation results are transferred into a guiding system that facilitates the group decision process design through iterative queries reformulations for the identification, represen…
Managerial Behavior in the Lab: Information Disclosure, Decision Process and Leadership Style
2019
This paper reports the results from a lab experiment in which subjects playing the manager role can implement either an efficient / inegalitarian allocation or an inefficient / egalitarian allocation of payoffs. The experiment simulates a stylized managerial context by allowing the manager to manipulate information and select the decision process and by allowing the stakeholders to retaliate against the manager given different choices in the decision process. We found that the inefficient allocation is often selected and that this choice depends on whether the employees can retaliate against the manager and on whether the manager can hide information about the payoffs. The social preference…
Explainable Reinforcement Learning with the Tsetlin Machine
2021
The Tsetlin Machine is a recent supervised machine learning algorithm that has obtained competitive results in several benchmarks, both in terms of accuracy and resource usage. It has been used for convolution, classification, and regression, producing interpretable rules. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. We combined the value iteration algorithm with the regression Tsetlin Machine, as the value function approximator, to investigate the feasibility of training the Tsetlin Machine through bootstrapping. Moreover, we document robustness and accuracy of learning on several instances of the grid-world problem.
QUALITATIVE RESEARCH IN TRAVEL BEHAVIOR STUDIES
2016
Qualitative methodology is extensively used in a wide range of scientific areas, such as Sociology and Psychology, and it is been used to study individual and household decision making processes. However, in the Transportation Planning and Engineering domain it is still infrequent to find in the travel behavior literature studies using qualitative techniques to explore activity-travel decisions. The aim of this paper is first, to provide an overview of the types of qualitative techniques available and to explore how to correctly implement them. Secondly, to highlight the special characteristics of qualitative methods that make them appropriate to study activity-travel decision processes. Fa…
Optimization of anemia treatment in hemodialysis patients via reinforcement learning
2013
Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. Methods: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDP…
The decision process in forward-masked intensity discrimination: evidence from molecular analyses.
2009
In a two-interval forced-choice intensity discrimination task presenting a fixed increment, the level of the forward masker in interval 1 and interval 2 was sampled independently from the same normal distribution on each trial. Mean and standard deviation of the distribution were varied. Correlational analyses of the trial-by-trial data revealed different decision strategies depending on the relation between mean masker level and standard level. If the two levels were identical, listeners tended to select the interval containing the higher-level masker, behaving like an energy detector at the output of a temporal window of integration. For mean masker level higher than the standard level, m…
The effect of personal involvement on the decision to buy store brands
2002
Store brands are appearing in an ever‐increasing number of categories and their acceptance by consumers is unquestionable. The purpose of this paper is to model the decision process involved in a purchase which the consumer goes through when choosing store brands over national brands. The model provided allows us to explain why the same consumer may choose a store brand in one product category and not in another. We have taken personal product involvement as the principal point of reference.